Abstract

A light detection and ranging (LiDAR) sensor is described herein. The LiDAR sensor can comprise a fiber optic ending, a laser assembly, and one or more processors. The fiber optic ending can comprise a fiber optic cable terminated by a reflector. The laser assembly can emit a chirp signal to detect an object in an environment. A portion of the chirp signal can be diverted to the fiber optic ending. The one or more processors construct a profile of the chirp signal based on the diverted portion of the chirp signal. The one or more processors determine a best fit curve based on the profile of the chirp signal and one or more parameters associated with the best fit curve. A frequency offset between an emitted chirp signal and a returned chirp signal can be computed based on the best fit curve and the one or more parameters. Based on the frequency offset, the one or more processors can determine a range of the object.

IPC Classes  ?

• G01S 17/34 - Systems determining position data of a target for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
• G01S 7/4911 - Transmitters
• G01S 7/481 - Constructional features, e.g. arrangements of optical elements

Abstract

Provided herein is a power distribution system comprising a main power bus, sub-buses coupled to the main power bus, and a controller. The sub-buses provide power to electrical components of a vehicle. Each of the sub-buses includes an electrically programmable fuse in series with a relay. The controller is configured to detect a fault in a sub-bus of the sub-buses, determine a fault type associated with the fault, and in response to determining the fault type, generate a command to cause the relay to change a relay state.

IPC Classes  ?

• H02H 7/00 - Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
• B60R 16/03 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for supply of electrical power to vehicle subsystems
• G01K 3/00 - Thermometers giving results other than momentary value of temperature
• G01R 31/00 - Arrangements for testing electric propertiesArrangements for locating electric faultsArrangements for electrical testing characterised by what is being tested not provided for elsewhere
• G01R 31/40 - Testing power supplies
• H02H 1/00 - Details of emergency protective circuit arrangements
• H02H 7/22 - Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for distribution gear, e.g. bus-bar systemsEmergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for switching devices

Abstract

A system trains a model to infer an intent of an entity. The model includes one or more sensors to obtain frames of data, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform steps. A first step includes determining, in each frame of the frames, one or more bounding regions, each of the bounding regions enclosing an entity. A second step includes identifying a common entity, the common entity being present in bounding regions corresponding to a plurality of the frames. A third step includes associating the common entity across the frames. A fourth step includes training a model to infer an intent of the common entity based on data outside of the bounding regions.

IPC Classes  ?

• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G06T 7/11 - Region-based segmentation
• G06T 11/20 - Drawing from basic elements, e.g. lines or circles
• G06F 18/24 - Classification techniques

Abstract

Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.

IPC Classes  ?

• G06F 18/25 - Fusion techniques
• G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 11/20 - Drawing from basic elements, e.g. lines or circles
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

Described herein are systems, methods, and non-transitory computer readable media for triggering a sensor operation of a second sensor (e.g., a camera) based on a predicted time of alignment with a first sensor (e.g., a LiDAR), where operation of the second sensor is simulated to determine the predicted time of alignment. In this manner, the sensor data captured by the two sensors is ensured to be substantially synchronized with respect to the physical environment being sensed. This sensor data synchronization based on predicted alignment of the sensors solves the technical problem of lack of sensor coordination and sensor data synchronization that would otherwise result from the latency associated with communication between sensors and a centralized controller and/or between sensors themselves.

IPC Classes  ?

• G01S 7/497 - Means for monitoring or calibrating
• G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computerOutput arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for implementing automated vehicle safety response measures to ensure continued safe automated vehicle operation for a limited period of time after a vehicle component or vehicle system that supports an automated vehicle driving function fails. When a critical vehicle component/system such as a vehicle computing platform fails, the vehicle is likely no longer capable of performing calculations required to safely operate and navigate the vehicle in an autonomous manner, or at a minimum, is no longer able to ensure the accuracy of such calculations. In such a scenario, the automated vehicle safety response measures disclosed herein can ensure - despite failure of the vehicle component/system -continued safe automated operation of the vehicle for a limited period of time in order to bring the vehicle to a safe stop.

IPC Classes  ?

• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• G07C 5/02 - Registering or indicating driving, working, idle, or waiting time only
• B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
• B60W 50/029 - Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts
• B60W 30/12 - Lane keeping

Abstract

A computing component implemented as part of a vehicle architecture and configured to process point cloud data. The computing component comprising one or more programmable logics that, when executed, cause the computing component to generate one or more transformations to align scans of point cloud data, translate the aligned scans of the point cloud data to a universal coordinate system, and generate addresses and offsets to store the aligned scans of the point cloud data.

IPC Classes  ?

• G06T 7/30 - Determination of transform parameters for the alignment of images, i.e. image registration
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces

Abstract

A sensor enclosure comprises a cover and a structure. The structure can be encased by the cover. The structure comprises a frame, a ring, and one or more anchoring posts. The frame can be configured to mount one or more sensors. The ring, disposed peripherally to the frame, can be operatively coupled to the cover. The ring can include a drainage ring plate that drains rainwater accumulated on the cover away from the sensor enclosure. The one or more anchoring posts, disposed underneath the frame and the ring, can be used to anchor the sensor enclosure to a vehicle.

IPC Classes  ?

• G01D 11/24 - Housings
• G01D 11/26 - WindowsCover glassesSealings therefor
• B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G01W 1/14 - Rainfall or precipitation gauges
• B60R 11/00 - Arrangements for holding or mounting articles, not otherwise provided for

Abstract

Provided herein is a system and method for cooling a sensor enclosure of a vehicle. The system comprises one or more sensors configured to determine a speed of the vehicle, an internal temperature of an enclosure, and an external temperature. The system comprises an enclosure to house the one or more sensors. The system comprises a fan disposed at a base of the enclosure. The system comprises a controller configured to regulate a rotation speed of the fan based on the speed of the vehicle, the internal temperature of the enclosure, the external temperature, or the difference between the internal temperature of the enclosure and the external temperature. The controller operates the fan at the regulated rotation speed.

IPC Classes  ?

• B60W 40/105 - Speed
• H05K 7/20 - Modifications to facilitate cooling, ventilating, or heating
• B60W 30/188 - Controlling power parameters of the driveline, e.g. determining the required power

Abstract

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

IPC Classes  ?

• G01S 7/497 - Means for monitoring or calibrating
• G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
• G01S 17/42 - Simultaneous measurement of distance and other coordinates

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for isolating commercial components from a harsh vehicle operating environment to increase the longevity of such components and to decrease their failure rate. Also described herein are systems, methods, and non-transitory computer-readable media for monitoring the operational health status of vehicle components for failure, and upon detecting failure of a component, initiating a processing task reassignment and fault recovery process. In this manner, processing load handled by the component prior to failure can be offloaded to one or more other vehicle components while a fault recovery process is initiated for the component. When the failed component is operational again, the vehicle may revert back to the task assignment in place prior to the component failure, may continue with the current task assignment, or may transition to another different task reassignment.

IPC Classes  ?

• B60W 50/029 - Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts
• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• B60W 50/038 - Limiting the input power, torque or speed

Abstract

A vehicle generates a city-scale map. The vehicle includes one or more Lidar sensors configured to obtain point clouds at different positions, orientations, and times, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform registering, in pairs, a subset of the point clouds based on respective surface normals of each of the point clouds; determining loop closures based on the registered subset of point clouds; determining a position and an orientation of each of the subset of the point clouds based on constraints associated with the determined loop closures; and generating a map based on the determined position and the orientation of each of the subset of the point clouds.

IPC Classes  ?

• G06T 15/08 - Volume rendering
• G06T 7/70 - Determining position or orientation of objects or cameras
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
• G06F 18/25 - Fusion techniques

Abstract

An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to determine a current velocity of the vehicle and control an operation of the compressor based on the current velocity of the vehicle.

IPC Classes  ?

• B08B 5/02 - Cleaning by the force of jets, e.g. blowing-out cavities
• B08B 13/00 - Accessories or details of general applicability for machines or apparatus for cleaning
• B60S 1/66 - Other vehicle fittings for cleaning for cleaning vehicle exterior
• B60W 40/105 - Speed
• G02B 27/00 - Optical systems or apparatus not provided for by any of the groups ,

Abstract

Provided herein is a power distribution system comprising a feedback circuit including a transistor in series with a relay, the feedback circuit regulating regulate a main power path including a main power supply connected in series with an electric power converter. The power distribution system further comprises OR-ing controllers that regulate the main power path and a backup power path including a low-voltage battery. The power distribution system further comprises terminals through which power from the main power path or the backup power path is transmitted to respective components corresponding to channels. The power distribution system further includes a microcontroller that acquires data in each of the channels and control operations associated with each of the channels based on the acquired data.

IPC Classes  ?

• H02J 1/10 - Parallel operation of dc sources
• G06F 1/3206 - Monitoring of events, devices or parameters that trigger a change in power modality
• G06F 1/30 - Means for acting in the event of power-supply failure or interruption, e.g. power-supply fluctuations
• H02J 1/14 - Balancing the load in a network
• H02J 1/08 - Three-wire systemsSystems having more than three wires

Abstract

Provided herein is a system on a vehicle, the system comprising one or more sensors, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: receiving one or more ride requests for ridesharing from one or more users; receiving respective preferences from each of the one or more users; selecting a ride request of a user from the one or more ride requests based on the respective preferences; notifying the user of the selecting of the ride request; sending at least one of the images or videos of the interior of the vehicle to the user; in response to the sending, determining whether the user confirms the ride request; and in response to determining that the user confirms the ride request, selecting a route to the user and driving, according to the route, to the user.

IPC Classes  ?

• G01C 21/34 - Route searchingRoute guidance
• H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
• G06Q 10/02 - Reservations, e.g. for tickets, services or events

Abstract

Provided herein is a headlamp assembly comprising a housing that encloses: a sensor that acquires data associated with a surrounding environment; a light source that illuminates a field of view comprising a portion of the surrounding environment; and one or more processors that analyze the acquired data and determine a direction, field of view, power, or an intensity of the illumination of the portion based on the analyzed data.

IPC Classes  ?

• B60Q 1/14 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to illuminate the way ahead or to illuminate other areas of way or environments the devices being headlights having dimming means
• B60Q 1/00 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor
• H05B 47/125 - Controlling the light source in response to determined parameters by determining the presence or movement of objects or living beings by using cameras
• G06N 20/00 - Machine learning
• G06N 5/04 - Inference or reasoning models
• B60S 1/56 - Cleaning windscreens, windows, or optical devices specially adapted for cleaning other parts or devices than front windows or windscreens

Abstract

Described herein is a sensor device. The sensor device comprises a housing and a printed circuit board encased by the housing. The printed circuit board comprises an image sensor that captures image data, an image sensor processor that processes the image data, a serializer that converts one or more data channels associated with the image data into a single data channel, and one or more exposed surfaces. The one or more exposed surfaces dissipate heat generated by the image sensor, the image sensor processor, and the serializer from the printed circuit board to the housing.

IPC Classes  ?

• H04N 23/51 - Housings
• H05K 1/02 - Printed circuits Details
• H05K 1/18 - Printed circuits structurally associated with non-printed electric components
• H05K 7/14 - Mounting supporting structure in casing or on frame or rack
• H04N 23/52 - Elements optimising image sensor operation, e.g. for electromagnetic interference [EMI] protection or temperature control by heat transfer or cooling elements

Goods & Services

Computer hardware and downloadable computer software for operating self-driving vehicles; Computer hardware and recorded computer software for operating self-driving vehicles; Computer hardware; computer hardware for communicating audio, video and data between computers via a global computer network, wide-area computer networks, and peer-to-peer computer network; Downloadable computer software for vehicle navigation; Recorded computer software for vehicle navigation; Downloadable computer software for operating an autonomous vehicle; Recorded computer software for operating an autonomous vehicle; Downloadable computer software for vehicle fleet management; Recorded computer software for vehicle fleet management; Downloadable computer software for scheduling and booking vehicles for passenger transport; Recorded computer software for scheduling and booking vehicles for passenger transport; Downloadable computer software for managing autonomous vehicles; Recorded computer software for managing autonomous vehicles; navigation apparatus for vehicles; navigational instruments, namely, GPS navigation devices and satellite-aided navigation systems; Electronic steering apparatus for vehicles, namely, simulators for the steering and controlling of vehicles; radar apparatus; laser device for sensing distance to objects; laser object detectors for use on vehicles; lidar, namely, light detection and ranging apparatus; vehicle infrared, acceleration, proximity, and velocity sensors; Electric sensors for determining position, velocity, direction, and acceleration; downloadable mobile applications for coordinating transportation services; downloadable mobile applications for booking vehicles for passenger transport; cameras for use with vehicles; Downloadable mobile applications for booking taxis Land vehicles; automobiles; autonomous cars; autonomous land vehicles; driverless cars Transportation of passengers and freight by trucks and autonomous vehicles; Transportation services, namely, making reservations and bookings for transportation; transportation of passengers by vehicle; transportation services, namely, providing travel by autonomous vehicles; transportation of passengers by land vehicle; transportation services, namely, pickup and drop-off of passengers at designated or directed locations; providing taxi booking services via mobile applications Providing online non-downloadable software services for transportation services, namely, software for coordinating, booking, and dispatching autonomous vehicles for transportation purposes; research and development into autonomous vehicles; research, design, and development of computer hardware and software for use with vehicle on-board computers for monitoring and controlling motor vehicle operation; installation, updating, and maintenance of computer software for use with vehicle on-board computers for monitoring and controlling motor vehicle operation

Goods & Services

Computer hardware; computer hardware for communicating data between computers via a global computer network, wide-area computer networks, or peer-to-peer computer networks; Downloadable computer software for vehicle navigation; Recorded computer software for vehicle navigation; Downloadable computer software for operating an autonomous vehicle; Recorded computer software for operating an autonomous vehicle; Downloadable computer software for vehicle fleet management; Recorded computer software for vehicle fleet management; Downloadable computer software for coordinating, scheduling, booking, and dispatching vehicles; Recorded computer software for coordinating, scheduling, booking, and dispatching vehicles; Downloadable computer software for managing autonomous vehicles; Recorded computer software for managing autonomous vehicles; downloadable mobile applications for coordinating transportation services; downloadable mobile applications for coordinating, scheduling, booking, and dispatching vehicles; Downloadable computer software and hardware for vehicle fleet launching, coordination, and management; Recorded computer software and hardware for vehicle fleet launching, coordination, and management Transportation services, namely, providing services by vehicles; transportation services, namely, making reservations and bookings for transportation; Vehicle sharing services, namely, providing temporary use of vehicles; transportation services, namely, providing travel by autonomous vehicles; transportation reservation services; providing a website featuring information regarding autonomous vehicle transportation services; transportation services, namely, coordinating the pickup and drop-off at designated or directed locations Providing online non-downloadable software services for transportation services, namely, software for coordinating, booking, and dispatching autonomous vehicles for transportation purposes; research and development into autonomous vehicles; research, design, and development of computer hardware and software for use with vehicle on-board computers; research, design, and development of computer hardware and software for vehicle coordination, navigation, and management; installation, updating, and maintenance of computer software for use with vehicle on-board computers; research, design, and development in the field of artificial intelligence

Abstract

Provided herein is a system and method for a sensor system on a vehicle. The sensor system comprises sensors connected with one another in a daisy chain communication network. The sensor system further comprises a controller connected to at least one of the sensors. The controller is configured to operate the vehicle based on data from the sensors and to operate the daisy chain communication network.

IPC Classes  ?

• H04L 45/28 - Routing or path finding of packets in data switching networks using route fault recovery
• H04L 12/44 - Star or tree networks
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Described herein are systems, methods, and computer readable media for dynamically determining a language variant to use for vehicle output to a vehicle occupant based on the vehicle's location. A geographic region may include multiple sub-regions, each of which may be associated with a respective one or more language variants. As an example, a geographic region may be a state or province, and each sub-region may have one or more dialects that are spoken by individuals in that sub-region. In some cases, a particular dialect may be predominant in a given sub-region. As a vehicle traverses a travel path, it may determine its current location, which geographic sub-region includes that location, and which language variant (e.g., dialect) is predominant there. That language variant may then be selected for in-vehicle communication with a vehicle occupant. The vehicle location determination may be made at or near where the occupant entered the vehicle.

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G06F 16/9537 - Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

Abstract

Provided herein is a system and method for determining whether a sensor is calibrated and error handling of an uncalibrated sensor. The system comprises a sensor system comprising a sensor and an analysis engine configured to determine whether the sensor is uncalibrated. The system further comprises an error handling system configured to perform an error handling in response to the sensor system determining that the sensor is uncalibrated. The method comprises determining, by a sensor system, whether the sensor is uncalibrated, and performing, by an error handling system, an error handling in response to the sensor system determining that the sensor is uncalibrated.

IPC Classes  ?

• G01S 7/497 - Means for monitoring or calibrating
• G01S 7/52 - Details of systems according to groups , , of systems according to group
• G01S 7/40 - Means for monitoring or calibrating
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining that a portion of an existing map is to be updated; obtaining a point cloud acquired by one or more Lidar sensors corresponding to a location of the portion; converting the portion into an equivalent point cloud; performing a point cloud registration based on the equivalent point cloud and the point cloud; and updating the existing map based on the point cloud registration.

IPC Classes  ?

• G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation
• G06T 3/00 - Geometric image transformations in the plane of the image
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts

Abstract

A computing system is implemented as part of a vehicle architecture. The computing system includes a computing component, a first computing node that includes a power distribution system, a second computing node that includes input/output (I/O) interfaces to connect to devices, actuators, or sensors, and a third computing node. The computing component further comprises one or more processors and instructions or logic that, when executed by the one or more processors, cause the computing component to perform, transmitting commands to the second computing node, the commands associated with initial processing of data received at the second computing node, receiving initially processed data from the second computing node, and performing further processing on the initially processed data.

IPC Classes  ?

• B60W 50/04 - Monitoring the functioning of the control system
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit

Abstract

A computing device performs initial processing of sensor data. The computing device performs obtaining sensor data, writing the sensor data to first addresses of a dynamically allocated buffer associated with the computing device, encoding the sensor data, writing the encoded sensor data to second addresses of the dynamically allocated buffer, in response to completing the writing of the encoded sensor data, indicating that the writing of the encoded sensor data has been completed, receiving, from a computing resource, a polling request to read the encoded sensor data, transmitting, to the computing resource, a status that the writing of the encoded sensor data to the second addresses has been completed, reading, to a memory of the computing resource, the encoded sensor data, receiving, from the computing resource, a second status that the encoded sensor data has been read, and removing, from the dynamically allocated buffer, the encoded sensor data.

IPC Classes  ?

• G06F 12/1009 - Address translation using page tables, e.g. page table structures
• G06F 12/1027 - Address translation using associative or pseudo-associative address translation means, e.g. translation look-aside buffer [TLB]
• G06F 12/02 - Addressing or allocationRelocation
• G06F 13/22 - Handling requests for interconnection or transfer for access to input/output bus using successive scanning, e.g. polling
• G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for self-detection of a fault condition by a vehicle component, generation of a device health code that includes multiple tiers of information relating to the fault condition experienced by the vehicle component, and broadcasting of the device health code to one or more other vehicle components via one or more vehicle communication networks. A recommended vehicle response measure indicated by a reaction code in the device health code can then be taken or alternate vehicle response may be selected and initiated based on an evaluation of current vehicle operational data.

IPC Classes  ?

• B60R 16/023 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for transmission of signals between vehicle parts or subsystems
• G05B 23/02 - Electric testing or monitoring

Abstract

A computing device performs initial processing of sensor data. The computing device includes one or more processors and instructions or logic that, when executed by the one or more processors, cause the computing device to perform obtaining sensor data, encoding the sensor data, writing the encoded sensor data to a dynamically allocated buffer, and logging a status of the written encoded sensor data at a static location of the dynamically allocated buffer. The status includes any one or more of memory addresses at which frames of the sensor data begin in the dynamically allocated buffer, valid bit fields corresponding to the frames, and sizes of each of data segments within the frames. The instructions further cause the computing device to perform, in response to receiving a polling request from a computing resource, transmitting the logged status to the computing resource.

IPC Classes  ?

• G06T 9/00 - Image coding
• H04N 19/30 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
• H04N 19/423 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements
• H04N 19/436 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements
• H04N 19/625 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using discrete cosine transform [DCT]

Abstract

A computing device triggers a sensor operation. The computing device includes one or more processors and instructions or logic that, when executed by the one or more processors, implements computing functions. The computing device performs receiving timestamps from a sensor, simulating an operation of the sensor, the simulation including predicting orientations of the sensor at different times based on the received timestamps, comparing a latest timestamp of the computing device to a latest timestamp of the sensor, and based on the comparison, triggering a second sensor to perform an operation.

IPC Classes  ?

• G01S 7/497 - Means for monitoring or calibrating
• G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• G01S 7/4865 - Time delay measurement, e.g. time-of-flight measurement, time of arrival measurement or determining the exact position of a peak
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for self-detection of a fault condition by a vehicle component, generation of a device health code that includes multiple tiers of information relating to the fault condition experienced by the vehicle component, and broadcasting of the device health code to one or more other vehicle components via one or more vehicle communication networks. A recommended vehicle response measure indicated by a reaction code in the device health code can then be taken or alternate vehicle response may be selected and initiated based on an evaluation of current vehicle operational data.

IPC Classes  ?

• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• H04L 41/0677 - Localisation of faults

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for self-detection of fault conditions experienced by vehicle components, generation of device health codes indicative of the fault conditions, and broadcasting of the device health codes over mixed vehicle communication networks. The device health codes can be parsed to identify fault information, and the fault information can be assessed along with current vehicle operational data to determine a recommended vehicle response measure to one or more fault conditions experienced by one or more vehicle components.

IPC Classes  ?

• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• H04L 41/0677 - Localisation of faults

Abstract

Provided herein is a system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: identifying, in a map, one or more entities that change over time; predicting an amount of change of the identified one or more entities over time; and updating the map based on the predicted amount of change of the identified one or more entities over time.

IPC Classes  ?

• G01C 21/32 - Structuring or formatting of map data
• G09B 29/00 - MapsPlansChartsDiagrams, e.g. route diagrams
• G06F 16/44 - BrowsingVisualisation therefor
• G06F 16/29 - Geographical information databases
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data; creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generic adversarial network (GAN) model to the created time-dependent 3D traffic environment data; and training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

IPC Classes  ?

• G05D 1/02 - Control of position or course in two dimensions
• B60W 50/06 - Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
• G06N 20/00 - Machine learning
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G06N 3/088 - Non-supervised learning, e.g. competitive learning
• G06N 3/045 - Combinations of networks
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit

Abstract

Provided herein is a system and method of a vehicle that detects a signal and reacts to the signal. The system comprises one or more sensors; one or more processors; a memory storing instructions that, when executed by the one or more processors, causes the system to perform detecting a signal from a source; determining an intended action of the vehicle based on the detected signal; sending, to the source, a response signal indicative of the intended action; determining whether the source has sent a response to the response signal; and in response to determining that the source has sent a response to the response signal, taking the intended action based on the response to the response signal.

IPC Classes  ?

• G05D 1/02 - Control of position or course in two dimensions
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• B60Q 1/34 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating change of drive direction

Abstract

Provided herein is a system and method for providing a vehicle navigation in response to broken or uncalibrated vehicle sensors. The system comprises a sensor to capture data, one or more processors, and a memory storing instructions that cause the system to determine whether the sensor is broken or uncalibrated based on the data, and to limit a range of motion of the vehicle when the sensor is determined to be broken or uncalibrated. The limiting a range of motion of the vehicle includes limiting a number of driving options for the vehicle when the sensor is broken.

IPC Classes  ?

• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G05D 1/02 - Control of position or course in two dimensions

Abstract

A method comprises obtaining smart seat sensor data, the smart seat sensor data being detected by a tactile-sensitive surface material of a seat of an autonomous vehicle in response to a user interacting with the tactile-sensitive surface material. Other sensor data is obtained from one or more other sensors disposed within the autonomous vehicle. The smart seat sensor data and the other sensor data are integrated. A behavior of the user is estimated based on the integrated data, and the autonomous vehicle is controlled based on the estimated behavior of the user.

IPC Classes  ?

• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• B60N 2/00 - Seats specially adapted for vehiclesArrangement or mounting of seats in vehicles
• B60W 30/18 - Propelling the vehicle
• B60W 10/18 - Conjoint control of vehicle sub-units of different type or different function including control of braking systems
• B60W 10/06 - Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
• G06N 20/00 - Machine learning
• B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers

Abstract

Systems, methods, and non-transitory computer-readable media configured to obtain one or more series of successive sensor data frames during a navigation of a vehicle. Disengagement data is obtained. The disengagement data indicates whether a vehicle is in autonomous mode. A training dataset with which train a machine learning model is determined based on the one or more series of successive sensor data frames and the disengagement data. The training dataset includes a subset of the one or more series of successive sensor data frames and a subset of the disengagement data, the machine learning model being trained to identify unsafe driving conditions.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 50/06 - Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
• G01C 21/34 - Route searchingRoute guidance
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric

Abstract

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include obtaining sensor data from a sensor of a vehicle, the sensor data including point cloud frames at different positions, orientations, and times, the sensor data used to generate a map, determining a position and an orientation of the sensor corresponding to a capture of each of the point cloud frames according to a simultaneous localization and mapping (SLAM) algorithm, and depicting, on an interface, a graphical illustration of the determined positions at which the point cloud frames were captured.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

Systems, methods, and non-transitory computer-readable media configured to obtain one or more series of successive sensor data frames during a navigation of a vehicle. Disengagement data is obtained. The disengagement data indicates whether a vehicle is in autonomous mode. A training dataset with which train a machine learning model is determined based on the one or more series of successive sensor data frames and the disengagement data. The training dataset includes a subset of the one or more series of successive sensor data frames and a subset of the disengagement data, the machine learning model being trained to identify unsafe driving conditions.

IPC Classes  ?

• G06N 7/04 - Physical realisation
• B60W 30/095 - Predicting travel path or likelihood of collision
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• G06N 20/00 - Machine learning

Abstract

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include obtaining sensor data from a sensor of a vehicle, the sensor data including point cloud frames at different positions, orientations, and times, the sensor data used to generate a map, determining a position and an orientation of the sensor corresponding to a capture of each of the point cloud frames according to a simultaneous localization and mapping (SLAM) algorithm, and depicting, on an interface, a graphical illustration of the determined positions at which the point cloud frames were captured.

IPC Classes  ?

• H04N 13/00 - Stereoscopic video systemsMulti-view video systemsDetails thereof
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
• G06T 7/00 - Image analysis
• G06T 15/08 - Volume rendering

Abstract

An apparatus includes a processing node of a distributed computing platform. The processing node communicates with other processing nodes over one or more networks. The processing node may receive frames of point clouds at a processing node of a distributed computing platform, determine a subset of the frames as key frames based at least in part on distances travelled between captures of the respective frames, and allocate tasks of processing the key frames to processing subnodes based at least in part on estimated processing demands of the key frames and processing capabilities of each of the processing subnodes.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
• G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining that a portion of an existing map is to be updated; obtaining a point cloud acquired by one or more Lidar sensors corresponding to a location of the portion; converting the portion into an equivalent point cloud; performing a point cloud registration based on the equivalent point cloud and the point cloud; and updating the existing map based on the point cloud registration.

IPC Classes  ?

• G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation

Abstract

Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a sensor, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform, determining data of interest comprising an object, feature, or region of interest, determining whether a sharpness of the data of interest exceeds a threshold, in response to determining that the sharpness does not exceed a threshold, operating the sensor to increase the sharpness of the data of interest until the sharpness exceeds the threshold, in response to the sharpness exceeding the threshold, determining a driving action of a vehicle based on the data of interest, and performing the driving action.

IPC Classes  ?

• G06T 5/73 - DeblurringSharpening
• B60K 31/00 - Vehicle fittings, acting on a single sub-unit only, for automatically controlling vehicle speed, i.e. preventing speed from exceeding an arbitrarily established velocity or maintaining speed at a particular velocity, as selected by the vehicle operator
• B60W 40/112 - Roll movement
• G06T 5/20 - Image enhancement or restoration using local operators
• G06T 7/11 - Region-based segmentation

Abstract

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining that a portion of an existing map is to be updated; obtaining a point cloud acquired by one or more Lidar sensors corresponding to a location of the portion; converting the portion into an equivalent point cloud; performing a point cloud registration based on the equivalent point cloud and the point cloud; and updating the existing map based on the point cloud registration.

IPC Classes  ?

• G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation
• G06T 3/00 - Geometric image transformations in the plane of the image
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts

Goods & Services

Computer hardware; computer hardware for communicating data between computers via a global computer network, wide-area computer networks, or peer-to-peer computer networks; Downloadable computer software for vehicle navigation; Recorded computer software for vehicle navigation; Downloadable computer software for operating an autonomous vehicle; Recorded computer software for operating an autonomous vehicle; Downloadable computer software for vehicle fleet management; Recorded computer software for vehicle fleet management; Downloadable computer software for coordinating, scheduling, booking, and dispatching vehicles; Recorded computer software for coordinating, scheduling, booking, and dispatching vehicles; Downloadable computer software for managing autonomous vehicles; Recorded computer software for managing autonomous vehicles; downloadable mobile applications for coordinating transportation services; downloadable mobile applications for coordinating, scheduling, booking, and dispatching vehicles; Downloadable computer software and hardware for vehicle fleet launching, coordination, and management; Recorded computer software and hardware for vehicle fleet launching, coordination, and management Land vehicles; automobiles; trucks; freight land vehicles; mass transit land vehicles; autonomous cars; autonomous land vehicles; driverless land vehicles; electric land vehicles; freight train containers; plastic parts for vehicles, namely, automotive exterior and interior plastic extruded decorative and protective trim; metal parts for vehicles, namely, automotive exterior and interior metal decorative and protective trim Transportation services, namely, providing services by vehicles; transportation services, namely, making reservations and bookings for transportation; Vehicle sharing services, namely, providing temporary use of vehicles; transportation services, namely, providing travel by autonomous vehicles; transportation reservation services; providing a website featuring information regarding autonomous vehicle transportation services; transportation services, namely, coordinating the pickup and drop-off at designated or directed locations Providing online non-downloadable software services for transportation services, namely, software for coordinating, booking, and dispatching autonomous vehicles for transportation purposes; research and development into autonomous vehicles; research, design, and development of computer hardware and software for use with vehicle on-board computers; research, design, and development of computer hardware and software for vehicle coordination, navigation, and management; installation, updating, and maintenance of computer software for use with vehicle on-board computers; research, design, and development in the field of artificial intelligence

Abstract

An adaptive filter system and a method for controlling the adaptive filter system are described herein. The system can includes one or more filters to attenuate incoming light. The one or more filters can be moved by one or more actuators. The method can capture image data from an imaging device through the one or more filters. Information can be determined from the captured image data. The one or more filters can be moved to a position for capturing image data based on the information.

IPC Classes  ?

• H04N 23/73 - Circuitry for compensating brightness variation in the scene by influencing the exposure time
• G02B 27/00 - Optical systems or apparatus not provided for by any of the groups ,
• G03B 11/00 - Filters or other obturators specially adapted for photographic purposes
• H04N 23/75 - Circuitry for compensating brightness variation in the scene by influencing optical camera components
• G02B 5/20 - Filters
• G02B 7/00 - Mountings, adjusting means, or light-tight connections, for optical elements
• G05D 1/02 - Control of position or course in two dimensions

Abstract

A vehicle generates a city-scale map. The vehicle includes one or more Lidar sensors configured to obtain point clouds at different positions, orientations, and times, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform registering, in pairs, a subset of the point clouds based on respective surface normals of each of the point clouds; determining loop closures based on the registered subset of point clouds; determining a position and an orientation of each of the subset of the point clouds based on constraints associated with the determined loop closures; and generating a map based on the determined position and the orientation of each of the subset of the point clouds.

IPC Classes  ?

• G06T 15/08 - Volume rendering

Abstract

Provided herein is a system and method implemented on a vehicle. The system comprises one or more sensors, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining data from the one or more sensors; comparing the obtained data from the one or more sensors with reference data; determining whether one or more characteristics of the obtained data deviate from corresponding characteristics of the reference data by more than a respective threshold; in response to determining that one or more characteristics of the data obtained deviate from corresponding characteristics of the reference data by more than a respective threshold, determining an action of the vehicle based on amounts of the one or more deviations; and performing the determined action.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G05D 1/02 - Control of position or course in two dimensions
• B60Q 1/50 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking
• B60Q 1/54 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking for indicating speed

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for implementing automated vehicle safety response measures to ensure continued safe automated vehicle operation for a limited period of time after a vehicle component or vehicle system that supports an automated vehicle driving function fails. When a critical vehicle component/system such as a vehicle computing platform fails, the vehicle is likely no longer capable of performing calculations required to safely operate and navigate the vehicle in an autonomous manner, or at a minimum, is no longer able to ensure the accuracy of such calculations. In such a scenario, the automated vehicle safety response measures disclosed herein can ensure—despite failure of the vehicle component/system—continued safe automated operation of the vehicle for a limited period of time in order to bring the vehicle to a safe stop.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 30/18 - Propelling the vehicle
• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures

Abstract

A vehicle generates a city-scale map. The vehicle includes one or more Lidar sensors configured to obtain point clouds at different positions, orientations, and times, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform registering, in pairs, a subset of the point clouds based on respective surface normals of each of the point clouds; determining loop closures based on the registered subset of point clouds; determining a position and an orientation of each of the subset of the point clouds based on constraints associated with the determined loop closures; and generating a map based on the determined position and the orientation of each of the subset of the point clouds.

IPC Classes  ?

• G06T 15/08 - Volume rendering
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining

Abstract

Described herein are apparatuses and methods for selectively controlling the application of a fluid to a sensor enclosure such as a camera housing to protect the housing from overheating. An apparatus that includes a protective shield and a conduit such as tubing for supplying a fluid is described. The protective shield is provided so as to protect an exterior surface of the camera housing from heat caused by sun exposure. The tubing includes an inlet for supplying a fluid such as water or air, can extend through or around an exterior of the camera housing, and includes an outlet with one or more nozzles for ejecting the fluid into a space between the protective shield and the camera housing. Sensor data is received from various vehicle sensors to assess the temperature of the housing, the velocity of the vehicle, and so forth to determine when the fluid should be supplied.

IPC Classes  ?

• G03B 17/55 - Details of cameras or camera bodiesAccessories therefor with provision for heating or cooling, e.g. in aircraft
• G01D 11/24 - Housings
• G01P 3/00 - Measuring linear or angular speedMeasuring differences of linear or angular speeds
• G01K 3/00 - Thermometers giving results other than momentary value of temperature
• G01L 19/14 - Housings
• G01D 11/30 - Supports specially adapted for an instrumentSupports specially adapted for a set of instruments
• G01D 11/26 - WindowsCover glassesSealings therefor
• H04N 23/51 - Housings
• B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for implementing automated vehicle safety response measures to ensure continued safe automated vehicle operation for a limited period of time after a vehicle component or vehicle system that supports an automated vehicle driving function fails. When a critical vehicle component/system such as a vehicle computing platform fails, the vehicle is likely no longer capable of performing calculations required to safely operate and navigate the vehicle in an autonomous manner, or at a minimum, is no longer able to ensure the accuracy of such calculations. In such a scenario, the automated vehicle safety response measures disclosed herein can ensure—despite failure of the vehicle component/system—continued safe automated operation of the vehicle for a limited period of time in order to bring the vehicle to a safe stop.

IPC Classes  ?

• B60W 50/029 - Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts
• B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
• B60W 30/12 - Lane keeping
• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• G07C 5/02 - Registering or indicating driving, working, idle, or waiting time only
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots

Abstract

Systems, methods, and non-transitory computer-readable media are provided for implementing a tracking camera system onboard an autonomous vehicle. Coordinate data of an object can be received. The tracking camera system actuates, based on the coordinate data, to a position such that the object is in view of the tracking camera system. Vehicle operation data of the autonomous vehicle can be received. The position of the tracking camera system can be adjusted, based on the vehicle operation data, such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion. A focus of the tracking camera system can be adjusted to bring the object in focus. The tracking camera system captures image data corresponding to the object.

IPC Classes  ?

• G01S 17/66 - Tracking systems using electromagnetic waves other than radio waves
• G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G02B 7/28 - Systems for automatic generation of focusing signals
• G02B 7/40 - Systems for automatic generation of focusing signals using time delay of the reflected waves, e.g. of ultrasonic waves
• G03B 13/20 - Rangefinders coupled with focusing arrangements, e.g. adjustment of rangefinder automatically focusing camera

Abstract

Described herein are systems, methods, and non-transitory computer readable media for using 3D point cloud data such as that captured by a LiDAR as ground truth data for training an instance segmentation deep learning model. 3D point cloud data captured by a LiDAR can be projected on a 2D image captured by a camera and provided as input to a 2D instance segmentation model. 2D sparse instance segmentation masks may be generated from the 2D image with the projected 3D data points. These 2D sparse masks can be used to propagate loss during training of the model. Generation and use of the 2D image data with the projected 3D data points as well as the 2D sparse instance segmentation masks for training the instance segmentation model obviates the need to generate and use actual instance segmentation data for training, thereby providing an improved technique for training an instance segmentation model.

IPC Classes  ?

• G06V 20/64 - Three-dimensional objects
• G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles

Abstract

Provided herein is a system and method for heat exchange of a vehicle. The system comprises an enclosure disposed on the vehicle. The enclosure comprises a vent at a base of the enclosure. The enclosure houses one or more sensors. The enclosure comprises a fan disposed at a base of the enclosure. The heat exchange system comprises an deflector disposed on the vehicle outside the enclosure and configured to direct an airflow into the vent of the enclosure. The heat exchange system comprises a motor configured to: generate electricity from the airflow and selectively supply electricity to operate the fan. The heat exchange system comprises a controller configured to adjust the deflector and regulate an amount of electricity supplied from the motor to the fan.

IPC Classes  ?

• H05K 7/20 - Modifications to facilitate cooling, ventilating, or heating
• H02K 7/18 - Structural association of electric generators with mechanical driving motors, e.g.with turbines
• H02S 10/12 - Hybrid wind-PV energy systems
• H02S 10/40 - Mobile PV generator systems
• F03D 9/25 - Wind motors characterised by the driven apparatus the apparatus being an electrical generator
• B60H 1/00 - Heating, cooling or ventilating devices
• B60R 16/03 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for supply of electrical power to vehicle subsystems

Abstract

Systems, methods, and non-transitory computer readable media are provided for obtaining a slice of static map data comprising a plurality of blocks, each block comprising a plurality of cells, each, each cell having a cell value indicating a probability that an object is present in the cell; loading the slice into a cache memory of a parallel processor; arranging the static map data in the cache memory in contiguous memory spaces assigned to a group of workers of the parallel processor that have coalescing constraints; loading a frame of dynamic map data into the cache memory; obtaining a plurality of scan match candidates each representing a possible position and attitude of the vehicle; processing, in the parallel processor, the static and dynamic map data and the candidates to generate results each representing a candidate and score; and selecting the candidate having the highest score as a vehicle position.

IPC Classes  ?

• G01C 21/32 - Structuring or formatting of map data
• G05D 1/02 - Control of position or course in two dimensions
• G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries

Abstract

Systems, methods, and non-transitory computer readable media are provided for obtaining a slice of static map data comprising a plurality of blocks, each block comprising a plurality of cells, each, each cell having a cell value indicating a probability that an object is present in the cell; loading the slice into a cache memory of a parallel processor; arranging the static map data in the cache memory in contiguous memory spaces assigned to a group of workers of the parallel processor that have coalescing constraints; loading a frame of dynamic map data into the cache memory; obtaining a plurality of scan match candidates each representing a possible position and attitude of the vehicle; processing, in the parallel processor, the static and dynamic map data and the candidates to generate results each representing a candidate and score; and selecting the candidate having the highest score as a vehicle position.

IPC Classes  ?

• G01C 21/26 - NavigationNavigational instruments not provided for in groups specially adapted for navigation in a road network

Abstract

A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: receive a request to meet a person at a location; drive the vehicle to the location; identify the person at the location; and providing an instruction for the person to interact with the vehicle.

IPC Classes  ?

• G06Q 50/40 - Business processes related to the transportation industry
• B60Q 1/50 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/35 - User authentication involving the use of external additional devices, e.g. dongles or smart cards communicating wirelessly

Abstract

Described herein are systems, methods, and non-transitory computer-readable media for isolating commercial components from a harsh vehicle operating environment to increase the longevity of such components and to decrease their failure rate. Also described herein are systems, methods, and non-transitory computer-readable media for monitoring the operational health status of vehicle components for failure, and upon detecting failure of a component, initiating a processing task reassignment and fault recovery process. In this manner, processing load handled by the component prior to failure can be offloaded to one or more other vehicle components while a fault recovery process is initiated for the component. When the failed component is operational again, the vehicle may revert back to the task assignment in place prior to the component failure, may continue with the current task assignment, or may transition to another different task reassignment.

IPC Classes  ?

• B60W 50/029 - Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts
• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• B60W 50/038 - Limiting the input power, torque or speed

Abstract

Described herein are systems, methods, and computer readable media for performing data conversion on sensor data to obtain modified sensor data that is formatted/structured appropriately for downstream processes that rely on the sensor data as input. The sensor data can include point cloud data captured by a LiDAR, for example. A grid structure and corresponding grid characteristics can be determined and the sensor data can be converted to grid-based sensor data by associating the grid structure and its characteristics with the sensor data. Generating the grid-based sensor data can include reformatting the point cloud data to superimpose the grid structure and its grid characteristics onto the point cloud data. Various downstream processing that cannot feasibly be performed on the raw sensor data can then be performed efficiently on the modified grid-based sensor data by virtue of the grid structure imbuing the sensor data with spatial proximity information.

IPC Classes  ?

• G06T 15/00 - 3D [Three Dimensional] image rendering
• G06T 17/00 - 3D modelling for computer graphics
• G06T 7/70 - Determining position or orientation of objects or cameras
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar

Abstract

A sensor enclosure comprising a domed cover and a base. The base can be encased by the domed cover. The base comprises an inner frame, an outer frame, one or more wipers, and a powertrain. The inner frame can provide surfaces for one or more sensors. The outer frame, disposed underneath the inner frame, the outer frame includes a slewing ring. The slewing ring comprises an inner ring to which the domed cover is attached and an outer ring attached to the outer frame. The one or more wipers extends vertically from the outer frame, each wiper having a first end attached to the outer frame and a second end attached to a support ring, and each wiper making a contact with the dome cover. The powertrain, disposed within the outer frame, configured to rotate the ring and the dome cover attached to the inner ring.

IPC Classes  ?

• B60S 1/04 - Wipers or the like, e.g. scrapers
• B60S 1/08 - Wipers or the like, e.g. scrapers characterised by the drive electrically driven
• B60S 1/56 - Cleaning windscreens, windows, or optical devices specially adapted for cleaning other parts or devices than front windows or windscreens
• B60S 1/44 - Wipers or the like, e.g. scrapers the wiper blades having other than swinging movement, e.g. rotary
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• B60R 11/00 - Arrangements for holding or mounting articles, not otherwise provided for
• G01S 7/481 - Constructional features, e.g. arrangements of optical elements
• G01S 7/52 - Details of systems according to groups , , of systems according to group
• G02B 27/00 - Optical systems or apparatus not provided for by any of the groups ,

Abstract

Described herein are systems, methods, and non-transitory computer readable media for memory address encoding of multi-dimensional data in a manner that optimizes the storage and access of such data in linear data storage. The multi-dimensional data may be spatial-temporal data that includes two or more spatial dimensions and a time dimension. An improved memory architecture is provided that includes an address encoder that takes a multi-dimensional coordinate as input and produces a linear physical memory address. The address encoder encodes the multi-dimensional data such that two multi-dimensional coordinates close to one another in multi-dimensional space are likely to be stored in close proximity to one another in linear data storage. In this manner, the number of main memory accesses, and thus, overall memory access latency is reduced, particularly in connection with real-world applications in which the respective probabilities of moving along any given dimension are very close.

IPC Classes  ?

• G06F 12/08 - Addressing or allocationRelocation in hierarchically structured memory systems, e.g. virtual memory systems
• G06F 12/02 - Addressing or allocationRelocation
• G06F 12/0811 - Multiuser, multiprocessor or multiprocessing cache systems with multilevel cache hierarchies
• G06F 12/0846 - Cache with multiple tag or data arrays being simultaneously accessible
• G06F 12/1045 - Address translation using associative or pseudo-associative address translation means, e.g. translation look-aside buffer [TLB] associated with a data cache
• G06F 12/0831 - Cache consistency protocols using a bus scheme, e.g. with bus monitoring or watching means

Abstract

Described herein is a memory architecture that is configured to dynamically determine an address encoding to use to encode multi-dimensional data such as multi-coordinate data in a manner that provides a coordinate bias corresponding to a current memory access pattern. The address encoding may be dynamically generated in response to receiving a memory access request or may be selected from a set of preconfigured address encodings. The dynamically generated or selected address encoding may apply an interleaving technique to bit representations of coordinate values to obtain an encoded memory address. The interleaving technique may interleave a greater number of bits from the bit representation corresponding to the coordinate direction in which a coordinate bias is desired than from bit representations corresponding to other coordinate directions.

IPC Classes  ?

• G06F 12/0811 - Multiuser, multiprocessor or multiprocessing cache systems with multilevel cache hierarchies
• G06F 12/0846 - Cache with multiple tag or data arrays being simultaneously accessible
• G06F 12/1045 - Address translation using associative or pseudo-associative address translation means, e.g. translation look-aside buffer [TLB] associated with a data cache
• G06F 12/02 - Addressing or allocationRelocation

Abstract

Provided herein is a power distribution system comprising a feedback circuit including a transistor in series with a relay, the feedback circuit regulating regulate a main power path including a main power supply connected in series with an electric power converter. The power distribution system further comprises OR-ing controllers that regulate the main power path and a backup power path including a low-voltage battery. The power distribution system further comprises terminals through which power from the main power path or the backup power path is transmitted to respective components corresponding to channels. The power distribution system further includes a microcontroller that acquires data in each of the channels and control operations associated with each of the channels based on the acquired data.

IPC Classes  ?

• H02J 1/10 - Parallel operation of dc sources
• G06F 1/3206 - Monitoring of events, devices or parameters that trigger a change in power modality
• G06F 1/30 - Means for acting in the event of power-supply failure or interruption, e.g. power-supply fluctuations
• H02J 1/14 - Balancing the load in a network
• H02J 1/08 - Three-wire systemsSystems having more than three wires

Abstract

Provided herein is a power distribution system comprising a main power bus, sub-buses coupled to the main power bus, and a controller. The sub-buses provide power to electrical components of a vehicle. Each of the sub-buses includes an electrically programmable fuse in series with a relay. The controller is configured to detect a fault in a sub-bus of the sub-buses, determine a fault type associated with the fault, and in response to determining the fault type, generate a command to cause the relay to change a relay state.

IPC Classes  ?

• H02H 7/00 - Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
• H02H 7/22 - Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for distribution gear, e.g. bus-bar systemsEmergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for switching devices
• H02H 1/00 - Details of emergency protective circuit arrangements
• G01K 3/00 - Thermometers giving results other than momentary value of temperature
• G01R 31/40 - Testing power supplies
• G01R 31/00 - Arrangements for testing electric propertiesArrangements for locating electric faultsArrangements for electrical testing characterised by what is being tested not provided for elsewhere
• B60R 16/03 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for supply of electrical power to vehicle subsystems

Abstract

Systems and methods of linearizing a signal of a light detection and ranging (LiDAR) sensor are described herein. A system receives a portion of a non-linear chirp signal. The portion of the non-linear chirp signal is sampled at a sampling frequency to generate data points corresponding to the portion of the non-linear chirp signal. A profile of the non-linear chirp signal is generated based on the data points. The non-linear chirp signal is linearized based on the profile of the non-linear chirp signal.

IPC Classes  ?

• G01S 17/34 - Systems determining position data of a target for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
• G01S 7/493 - Extracting wanted echo signals
• G01S 7/4913 - Circuits for detection, sampling, integration or read-out

Abstract

Described herein is a sensor device. The sensor device comprises a housing and a printed circuit board encased by the housing. The printed circuit board comprises an image sensor that captures image data, an image sensor processor that processes the image data, a serializer that converts one or more data channels associated with the image data into a single data channel, and one or more exposed surfaces. The one or more exposed surfaces dissipate heat generated by the image sensor, the image sensor processor, and the serializer from the printed circuit board to the housing.

IPC Classes  ?

• H04N 5/225 - Television cameras
• H05K 1/02 - Printed circuits Details
• H05K 1/18 - Printed circuits structurally associated with non-printed electric components
• H05K 7/14 - Mounting supporting structure in casing or on frame or rack

Abstract

A system trains a model to infer an intent of an entity. The model includes one or more sensors to obtain frames of data, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform steps. A first step includes determining, in each frame of the frames, one or more bounding regions, each of the bounding regions enclosing an entity. A second step includes identifying a common entity, the common entity being present in bounding regions corresponding to a plurality of the frames. A third step includes associating the common entity across the frames. A fourth step includes training a model to infer an intent of the common entity based on data outside of the bounding regions.

IPC Classes  ?

• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G06T 7/11 - Region-based segmentation
• G06T 11/20 - Drawing from basic elements, e.g. lines or circles
• G06F 18/24 - Classification techniques

Abstract

Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. A static object present in the environment is detected such as signage. A type of the detected object is determined from static map data. Point cloud data representative of the static object is captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by a second known transformation matrix.

IPC Classes  ?

• G01S 7/497 - Means for monitoring or calibrating
• G01S 7/481 - Constructional features, e.g. arrangements of optical elements
• G01S 17/10 - Systems determining position data of a target for measuring distance only using transmission of interrupted, pulse-modulated waves
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G01S 19/13 - Receivers

Abstract

Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. Static objects present in the environment are detected such as signage. Point cloud data representative of the static objects are captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by application of a second known transformation matrix to transition from the local coordinate system of the second pre-calibrated sensor to the global coordinate system.

IPC Classes  ?

• G01S 7/497 - Means for monitoring or calibrating
• G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles

Abstract

A light detection and ranging (LiDAR) sensor is described herein. The LiDAR sensor can comprise a fiber optic ending, a laser assembly, and one or more processors. The fiber optic ending can comprise a fiber optic cable terminated by a reflector. The laser assembly can emit a chirp signal to detect an object in an environment. A portion of the chirp signal can be diverted to the fiber optic ending. The one or more processors construct a profile of the chirp signal based on the diverted portion of the chirp signal. The one or more processors determine a best fit curve based on the profile of the chirp signal and one or more parameters associated with the best fit curve. A frequency offset between an emitted chirp signal and a returned chirp signal can be computed based on the best fit curve and the one or more parameters. Based on the frequency offset, the one or more processors can determine a range of the object.

IPC Classes  ?

• G01C 3/08 - Use of electric radiation detectors
• G01S 17/34 - Systems determining position data of a target for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
• G01S 7/4911 - Transmitters
• G01S 7/481 - Constructional features, e.g. arrangements of optical elements

Abstract

Described herein is a sensor assembly and a method of operating the sensor assembly. In various embodiments, the sensor assembly can comprise a base component, a light detection and ranging (LiDAR) sensor, a transparent cylinder, a motor component, and a controller. The LiDAR sensor can be mounted on a support platform disposed centrally on the base component. The transparent cylinder can be disposed peripherally to the LiDAR sensor and can provide a field of view (FOV) for the LiDAR sensor. The transparent cylinder can be rotated independently of the base component. The motor component can be disposed on the base component, adjacent to the support platform. The motor component can be coupled to the transparent cylinder through a gearset and configured to rotate the transparent cylinder. The controller can be configured to obtain sensor data from on-board vehicle sensors. The controller can determine a level of obscurement on the transparent cylinder based on the sensor data. The controller can determine that the level of obscurement exceeds a threshold level of obscurement. The controller can transmit an actuation signal to the motor component to cause a rotation of the transparent cylinder at a rotational speed. The rotation of the transparent cylinder can disperse obscurements away from the transparent cylinder.

IPC Classes  ?

• G01S 7/48 - Details of systems according to groups , , of systems according to group
• G01S 7/497 - Means for monitoring or calibrating
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles

Abstract

Provided herein is a headlamp assembly comprising a housing that encloses: a sensor that acquires data associated with a surrounding environment; a light source that illuminates a field of view comprising a portion of the surrounding environment; and one or more processors that analyze the acquired data and determine a direction, field of view, power, or an intensity of the illumination of the portion based on the analyzed data.

IPC Classes  ?

• B60Q 1/14 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to illuminate the way ahead or to illuminate other areas of way or environments the devices being headlights having dimming means
• B60Q 1/00 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor
• H05B 47/125 - Controlling the light source in response to determined parameters by determining the presence or movement of objects or living beings by using cameras
• G06N 20/00 - Machine learning
• G06N 5/04 - Inference or reasoning models
• B60S 1/56 - Cleaning windscreens, windows, or optical devices specially adapted for cleaning other parts or devices than front windows or windscreens

Abstract

Described herein is a sensor device. The sensor device includes a housing with a front plate and a back plate. The front plate includes mounting holes and the back plate includes second mounting holes and alignment holes. The sensor device includes a printed circuit board encased by the housing, wherein the printed circuit board comprises an image sensor, an image sensor processor, and a serializer.

IPC Classes  ?

• H04N 5/225 - Television cameras
• G03B 17/14 - Bodies with means for supporting objectives, supplementary lenses, filters, masks, or turrets interchangeably

Abstract

A vehicle generates a city-scale map. The vehicle includes one or more Lidar sensors configured to obtain point clouds at different positions, orientations, and times, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform registering, in pairs, a subset of the point clouds based on respective surface normals of each of the point clouds; determining loop closures based on the registered subset of point clouds; determining a position and an orientation of each of the subset of the point clouds based on constraints associated with the determined loop closures; and generating a map based on the determined position and the orientation of each of the subset of the point clouds.

IPC Classes  ?

• G06T 15/08 - Volume rendering
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining

Abstract

Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a processor configured to acquire data of nonuniform resolution over a field of view of the sensor, and a controller configured to determine a driving action of a vehicle based on the data, and perform the driving action.

IPC Classes  ?

• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G06V 10/147 - Details of sensors, e.g. sensor lenses
• G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders

Abstract

Described herein are systems, methods, and non-transitory computer readable media for using 3D point cloud data such as that captured by a LiDAR as ground truth data for training an instance segmentation deep learning model. 3D point cloud data captured by a LiDAR can be projected on a 2D image captured by a camera and provided as input to a 2D instance segmentation model. 2D sparse instance segmentation masks may be generated from the 2D image with the projected 3D data points. These 2D sparse masks can be used to propagate loss during training of the model. Generation and use of the 2D image data with the projected 3D data points as well as the 2D sparse instance segmentation masks for training the instance segmentation model obviates the need to generate and use actual instance segmentation data for training, thereby providing an improved technique for training an instance segmentation model.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• G06K 9/46 - Extraction of features or characteristics of the image

Abstract

Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.

IPC Classes  ?

• G06F 18/25 - Fusion techniques
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G06T 11/20 - Drawing from basic elements, e.g. lines or circles

Abstract

A system comprises a sensor system comprising a sensor and an analysis engine configured to determine whether the sensor is uncalibrated. The system further comprises an error handling system configured to determine whether to perform a recalibration in response to the sensor system determining that the sensor is uncalibrated. The error handling system further comprises a recalibration engine configured to perform a recalibration.

IPC Classes  ?

• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• G01D 18/00 - Testing or calibrating apparatus or arrangements provided for in groups
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots

Abstract

An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to predict a trajectory of the vehicle and control an operation of the compressor based on the predicted trajectory of the vehicle.

IPC Classes  ?

• B60S 1/56 - Cleaning windscreens, windows, or optical devices specially adapted for cleaning other parts or devices than front windows or windscreens
• B08B 3/02 - Cleaning by the force of jets or sprays
• B08B 5/02 - Cleaning by the force of jets, e.g. blowing-out cavities
• B60S 1/48 - Liquid supply therefor
• B60S 1/54 - Cleaning windscreens, windows, or optical devices using gas, e.g. hot air
• G02B 27/00 - Optical systems or apparatus not provided for by any of the groups ,

Abstract

An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to determine a current velocity of the vehicle and control an operation of the compressor based on the current velocity of the vehicle.

IPC Classes  ?

• B60S 1/66 - Other vehicle fittings for cleaning for cleaning vehicle exterior
• B08B 5/02 - Cleaning by the force of jets, e.g. blowing-out cavities
• B08B 13/00 - Accessories or details of general applicability for machines or apparatus for cleaning
• G02B 27/00 - Optical systems or apparatus not provided for by any of the groups ,
• B60W 40/105 - Speed

Abstract

Described herein are systems, methods, and computer readable media for capturing sensor data relating to an enclosed compartment of a vehicle (e.g., a cargo area of the vehicle) via one or more vehicle sensors; analyzing the sensor data to determine whether it is indicative of a living being present in the enclosed compartment; performing an object detection analysis on at least a portion of the sensor data to determine a type of living being detected; and initiating one or more automated vehicle response measures based on the type of living being.

IPC Classes  ?

• B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
• G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• G06V 20/64 - Three-dimensional objects

Abstract

Described herein are systems, methods, and computer readable media for performing data conversion on sensor data to obtain modified sensor data that is formatted/structured appropriately for downstream processes that rely on the sensor data as input. The sensor data can include point cloud data captured by a LiDAR, for example. A grid structure and corresponding grid characteristics can be determined and the sensor data can be converted to grid-based sensor data by associating the grid structure and its characteristics with the sensor data. Generating the grid-based sensor data can include reformatting the point cloud data to superimpose the grid structure and its grid characteristics onto the point cloud data. Various downstream processing that cannot feasibly be performed on the raw sensor data can then be performed efficiently on the modified grid-based sensor data by virtue of the grid structure imbuing the sensor data with spatial proximity information.

IPC Classes  ?

• G06T 15/00 - 3D [Three Dimensional] image rendering
• G06T 17/00 - 3D modelling for computer graphics
• G06T 7/70 - Determining position or orientation of objects or cameras
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar

Abstract

Described herein are systems, methods, and computer readable media for performing data conversion on sensor data to obtain modified sensor data that is formatted/structured appropriately for downstream processes that rely on the sensor data as input. The sensor data can include point cloud data captured by a LiDAR, for example. A grid structure and corresponding grid characteristics can be determined and the sensor data can be converted to grid-based sensor data by associating the grid structure and its characteristics with the sensor data. Generating the grid-based sensor data can include reformatting the point cloud data to superimpose the grid structure and its grid characteristics onto the point cloud data. Various downstream processing that cannot feasibly be performed on the raw sensor data can then be performed efficiently on the modified grid-based sensor data by virtue of the grid structure imbuing the sensor data with spatial proximity information.

IPC Classes  ?

• B60W 50/08 - Interaction between the driver and the control system
• G01C 3/08 - Use of electric radiation detectors
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06K 9/48 - Extraction of features or characteristics of the image by coding the contour of the pattern

Abstract

An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to determine information of an acoustic emission from the compressor and to counteract the acoustic emission based on the determined information.

IPC Classes  ?

• G10K 11/178 - Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effectsMasking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
• G10K 15/02 - Synthesis of acoustic waves
• G06F 17/14 - Fourier, Walsh or analogous domain transformations
• H04S 7/00 - Indicating arrangementsControl arrangements, e.g. balance control

Abstract

Provided herein is a system and method for fleet coordination in a vehicle. The system comprises one or more sensors, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform, capturing current data associated with the vehicle, planning a route of the vehicle based on the captured current data, navigating the vehicle in accordance with the planned route, detecting an instant position of the vehicle while navigating the vehicle, and coordinating a movement of another vehicle with the vehicle based on the detected instant position of the vehicle.

IPC Classes  ?

• G05D 1/02 - Control of position or course in two dimensions
• G08G 1/00 - Traffic control systems for road vehicles
• H04W 4/46 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
• B60W 30/16 - Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• B60W 40/06 - Road conditions
• G07C 5/00 - Registering or indicating the working of vehicles
• G08G 1/01 - Detecting movement of traffic to be counted or controlled

Abstract

Provided herein is a system that comprises one or more sensors that capture data, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform functions that include identifying one or more locations within a distance of the vehicle, capturing current data of the identified one or more locations, determining one or more changes that exceed respective threshold amounts between the current data and historical data of the identified one or more locations, and updating the historical data of the identified one or more locations based on the determined one or more changes.

IPC Classes  ?

• G01C 21/32 - Structuring or formatting of map data
• G01C 21/34 - Route searchingRoute guidance
• G01C 11/06 - Interpretation of pictures by comparison of two or more pictures of the same area
• G01C 21/36 - Input/output arrangements for on-board computers
• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G01C 21/00 - NavigationNavigational instruments not provided for in groups

Abstract

Described herein are systems, methods, and computer readable media for capturing image data of one or more regions of a vehicle (e.g., a cargo area of an autonomous vehicle) at various particular times and assessing the image data to determine whether a past vehicle occupant has left behind one or more belongings of value in the vehicle. If it is determined that a former vehicle occupant has left behind an article of value, an audible message may be outputted from a speaker of the vehicle to inform the former occupant of the presence of the article in the vehicle or a notification may be sent to a mobile device of the former occupant. The audible message may be outputted, for example, while the former occupant is beyond a predetermined distance from the vehicle, but still within range of hearing the message.

IPC Classes  ?

• G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• G08B 21/24 - Reminder alarms, e.g. anti-loss alarms
• G08B 5/36 - Visible signalling systems, e.g. personal calling systems, remote indication of seats occupied using electric transmissionVisible signalling systems, e.g. personal calling systems, remote indication of seats occupied using electromagnetic transmission using visible light sources
• B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
• B60Q 5/00 - Arrangement or adaptation of acoustic signal devices
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06Q 20/10 - Payment architectures specially adapted for electronic funds transfer [EFT] systemsPayment architectures specially adapted for home banking systems
• B60Q 3/20 - Arrangement of lighting devices for vehicle interiorsLighting devices specially adapted for vehicle interiors for lighting specific fittings of passenger or driving compartmentsArrangement of lighting devices for vehicle interiorsLighting devices specially adapted for vehicle interiors mounted on specific fittings of passenger or driving compartments

Abstract

Systems and methods are provided for cooling air in a vehicle. The system includes a chassis, inside an interior area of a vehicle, with one or more openings that are configured to allow air to enter the chassis to cool a heat generating component in the vehicle and an exhaust duct that directs the air away from the chassis after the air has contacted at least a portion of the heat generating component. The system includes a fan that acts to propel the air through the one or more openings and through the exhaust duct.

IPC Classes  ?

• B60H 1/00 - Heating, cooling or ventilating devices
• B62D 21/17 - Understructures, i.e. chassis frame on which a vehicle body may be mounted forming fluid or electrical conduit means or having other means to accommodate the transmission of a force or signal
• H05K 7/20 - Modifications to facilitate cooling, ventilating, or heating

Abstract

Provided herein is a system of a vehicle that comprises one or more sensors, one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform: selecting a trajectory along a route of the vehicle; predicting a trajectory of another object along the route; adjusting the selected trajectory based on a predicted change, in response to adjusting the selected trajectory, to the predicted trajectory of the another object, the predicted change to the predicted trajectory of the another object being stored in a model; determining an actual change, in response to adjusting the selected trajectory, to a trajectory of the another object, in response to an interaction between the vehicle and the another object; updating the model based on the determined actual change to the trajectory of the another object; and selecting a future trajectory based on the updated model.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 40/06 - Road conditions
• G05D 1/02 - Control of position or course in two dimensions

Abstract

Systems and methods are provided for cooling air in a vehicle. The system includes a chassis, inside an interior area of a vehicle, with one or more openings that are configured to allow air to enter the chassis to cool a heat generating component in the vehicle and an exhaust duct that directs the air away from the chassis after the air has contacted at least a portion of the heat generating component. The system includes a fan that acts to propel the air through the one or more openings and through the exhaust duct.

IPC Classes  ?

• B60H 1/00 - Heating, cooling or ventilating devices
• B60H 1/24 - Devices purely for ventilating or where the heating or cooling is irrelevant
• B60H 1/32 - Cooling devices

Abstract

Systems and methods are provided for cooling vehicle components. The system includes one or more heat generating components in a vehicle and a coolant flow path connected to the two or more heat generating components. The system includes a coolant pump configured to circulate coolant through the coolant flow pat and a reversing mechanism configured to reverse a direction of circulation of coolant.

IPC Classes  ?

• B60H 1/32 - Cooling devices
• B60H 1/00 - Heating, cooling or ventilating devices
• B60R 16/00 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
• B60R 16/037 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for occupant comfort
• B60R 16/08 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for fluid

Abstract

Provided herein is a system and method of a vehicle that communicates an intended action of the vehicle. The system comprises one or more sensors; one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform capturing data from the one or more sensors of another vehicle or a road condition; determining an intended action of the vehicle based on the captured data; simulating the intended action of the vehicle on a map; communicating, within the vehicle, the intended action of the vehicle; and navigating the vehicle based on the intended action of the vehicle.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 30/18 - Propelling the vehicle
• B60W 40/06 - Road conditions
• G01C 21/36 - Input/output arrangements for on-board computers
• B60Q 5/00 - Arrangement or adaptation of acoustic signal devices
• H04W 4/46 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

Provided herein is a system and method of a vehicle. The system comprises one or more sensors, processors, maps, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: monitoring a location of the vehicle while driving; detecting a sign while the vehicle is driving; capturing, frame-by-frame, data of the sign until the sign disappears from a field of view of the sensor; synchronizing each frame of the data with the location of the vehicle; determining a location of the sign based on the frame-by-frame data; in response to determining, at a frame immediately before the sign disappears from the field of view of the sensor, that the vehicle is driving towards the sign, uploading the detected sign and the location of the sign onto the one or more maps; and implementing a driving action based on the sign.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• B60W 30/14 - Cruise control
• G01C 21/32 - Structuring or formatting of map data
• G08G 1/0967 - Systems involving transmission of highway information, e.g. weather, speed limits
• H04W 4/46 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

Described herein are aerodynamically enhanced sensor housings. An aerodynamically enhanced sensor housing has an asymmetrical lateral cross-section that includes a first portion having a substantially spherical curvature and a second portion having a non-spherical curvature. The second portion having the non-spherical curvature may be elongated in relation to the first portion. An aerodynamically enhanced housing can also include one or more indentations formed in an exterior surface thereof to further enhance drag reducing characteristics of the housing. In addition, air flow characteristics around the sensor housing during vehicle operation can be assessed and a drag reduction protocol can be generated and implemented to further enhanced the drag reducing characteristics of the sensor housing.

IPC Classes  ?

• B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
• B62D 35/00 - Vehicle bodies characterised by streamlining
• B60R 11/00 - Arrangements for holding or mounting articles, not otherwise provided for
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G06F 30/15 - Vehicle, aircraft or watercraft design

Abstract

Described herein are systems, methods, and computer readable media for dynamically determining a language variant to use for vehicle output to a vehicle occupant based on the vehicle's location. A geographic region may include multiple sub-regions, each of which may be associated with a respective one or more language variants. As an example, a geographic region may be a state or province, and each sub-region may have one or more dialects that are spoken by individuals in that sub-region. In some cases, a particular dialect may be predominant in a given sub-region. As a vehicle traverses a travel path, it may determine its current location, which geographic sub-region includes that location, and which language variant (e.g., dialect) is predominant there. That language variant may then be selected for in-vehicle communication with a vehicle occupant. The vehicle location determination may be made at or near where the occupant entered the vehicle.

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G06F 16/9537 - Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

Abstract

A system of a first vehicle includes sensors and a processor that performs generating of an initial trajectory along a travel route of the first vehicle, acquiring trajectories of second vehicles along the route, adjusting the initial trajectory based on the acquired trajectories of the second vehicles, and navigating the first vehicle based on the adjusted initial trajectory.

IPC Classes  ?

• G05D 1/02 - Control of position or course in two dimensions
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Described herein are sensor assembly cleaning systems and apparatuses that are adapted to rotate a transparent surface of a sensor assembly independently of a housing of the sensor assembly in order to disperse water, moisture, debris, or the like from the surface. The transparent surface may be a glass window that provides a camera of the sensor assembly with a field-of-view of an external environment. Sensor data captured from various on-board vehicle sensors such as moisture data, image data, vehicle velocity data, or the like can be evaluated against various criteria to determine when and for how long to rotate the transparent surface. Sensor data can be evaluated over a period of time to identify patterns or trends relating to one or more vehicle parameters. An activation schedule for initiating and ceasing rotation of the transparent surface can be determined based on such patterns/trends.

IPC Classes  ?

• B60R 1/00 - Optical viewing arrangementsReal-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
• G02B 27/00 - Optical systems or apparatus not provided for by any of the groups ,
• H04N 17/00 - Diagnosis, testing or measuring for television systems or their details
• B60S 1/56 - Cleaning windscreens, windows, or optical devices specially adapted for cleaning other parts or devices than front windows or windscreens
• G08B 13/196 - Actuation by interference with heat, light, or radiation of shorter wavelengthActuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
• H04N 5/225 - Television cameras

Abstract

Provided herein is a system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining a previous pose of a vehicle; acquiring one or more previous readings corresponding to one or more wheel encoders during the previous pose; acquiring one or more readings corresponding to one or more wheel encoders acquired after the previous pose; and adjusting the previous pose based on the one or more readings to obtain a current pose.

IPC Classes  ?

• G01C 21/28 - NavigationNavigational instruments not provided for in groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• G01C 21/16 - NavigationNavigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigatedDead reckoning by integrating acceleration or speed, i.e. inertial navigation
• G01C 22/00 - Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers or using pedometers

Abstract

Described herein are systems, methods, and non-transitory computer readable media for triggering a sensor operation of a second sensor (e.g., a camera) based on a predicted time of alignment with a first sensor (e.g., a LiDAR), where operation of the second sensor is simulated to determine the predicted time of alignment. In this manner, the sensor data captured by the two sensors is ensured to be substantially synchronized with respect to the physical environment being sensed. This sensor data synchronization based on predicted alignment of the sensors solves the technical problem of lack of sensor coordination and sensor data synchronization that would otherwise result from the latency associated with communication between sensors and a centralized controller and/or between sensors themselves.

IPC Classes  ?

• G01S 7/497 - Means for monitoring or calibrating
• G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computerOutput arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors