A system for navigating a host vehicle receives a plurality of captured images acquired by at least one camera onboard the host vehicle. The system determines an actual velocity vector for the host vehicle based on the plurality of captured images and compares the actual velocity vector to an expected velocity vector. The system also determines a lateral slip angle based on an observed angular difference between the actual velocity vector and the expected velocity vector. The system causes the host vehicle navigation system to take at least one action based on the determined lateral slip angle.
For example, a polarization rotator may be configured to rotate a polarization of one or more Radio Frequency (RF) signals communicated by one or more antennas via a wireless medium. For example, the polarization rotator may include an input to receive a control signal; and a plurality of polarization-rotator cells switchable between a plurality of predefined states, for example, based on the control signal. For example, the plurality of predefined states may include a no-rotation state and a rotation state. For example, the plurality of polarization-rotator cells may be configured to transfer the RF signals between the one or more antennas and the wireless medium at the no-rotation state, and to transfer the RF signals with a predefined polarization rotation applied to the RF signals at the rotation state.
For example, a Light Detection and Ranging (LiDAR) processor may be configured to determine masked Azimuth-Elevation-Frequency (AEF) bin information corresponding to a plurality of AEF bins, for example, based on at least one mask function according to a masking pattern. For example, the masking pattern may define a pattern of one or more masking bin locations of one or more masking AEF bins relative to a masked AEF bin location of a masked AEF bin. For example, determining the masked AEF information may include determining masked AEF bin information for the masked AEF bin by applying the mask function to AEF bin information of the masked AEF bin and to AEF bin information of the one or more masking AEF bins, for example, according to the masking pattern.
H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
For example, a processor may be configured to identify one or more sets of virtual antenna values corresponding to one or more sets of overlapping virtual antennas, respectively. For example, a set of virtual antenna values corresponding to a set of overlapping virtual antennas may include a first virtual antenna value corresponding to a first virtual antenna, and a second virtual antenna value corresponding to a second virtual antenna. For example, the first virtual antenna may be based on a combination of a first Transmit (Tx) antenna and a first Receive (Rx) antenna, and the second virtual antenna may be based on a combination of a second Tx antenna and a second Rx antenna. For example, the processor may determine a plurality of adjusted virtual antenna values by adjusting a plurality of second-Tx-based virtual antenna values based on the one or more sets of virtual antenna values.
A device includes a one-time programmable memory, electrically conductively connected to a voltage source; and a voltage monitor, electrically conductively connected to the voltage source, and configured to detect a voltage of the voltage source; cause the one-time programmable memory to operate according to a first operational mode when the detected voltage is above a first predetermined threshold; and cause the one-time programmable memory to operate according to a second operational mode when the detected voltage is below a second predetermined threshold.
Systems and methods enable detection of occlusion zones while navigating a host vehicle. In one implementation, a navigation system for a host vehicle includes at least one processing device. The at least one processing device is programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify at least one pedestrian occlusion zone in an environment of the host vehicle; and cause a navigational change for the host vehicle based on the identified at least one pedestrian occlusion zone.
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
B60W 30/095 - Predicting travel path or likelihood of collision
B60W 30/16 - Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
B60W 30/165 - Control of distance between vehicles, e.g. keeping a distance to preceding vehicle automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
G01C 21/36 - Input/output arrangements for on-board computers
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G05D 1/243 - Means capturing signals occurring naturally from the environment, e.g. ambient optical, acoustic, gravitational or magnetic signals
G05D 1/249 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons from positioning sensors located off-board the vehicle, e.g. from cameras
G05D 1/617 - Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards
G05D 1/81 - Handing over between on-board automatic and on-board manual control
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G08G 1/0968 - Systems involving transmission of navigation instructions to the vehicle
A method for decompressing data may include receiving a first sequence of bits and performing a plurality of iterations. Each of the plurality of iterations may include scanning bits of the first sequence, starting from a starting point, to search for at least one of a variable length codeword or a bypass indicator, the starting point being either a starting point of the first sequence or a starting point defined in a previous iteration. The method also include, for at least one of the plurality of iterations, when a bypass indicator is found, outputting a neural network coefficient related value (NNCRV) that is non-compressed and follows the bypass indicator, and defining a starting point that follows the NNCRV as a starting point for a next iteration.
A computer-implemented method for navigating a host vehicle includes receiving an image frame from an image capture device, the image frame representing an environment of the host vehicle and including a representation of a target vehicle; analyzing the image frame to determine a sparse representation of a portion of the image frame; providing the sparse representation to an object detection network; receiving an identifier of a candidate region identified by the object detection network; based on the identifier, extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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
9.
MAP AND HIGH-RESOLUTION SNIPPET FOR OBJECT DETECTION
A system for navigating a host vehicle relative to a road segment may receive a captured image acquired by a camera onboard the host vehicle, ; generate a resampled image based on at least a portion of the captured image; determine a first navigational action for the host vehicle based on the resampled image and a map, identify a region of interest in the captured image or the resampled image based on a future path of a mapped target trajectory selected by the navigation system; extract an image snippet from the captured image corresponding to the region of interest, the image snippet having a third resolution of the captured image; analyze the image snippet to determine a second navigational action for the host vehicle; and cause the host vehicle to implement the second navigational action.
G01C 21/36 - Input/output arrangements for on-board computers
G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
Systems and methods for navigating a host vehicle relative to a parking space may include receiving sensed image data acquired by an image sensing system; determining, based at least in part on analysis of the sensed image data, a first indicator of a left boundary and a second indicator of a right boundary associated with a parking space; and determining a parking space approach maneuver initial position. The parking space approach maneuver initial position may be determined based on the determined first and second indicators of the left and right boundaries and also based on a determined indicator of an entrance to the parking space. One or more actuator systems associated with the host vehicle may be caused to active to cause the host vehicle to navigate into the parking space from the parking space approach maneuver initial position.
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
B60W 10/20 - Conjoint control of vehicle sub-units of different type or different function including control of steering systems
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
A system for navigating a host vehicle relative to a road segment may receive a captured image acquired by a camera onboard the host vehicle, wherein the captured image includes representations of at least a first object and a second object present in an environment of the host vehicle; provide the captured image to a trained model configured to receive the captured image as input, apply large language model functionality to establish a semantic constraint between the first object and the second object, and generate an output that identifies the semantic constraint between the first object and the second object; determine, based on the semantic constraint between the first object and the second object, at least one navigational action for the host vehicle; and cause the host vehicle to implement the at least one navigational action.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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
12.
TEMPORARY RULE SUSPENSION FOR AUTONOMOUS NAVIGATION
A navigation system for a host vehicle comprises at least one processing device comprising circuitry and a memory. The memory includes instructions that when executed by the circuitry cause the at least one processing device to: receive a plurality of images acquired by a camera, the plurality of images being representative of an environment of the host vehicle; analyze the plurality of images to identify a plurality of factors associated with a navigation rule suspension condition; assign a weight to each of the plurality of factors; determine, based on the plurality of weighted factors, to temporarily suspend at least one navigational rule; and cause at least one navigational change of the host vehicle unconstrained by the temporarily suspended at least one navigational rule.
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
B60W 30/095 - Predicting travel path or likelihood of collision
B60W 30/16 - Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
B60W 30/165 - Control of distance between vehicles, e.g. keeping a distance to preceding vehicle automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
B60W 50/10 - Interpretation of driver requests or demands
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G01C 21/36 - Input/output arrangements for on-board computers
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G05D 1/243 - Means capturing signals occurring naturally from the environment, e.g. ambient optical, acoustic, gravitational or magnetic signals
G05D 1/249 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons from positioning sensors located off-board the vehicle, e.g. from cameras
G05D 1/617 - Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards
G05D 1/81 - Handing over between on-board automatic and on-board manual control
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G08G 1/0968 - Systems involving transmission of navigation instructions to the vehicle
A system may receive at least one image captured by at least one camera of a host vehicle from the environment of the host vehicle. The system may obtain at least one value of at least one imaging parameter associated with the at least one camera of the host vehicle, and analyze at least a portion of the at least one image to determine at least one light intensity level in the environment of the host vehicle, wherein the at least a portion of the at least one image is analyzed based on the at least one value of the at least one imaging parameter associated with the at least one camera of the host vehicle.
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
G06V 10/40 - Extraction of image or video features
G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
A system may receive, from a map associated with a road segment, at least one indicator of a lane of travel associated with the road segment, location information associated with the lane of travel, an identifier of a road element associated with the road segment, and location information associated with the road element; generate a graph representation of a junction associated with the road segment including a plurality of nodes and one or more semantic links; provide the graph representation to a trained model configured to infer a relationship between the road element and the lane of travel; based on the output of the trained model, store in the map road element relationship; and distribute the map for use in navigating along the road segment.
A Printed Circuit Board (PCB) may include one or more single-ended PCB traces configured to route single-ended Radio-Frequency (RF) signals between an integrated circuit and one or more waveguides, wherein first ends of the one or more single-ended PCB traces are to be coupled to the integrated circuit; and one or more PCB-to-waveguide transitions configured to couple second ends of the one or more single-ended PCB traces to the one or more waveguides. For example, a PCB-to-waveguide transition may include a PCB probe connected to a second end of a single-ended PCB trace of the one or more single-ended PCB traces, the PCB probe configured to couple RF energy of the single-ended RF signals between the single-ended PCB trace and a waveguide of the one or more waveguides; and a via configured to electrically connect the PCB probe to a ground layer of the PCB.
H01P 5/10 - Coupling devices of the waveguide type for linking lines or devices of different kinds for coupling balanced lines or devices with unbalanced lines or devices
G01S 7/03 - Details of HF subsystems specially adapted therefor, e.g. common to transmitter and receiver
G01S 13/931 - Radar or analogous systems, specially adapted for specific applications for anti-collision purposes of land vehicles
The present disclosure relates to an apparatus for processing data from a LIDAR sensor, the apparatus including a processor configured to: obtain a LIDAR map, wherein the LIDAR map includes an aggregation of a plurality of LIDAR scans from a LIDAR sensor; obtain an additional first LIDAR scan from the LIDAR sensor, wherein the first LIDAR scan includes a plurality of first data points; obtain pose information representative of each of a plurality of poses of the LIDAR sensor during the first LIDAR scan; and determine a rigid transformation to map each of the plurality of first data points onto the LIDAR map based on at least two adjacent poses of the plurality of poses.
A system for navigating a host vehicle relative to a road segment may store a speed limit indicator in a map among navigation information associated with the road segment. The system may determine a first navigational action for the host vehicle based on the received speed limit indicator; cause at least one system associated with the host vehicle to implement the first navigational action; receive an image acquired by at least one camera onboard the host vehicle; determine a speed limit affecting condition represented in the received image; determine a condition-based speed limit based on the speed limit affecting condition; determine a second navigational action for the host vehicle based on the condition-based speed limit; and cause the at least one system associated with the host vehicle to implement the second navigational action.
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
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
Systems and methods may generate a map for use in navigating a host vehicle relative to a road segment by receiving drive information from a vehicle that previously traversed the road segment, providing a representation of at least one road topography indicator to a trained model, wherein the trained model is configured to provide a driving protocol indicator as an output; generating, based on the driving protocol indicator, a representation of at least one driving convention associated with the driving protocol indicator and storing the representation of the at least one driving convention in the map; and distributing the map to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the stored representation of the at least one driving convention.
Distance calculations for autonomous vehicle (AV) or advanced driver assistance system (ADAS) navigation may include calculating a distance between two points within an image captured by a vehicle camera. To calculate distance in ADAS and AV systems, the vectors and other values may be represented as floating-point (FP) values. The use of FP values may provide improved performance of these calculations in ADAS, AV systems, and other systems that seek to reduce or minimize computation speed. When the input values are represented as FP values, the distance may be calculated as a square root of a sum of squares of the two FP values. Improved systems and methods are provided for floating-point calculation related to a square root, such as for determining distance calculations.
G06F 7/483 - Computations with numbers represented by a non-linear combination of denominational numbers, e.g. rational numbers, logarithmic number system or floating-point numbers
Distance calculations for autonomous vehicle (AV) or advanced driver assistance system (ADAS) navigation may include calculating a distance between two points within an image captured by a vehicle camera. To calculate distance in ADAS and AV systems, the vectors and other values may be represented as floating-point (FP) values. The use of FP values may provide improved performance of these calculations in ADAS, AV systems, and other systems that seek to reduce or minimize computation speed. When the input values are represented as FP values, the distance may be calculated as a square root of a sum of squares of the two FP values. Improved systems and methods are provided for floating-point calculation related to a square root, such as for determining distance calculations.
Techniques are disclosed for reducing false positives for generating warnings to avoid potential collisions between a vehicle and vulnerable road users (VRUs). This is accomplished via an onboard vehicle safety system that uses crowdsourced map data to determine whether a vehicle is capable of performing a maneuver that results in a lateral shift of the vehicle (which may include a lane-shifting or turning maneuver) within a predetermined threshold time period. The ability for the vehicle to make the turning maneuver, among other driving scenarios, may be used to by the safety system to intelligently determine whether a warning or other action is needed to avoid a potential collision with a VRU. In this way, the occurrence and number of false warnings/interventions are minimized or at least reduced, leading to more attentive drivers and thereby improving VRU safety.
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
G08G 1/052 - Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
22.
Vehicle trajectory estimation for the generation of ground truth data
Techniques are disclosed to enable the generation of ground truth datasets that identify a continuous time movement trajectory of dynamic objects such as non-stationary vehicles. The techniques as described herein may utilize iterative closest point (ICP) processes to compute a rigid-body transform that describes a 3D correspondence between different pairs of temporal point cloud data sample sets at various discrete time periods that are identified with an object along a movement trajectory. From these rigid-body transforms at each of the discrete time periods, a pose graph optimization may be performed to compute the continuous time movement trajectory. The ground truth datasets may be used to enable training of machine learning models, which may be used by a vehicle computing system when navigating a driving environment.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60W 40/02 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to ambient conditions
The disclosed technique is an any-in, first-out (“AIFO”) memory device that improves upon conventional first-in, first-out memory devices. The technique mimics a first-in, first-out queue to provide sequential storage and retrieval in scenarios where transactional ordering cannot be guaranteed. By associating an identifier and a write-pointer in a look-up table with read requests prior to transmission, responses may be received out of order and/or interleaved without delaying processing until the next response in the sequence is received. An internal validity flag may be included in the AIFO memory array to represent the validity of each line in the memory array. Once a read pointer reaches a row that is marked as invalid, the read command pauses and discontinues further reading until a write to the row that marks the row as valid. To further optimize writing, a polarity of the validity may be toggled each read cycle. The level of the AIFO queue may be approximated using a vector-based algorithm that analyzes the look-up table.
A system may receive drive information from each of a plurality of harvesting vehicles that traversed a road segment, wherein the drive information includes at least one indicator of an actual trajectory traveled by the harvesting vehicle and altitude information associated with a road surface included among a plurality of road surfaces associated with the road segment, wherein each of the plurality of road surfaces is located at a different altitude. The system may aggregate the drive information to determine at least one host vehicle drivable path, store a representation of the drivable path, generate a road surface representation for each road surface, store the road surface representations, assign a detected road feature to a road surface representation, and distribute the map to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the host vehicle drivable path.
A system for navigating a host vehicle relative to a road segment may receive object detection information relating to detection of one or more objects represented in a first image frame captured by at least one camera onboard the host vehicle; receive a second image frame captured by the at least one camera onboard the host vehicle; receive a portion of a map representative of the road segment; provide to a trained model the object detection information, the second image frame, and a representation of at least a portion of the at least one drivable path, wherein the trained model is configured to identify at least one target vehicle represented in the second image frame based, at least in part, on inferences relative to the object detection information, the second image frame, and the representation of at least a portion of the at least one drivable path.
G06T 7/70 - Determining position or orientation of objects or cameras
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
Systems and methods are provided for generating a crowd-sourced map for use in vehicle navigation. In one implementation, a system may include at least one processor configured to receive drive information collected from vehicles that traversed a junction; aggregate the received drive information to determine positions of traffic lights and spline representations for drivable paths; input the determined positions and the spline representations to a trained model configured to generate a traffic light relevancy mapping indicating a traffic light relevancy for traffic light to drivable path pairs of the junction; input an observed vehicle behavior to the at least one trained model to generate an updated traffic light relevancy mapping; store in the crowd-sourced map the indicators of traffic light relevancy for the traffic light to drivable path pairs; and transmit the crowd-sourced map to a vehicle for use in navigating the road segment.
A navigation system may receive a captured image acquired by a camera onboard a host vehicle; determine a localized position of the host vehicle relative to a map representative of the road segment; generate based on the localized position of the host vehicle and three-dimensional location information associated with the plurality of topography features, a two-dimensional image representation of a plurality of road topography features; analyze the captured image and the two-dimensional image representation and determine three-dimensional information for at least one object represented in the captured image, but not represented in the map representative of the road segment, based on the three-dimensional location information associated with the plurality of topography features included in the map; determine, based on the three-dimensional information for the at least one object, at least one navigational action for the host vehicle; and cause the host vehicle to implement the at least one navigational action.
G01C 21/36 - Input/output arrangements for on-board computers
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive an image captured by a camera onboard the host vehicle; provide the image to a trained model configured to generate an output identifying two or more target trajectories associated with each of the two or more features represented in the image; determine, based on the output generated by the trained model, location information for the two or more target trajectories associated with each of the two or more features; determine at least one navigational action for the host vehicle based on the location information determined for at least one of the target trajectories; and cause the host vehicle to implement the at least one navigational action.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
A navigation system for a host vehicle may generate an image input based on at least one image captured by the camera onboard the host vehicle; provide the image input to a trained model configured to generate, based on the image input, an output identifying a left side boundary of a lane of travel for the host vehicle relative to a road surface and a right side boundary of the lane of travel for the host vehicle relative to the road surface; determine, based on the output generated by the trained model, a first location information for the left side boundary and a second location information for the right side boundary; determine at least one navigational action for the host vehicle based on at least one of the first location information or the second location information; and cause the host vehicle to implement the at least one navigational action.
For example, a compiler may be configured to compile a source code into a target code configured for execution by a target processor in a plurality of execution cycles including a first execution cycle, a second execution cycle after the first execution cycle, and a third execution cycle after the second execution cycle. For example, the target code may include one or more no operation (no-op) instructions configured to maintain a first variable live such that a value of the first variable is to be available in a register of the target processor at the first execution cycle and at the third execution cycle.
A driver assistance system may also include a driver monitoring system that determines driver awareness. The driver monitoring system may include a camera that observes the driver. According to an embodiment, based on an image captured from the camera, a gaze direction of the driver may be determined. The gaze direction may be evaluated to determine whether it is directed at an object from the vehicle surroundings. If the object is within the driver trajectory, a driver assistance system may recognize that the driver is aware of the object. For that reason, the driver assistance system may delay automatic braking that would otherwise be triggered due to the object.
B60T 7/22 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle
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
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 40/18 - Eye characteristics, e.g. of the iris
32.
PREDICTING DRIVER PATH BASED ON GAZE DIRECTION, AND APPLICATIONS THEREOF
Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for predicting a driver path of a vehicle. In the method, an image of a driver of the vehicle is received. Based on the image, a gaze direction of the driver is determined. Based at least in part on the gaze direction, a path of the vehicle is predicted. The vehicle is controlled based on the predicted path.
B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
B60W 10/18 - Conjoint control of vehicle sub-units of different type or different function including control of braking systems
B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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
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
A navigation system for a host vehicle may include at least one processor comprising circuitry and a memory. The memory may include instructions that when executed by the circuitry cause the at least one processor to receive from an image capture device associated with the host vehicle a captured image representative of an environment of the host vehicle, to identify a first segment of the captured image associated with a traffic light, to provide the first segment of the captured image to a first trained network, the first trained network being configured to generate a first output indicative of a state of the traffic light, to identify a second segment of the captured image that includes contextual information associated with the traffic light, to provide the second segment to a second trained network, the second trained network being configured to generate a second output indicative of a proposed navigational action for the host vehicle relative to the traffic light, to determine, based on both the first output from the first trained network and the second output from the second trained network a planned navigational action for the host vehicle and to cause the host vehicle to take the planned navigational action.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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
34.
SAFETY SYSTEM ARCHITECTURE FOR SELF-DRIVING SYSTEMS
A host vehicle navigation system for navigating a host vehicle relative to a road segment. The system may receive, at a first navigation sub-system, first information indicative of a drive state associated with the host vehicle, receive, at a second navigation sub-system, second information indicative of the drive state, provide the output of the first navigation sub-system to an intermediary sub-system configured to monitor the output and generate a directive output indicating which navigation sub-system output should be relied upon, and determine a navigational action based on the output of the intermediary sub-system and either the output of the first navigation sub-system or the second navigation sub-system. The second navigation sub-system output may be redundant, in one or more aspects, with respect to the first navigation sub-system output.
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
H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
35.
ALTERING LANE KEEP ASSIST BEHAVIOR BASED ON WHETHER A DRIVER IS GAZING AT A TARGET LANE
A driver assistance system may also include a driver monitoring system that determines driver awareness. The driver monitoring system may include a camera that observes the driver. Embodiments avoid unnecessary lane keeping activity based on where the driver is gazing. For example, if the driver is looking at an adjacent lane, then lane keeping activity to avoid or discourage the vehicle from moving into the adjacent lane may be disabled. And vice-versa, if the vehicle is shifting to adjacent lane in one direction and the driver is gazing at the opposite side, then the lane keeping activity, or at least warning, could be activated earlier.
B60W 50/08 - Interaction between the driver and the control system
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60W 30/00 - Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
36.
USING A DRIVER'S GAZE DIRECTION TO IMPROVE OBJECT DETECTION, AND APPLICATIONS THEREOF
Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for improving object detection based on gaze direction. In the method, a first image of surroundings of a vehicle is received. A second image of a driver of the vehicle is also received. The second image captured contemporaneously with the first image. Based on the second image, a gaze direction of the driver is determined. Based on the gaze direction, a region of the first image being viewed by the driver is determined. An object detection algorithm is applied to recognize an object in the surroundings of the vehicle at a greater level of detail within the region.
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
B60T 7/22 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/52 - Scale-space analysis, e.g. wavelet analysis
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
A system for generating map information for use in navigating a host vehicle may comprise at least one processor programmed to: receive drive information from each of a plurality of vehicles that traversed the road segment, wherein the drive information includes one or more indicators of a road topography feature and the drive information includes actual trajectory information; generate, based on the one or more indicators of the actual trajectory information, a map including at least a portion of a drivable path for the road segment; provide the map as input to at least one trained model configured to generate, in response to the provided input, an output including an updated map with at least one updated drivable path for the road segment; and provide the updated map to at least one host vehicle navigation system.
Embodiments here observe the driver and determine an orientation of an articulating camera based on driver gaze tracking and external object detection. A fixed camera collects image data of the vehicle's surroundings, and an articulating camera collects image data of the driver. The gaze direction is used to identify what objects in the vehicle's surroundings the driver is looking at. Based on the position of the object, an orientation of the articulating camera may be determined.
For example, a compiler may be configured to identify a loop nest based on a source code to be compiled into a target code to be executed by a target processor, the loop nest including a plurality of loops including at least a first loop and a second loop nested in the first loop, the first loop including at least one first-loop instruction outside the second loop; and to generate Address Generation Unit (AGU) configuration code to configure an AGU of the target processor based on the first-loop instruction, wherein the AGU configuration code is to configure a first dimension of the AGU based on the first loop and a second dimension of the AGU based on the second loop to configure a memory-access operation based on the first-loop instruction, to be performed at a start of the second loop or at an end of the second loop.
For example, a compiler may be configured to identify a plurality of memory-access operations in a loop based on a source code to be compiled into a target code to be executed by a target processor, the plurality of memory-access operations may include at least a first memory-access operation and a second memory-access operation to a same memory pointer. For example, the first memory-access operation may have a first offset, and the second memory-access operation may have a second offset different from the first offset. For example, the compiler may configure Address Generation Unit (AGU) configuration code to configure the plurality of memory-access operations by a same AGU. For example, the compiler may generate the target code based on compilation of the source code. For example, the target code may be based on the AGU configuration code.
A driver assistance system may also include a driver monitoring system that determines driver awareness. The driver monitoring system may include a camera that observes the driver. Embodiments avoid unnecessary lane keeping activity based on where the driver is gazing. For example, if the driver is looking at an adjacent lane, then lane keeping activity to avoid or discourage the vehicle from moving into the adjacent lane may be disabled. And vice-versa, if the vehicle is shifting to adjacent lane in one direction and the driver is gazing at the opposite side, then the lane keeping activity, or at least warning, could be activated earlier.
Embodiments here observe the driver and determine an orientation of an articulating camera based on driver gaze tracking and external object detection. A fixed camera collects image data of the vehicle's surroundings, and an articulating camera collects image data of the driver. The gaze direction is used to identify what objects in the vehicle's surroundings the driver is looking at. Based on the position of the object, an orientation of the articulating camera may be determined.
B60R 1/29 - Real-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 for viewing an area inside the vehicle, e.g. for viewing passengers or cargo
43.
SAFETY SYSTEM ARCHITECTURE FOR SELF-DRIVING SYSTEMS
A host vehicle navigation system for navigating a host vehicle relative to a road segment. The system may receive, at a first navigation sub-system, first information indicative of a drive state associated with the host vehicle, receive, at a second navigation sub-system, second information indicative of the drive state, provide the output of the first navigation sub-system to an intermediary sub-system configured to monitor the output and generate a directive output indicating which navigation sub-system output should be relied upon, and determine a navigational action based on the output of the intermediary sub-system and either the output of the first navigation sub-system or the second navigation sub-system. The second navigation sub-system output may be redundant, in one or more aspects, with respect to the first navigation sub-system output.
While the cameras used by an ADAS system may provide an unobstructed view of the surroundings, a driver's view may be obstructed. For example, a driver's view could be obstructed by the construction of the vehicle. Analysis may be conducted on the images collected by the ADAS system to identify objects that are in areas where the driver's view is occluded. If the driver's view of the object is occluded, the ADAS may behave differently than it otherwise would if the driver was aware of the object. For example, the ADAS system may apply automatic braking to avoid hitting an object more quickly if the driver cannot see it due to an occlusion.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
B60T 7/22 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle
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
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
45.
APPARATUS, SYSTEM, AND METHOD OF COMPILING CODE FOR A PROCESSOR
For example, a compiler may be configured to identify a loop nest based on a source code, the loop nest including a plurality of loops, the plurality of loops including at least a first loop and second loop nested in the first loop, wherein the first loop includes at least one first-loop instruction which is outside the second loop, wherein the second loop includes one or more second-loop instructions; to transform the loop nest into a transformed loop, the transformed loop including a conditional instruction based on the first-loop instruction, the conditional instruction based on a state of a second-loop predicate, wherein the second-loop predicate is to identify a start of the second loop or an end of the second loop; and one or more transformed-loop instructions based on the one or more second-loop instructions; and to generate target code based on the transformed loop.
Techniques are disclosed for performing an image sensor profiling process and accompanying architecture that facilitates performing an image quality measurement over a range of different parameters such as light levels, exposure values, contrast targets, confidence intervals, color ratios, temperature, etc. An image sensor profile may then be generated from these measurements includes an image sensor performance profile dataset. This dataset provides contrast detection probability (CDP) measurement data over a full range of operating parameters of the image sensor. The image sensor performance profile dataset may quantify the image quality and performance for a full range of operation. The image sensor performance profile dataset may be implemented to generate an image sensor model, which may be deployed in a vehicle and used to modify the functions thereof during operation.
A method for automatically placing and routing processing elements (PEs), where each PE has a limited connectivity to its neighbors. The method may obtain placement primitive (PP) location window information, the location window information defining possible locations of a group of PPs within a coarse-grained reconfigurable array (CGRA). The method may obtain hardware constraints regarding the array of PEs, the hardware constraints comprise local connectivity constraints and remote connectivity constraints. The method may receive a computation graph (CG) that represents mathematical expressions to be calculated by the array of PEs. The method may determine a location of the PEs of the array of PEs based on the CG, the PP location window information, and hardware constraints.
G06F 15/80 - Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
48.
GENERATING MAPS FROM CROWDSOURCED DRIVE INFORMATION
A system may receive drive information from each of a plurality of harvesting vehicles that traversed a road segment and provide a representation of the drive information as input to a trained model. The trained model may be configured to determine that at least a first sub-portion of a first trajectory representation and second sub-portion of a second trajectory representation correspond to a common road interval included in a road segment and output an indication of a correlation between the first sub-portion, the second sub-portion, and the common road interval, align and aggregate the drive information associated with the common road interval based on the output of the trained model to generate a location indicator for at least one map feature of the road segment, store the location indicator for at least one map feature in the map, and distribute the map
For example, a compiler may be configured to identify a select instruction in a loop operation based on a source code, the select instruction to select between a first value and a second value according to a mask in the select instruction: to configure a masked operation in the loop operation based on the select instruction, wherein the masked operation is based on the mask in the select instruction, the masked operation including a passthrough value based on the second value; and to generate target code based on compilation of the source code, wherein the target code is based on the masked operation.
For example, a compiler may be configured to identify a first masked memory-access operation based on a source code, wherein the first masked memory-access operation is based on a first mask expression comprising one or more mask leaves: to determine a second masked memory-access operation by reconfiguring the first masked memory-access operation based on an identified mask leaf of the one or more mask leaves, wherein the second masked memory-access operation is based on a second mask expression which is logically simplified compared to the first mask expression; and to generate target code based on compilation of the source code, wherein the target code is based on the second masked memory-access operation.
Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium includes a sparse map for autonomous vehicle navigation along a road segment. The sparse map includes a polynomial representation of a target trajectory for the autonomous vehicle along the road segment and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters. The sparse map has a data density of no more than 1 megabyte per kilometer.
A computer-implemented method for navigating a host vehicle may include receiving an image frame acquired by an image capture device associated with the host vehicle; identifying in the image frame a representation of a target vehicle; determining an orientation indicator associated with the target vehicle; based on the determined orientation indicator, identifying a candidate region of the acquired image frame where a representation of a vehicle door of the target vehicle is expected in an open door condition; extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.
For example, a compiler may be configured to identify a first data operation and a second data operation, which are executable in parallel according to a SIMD instruction to be executed by a single ALU of a target processor: to determine a selected compilation scheme from a first compilation scheme and a second compilation scheme based on a predefined selection criterion, wherein the first compilation scheme includes compilation of the first data operation and the second data operation into the SIMD instruction, wherein the second compilation scheme includes compilation of the first data operation into a first ALU instruction, and compilation of the second data operation into a second ALU instruction to be executed separately from the first ALU instruction; and to generate target code based on compilation of the first data operation and the second data operation according to the selected compilation scheme.
Techniques are disclosed for improving the detection of objects having different relative angular velocities with respect to the vehicle cameras. The techniques function to selectively weight pixel exposure values to favor longer or shorter exposure times for certain pixels within the pixel array over others. A selective pixel exposure weighting system is disclosed that functions to weight the exposure values for pixels acquired within a pixel array based upon the position of the pixel within the pixel array and other factors such as the movement and/or orientation of the vehicle. The techniques advantageously enable an autonomous vehicle (AV) or advanced driver-assistance systems (ADAS) to make better use of existing cameras and eliminate motion blur and other artifacts.
Techniques are disclosed for performing knowledge distillation in the context of machine learning model training. A fixed pool of “teacher” models are provided, which meet predefined conditions. A machine learning model is then trained to provide a “student.” by applying a random selection of the teachers to unlabeled sample data, which accelerates the training process. The algorithm implemented for this purpose ensures that the trained student provides low error.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
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
A system for navigating a host vehicle relative to a road segment is disclosed. The system may comprise at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive a captured image acquired by a camera onboard the host vehicle; based on analysis of the image, generate image height information including a predicted height value for each of a first plurality of pixels included in the captured image, the predicted height value for each of the first plurality of pixels being indicative of height above a ground surface; based on analysis of the image, generate image range information, including a predicted range value for each of a second plurality of pixels included in the captured image, the predicted range value for each of the second plurality of pixels being indicative of distance relative to the camera; and determine a navigational action for the host vehicle based on at least one of the image height information or the image range information.
G05D 1/243 - Means capturing signals occurring naturally from the environment, e.g. ambient optical, acoustic, gravitational or magnetic signals
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
Systems and methods are provided for vehicle navigation. In one implementation, at least one processing device may receive, from a camera of the host vehicle, at least one captured image representative of an environment of the host vehicle. The processing device may analyze one or more pixels of the at least one captured image to determine whether the one or more pixels represent at least a portion of a target vehicle. For pixels determined to represent at least a portion of the target vehicle, the processing device may determine one or more estimated distance values from the one or more pixels to at least one edge of a face of the target vehicle; and generate, based on the analysis of the one or more pixels, including the determined one or more distance values associated with the one or more pixels, at least a portion of a boundary relative to the target vehicle.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
B60R 11/00 - Arrangements for holding or mounting articles, not otherwise provided for
B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G05D 1/249 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons from positioning sensors located off-board the vehicle, e.g. from cameras
G06T 7/70 - Determining position or orientation of objects or cameras
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
Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle may comprise at least one processor. The processor may be programmed to receive from a first camera at least a first captured image representative of an environment of the host vehicle. The processor may be programmed to receive from a second camera at least a second captured image representative of the environment of the host vehicle. Both the first captured image and the second image includes a representation of the traffic light, and wherein the second camera is configured to operate in a primary mode where at least one operational parameter of the second camera is tuned to detect at least one feature of the traffic light. The processor may be further programmed cause at least one navigational action by the vehicle based on analysis of the representation of the traffic light.
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
G06V 10/143 - Sensing or illuminating at different wavelengths
G06V 10/88 - Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
59.
Systems and methods for searching an image database
The present disclosure is directed to searching an image database. A system may include at least one processor comprising circuitry and a memory, wherein the memory includes instructions that, when executed by the circuitry cause the at least one processor to: receive a first user input including one or more scenario descriptors, identify in a database an initial plurality of images based on the first user input and display the initial plurality of images to a user. The processor may be further configured to receive a second user input wherein the second user input identifies at least one of the initial plurality of images. The at least one processor may identify a refined plurality of images in the database based on the one or more scenario descriptors in combination with the second user input and display the refined plurality of images to the user.
G06F 16/58 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
60.
APPARATUS, SYSTEM, AND METHOD OF CONTROLLING AN ARRAY-RADIATION PATTERN OF AN ANTENNA ARRAY
For example, an apparatus may include an antenna array, which may be configured to include a plurality of configurable-radiation-pattern antenna elements. For example, a configurable-radiation-pattern antenna element of the plurality of configurable-radiation-pattern antenna elements may have a configurable element-radiation-pattern. For example, the apparatus may include control circuitry, which may be configured to control an array-radiation-pattern of the antenna array. For example, the control circuitry may be configured to control the array-radiation-pattern of the antenna array according to an array-radiation-pattern setting, for example, by configuring a plurality of element-radiation-patterns for the plurality of configurable-radiation-pattern antenna elements based on the array-radiation-pattern setting.
For example, a polarization controller may be configured to control a polarization for an antenna. For example, the polarization controller may include a processor, which may be configured to process interference information to identify angle-based information. For example, the angle-based information may be based on an angle of an interferer signal relative to a boresight of the antenna. For example, the processor may be configured to determine a polarization setting of the antenna based on the angle-based information. For example, the polarization controller may include an output to provide a control output to control the polarization for the antenna based on the polarization setting.
For example, an Amplifier-Oscillator (AMP-OSC) may be switchable between an amplifying mode and an oscillating mode based on a control input. For example, the AMP-OSC may include an input terminal; an output terminal; and an AMP-OSC core connected between the input terminal and the output terminal. For example, the AMP-OSC core may be operable at an amplification core-mode based on a first setting of the control input corresponding to the amplifying mode, and operable at an oscillation core-mode based on a second setting of the control input corresponding to the oscillating mode. For example, at the amplification core-mode, the AMP-OSC core may provide an amplified signal to the output terminal by amplifying an input signal from the input terminal. For example, at the oscillation core-mode, the AMP-OSC core may generate an oscillating signal, and may provide the oscillating signal to the output terminal.
H03B 5/12 - Generation of oscillations using amplifier with regenerative feedback from output to input with frequency-determining element comprising lumped inductance and capacitance active element in amplifier being semiconductor device
For example, an apparatus may include a processor, which may be configured to identify a road segment for a vehicle; and to determine a Radar Transmit Configuration (RTC) setting for at least one radar radio of the vehicle based on an RTC allocation corresponding to the road segment. For example, the RTC allocation corresponding to the road segment may be based on a road topology at the road segment. For example, the RTC setting may define a setting of one or more RTC parameters to be implemented for transmission of radar signals by the at least one radar radio at the road segment. For example, the apparatus may include an output to provide RTC setting information based on the RTC setting.
For example, an apparatus may include a processor, which may be configured to identify a particular Transmit (Tx) configuration selected from a plurality of Tx configurations. For example, the plurality of Tx configurations may have a plurality of different Pulse Repetition Intervals (PRIs), respectively. For example, the particular Tx configuration may have a particular PRI from the plurality of different PRIs. For example, the processor may be configured to generate Tx configuration information to configure transmission of a plurality of radar Tx pulses from a radar device according to the particular PRI of the particular Tx configuration. For example, the apparatus may include an output to provide the Tx configuration information.
G01S 13/32 - Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
G01S 13/34 - Systems 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 13/88 - Radar or analogous systems, specially adapted for specific applications
65.
APPARATUS, SYSTEM, AND METHOD OF SYSTEM ON CHIP (SOC) FUNCTIONAL SAFETY (FUSA)
For example, a System on Chip (SoC) may include a plurality of Integrated Circuits (ICs); at least one Network on Chip (NoC) to communicate information between the plurality of ICs; and a plurality of parity circuits on a plurality of IC-NoC paths between the plurality of ICs and the at least one NoC. For example, the plurality of parity circuits may be configured according to a same parity protocol. For example, a parity circuit on an IC-NoC path between an IC and the at least one NoC may include a parity generator and a parity checker. For example, the parity generator may be configured to generate a first parity value for first information provided from the IC to the at least one NoC, and the parity checker may be configured to check a second parity value of second information provided from the at least one NoC to the IC.
For example, an apparatus may include an input to receive digital radar Receive (Rx) information corresponding to radar Rx signals, the digital radar Rx information having a first number-of-bits-per-sample; and a noise-shaping quantizer configured to generate quantized radar Rx information by quantizing the digital radar Rx information. For example, the quantized radar Rx information may have a second number-of-bits-per-sample less than the first number-of-bits-per-sample. For example, the noise-shaping quantizer may be configured to generate the quantized radar Rx information having a non-uniform quantization noise spectrum, which has a non-uniform distribution in a frequency domain. For example, the apparatus may include an output to provide the quantized radar Rx information.
For example, an apparatus may include a transmission controller configured to generate control signals to control transmissions via an antenna array according to a plurality of transmission modes, the plurality of transmission modes including a single-element transmission mode and a multi-element transmission mode. For example, the single-element transmission mode may include a plurality of single-element transmissions via a plurality of single-element antennas. For example, a single-element antenna may include a single antenna element of the antenna array. For example, the multi-element transmission mode may include a plurality of multi-element transmissions via a plurality of multi-element antennas. For example, a multi-element antenna may include two or more adjacent antenna elements of the antenna array. For example, a multi-element transmission via the multi-element antenna may include a simultaneous transmission via the two or more adjacent antenna elements.
H04B 7/04 - Diversity systemsMulti-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
H04B 7/06 - Diversity systemsMulti-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
A system for navigating a host vehicle relative to a road segment includes at least one processor comprising circuitry and a memory. The memory includes instructions that when executed by the circuitry cause the at least one processor to: store map information for navigation, wherein the map includes a plurality of tiles, wherein each of the plurality of map tiles includes edges and each of the edges has an associated checksum value, wherein the checksum value represents edge features; receive an updated tile for the map and compare checksum values for adjacent edges of the updated tile; and cause the host vehicle to take at least one remedial action when the checksum values for the adjacent edges do not match.
Techniques are disclosed for an unsafe hot spot detection and reporting system to identify unsafe “hotspot” zones. The system functions to identify hotspot zones that are associated with an increased number of detected warnings regarding unsafe conditions, vehicle collisions, near misses, or other hazardous events. The hotspots are determined based upon historical data that may include safety-based warnings issued from vehicle with respect to in-vehicle monitoring data or other data sources. The system leverages the hotspot location information to identify a safety data model (SDM) rule and automatically adjust one or more safety data model (SDM) parameters of the SDM rule such as follow distance, speed settings, etc., and/or to route the vehicle to an alternate course to avoid an identified unsafe hotspot zone.
Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive at least one image captured by a front view camera of the host vehicle; analyze the at least one image to detect a representation of a traffic sign in an environment of the host vehicle; determine a relevancy of the traffic sign to the host vehicle, wherein the relevancy of the traffic sign to the host vehicle is determined based on a trajectory of the host vehicle relative to a location of the traffic sign; and cause the host vehicle to implement at least one navigational action based on the relevancy of the traffic sign to the host vehicle.
B60W 10/04 - Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
B60W 10/18 - Conjoint control of vehicle sub-units of different type or different function including control of braking systems
B60W 10/20 - Conjoint control of vehicle sub-units of different type or different function including control of steering systems
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
71.
CONFORMAL RISK CONTROL SYSTEM AND METHOD FOR LANE PRIORITY ASSIGNMENT
A system for generating a map for use in navigating a host vehicle relative to a road segment. The system may receive a representation of at least a first lane and a second lane associated with the road segment, provide at least one descriptor associated with the representation of the first lane and at least one descriptor associated with the representation of the second lane as input to a trained model configured to apply a conformal prediction technique to generate an output including an indicator of which of the first lane or the second lane has navigational priority with respect to the other, store in the map an indication of which of the first lane or the second lane has navigational priority based on the output of the trained model, and distribute the map to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to at least one of the first lane or the second lane and further relative to the indicator of which of the first lane or the second lane has navigational priority.
In one implementation, a system generates a map for use in navigating a host vehicle relative to a road segment. The system may receive drive information from each of a plurality of vehicles that traversed the road segment. The system may aggregate indicators representative of road topography features and generate a representation of road topography of the road segment based on the aggregated indicators; provide the representation of road topography of the road segment as input to at least one trained model configured to generate, in response to the provided input, an output including a target trajectory for at least a first portion of the road segment; aggregate the actual trajectory information included in the drive information received from the plurality of vehicles that traversed the road segment; generate a crowdsourced trajectory for at least a second portion of the road segment based on the aggregated actual trajectory information; combine the target trajectory and the crowdsourced trajectory to generate a hybrid trajectory associated with the road segment; store the hybrid trajectory in the map; and provide the map to at least one host vehicle navigation system.
A system for generating a map for use in navigating a host vehicle relative to a road segment, including: at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive road topography information representative of one or more features associated with the road segment; provide one or more indicators associated with the road topography information as input to a trained model, wherein the trained model is configured to: determine a location indicator for at least one map feature based on the one or more indicators associated with the road topography information; determine a quality value associated with the location indicator; and output the location indicator and the quality value; store in the map the determined location indicator for the at least one map feature; store in the map the determined quality value associated with the location indicator; and distribute the map to a host vehicle navigation system for use in navigating the host vehicle relative to the road segment.
The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may receive a plurality of images captured by an image capture device onboard the host vehicle, the plurality of images being associated with an environment of the host vehicle; analyze at least one of the plurality of images to identify the parked vehicle and at least one light associated with the parked vehicle; determine, based on an analysis of at least two of the plurality of images, a change in an illumination state of the at least one light associated with the parked vehicle; and cause at least one navigational change of the host vehicle based on the change in the illumination state of the at least one light associated with the parked vehicle.
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
B60W 30/08 - Predicting or avoiding probable or impending collision
G05D 1/228 - Command input arrangements located on-board unmanned vehicles
G05D 1/249 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons from positioning sensors located off-board the vehicle, e.g. from cameras
A system may comprise at least one processor comprising circuitry and a memory including instructions that when executed by the circuitry cause the at least one processor to: receive first navigation data representative of aspects of the road segment; receive second navigation data representative of aspects of the road segment; generate a first list of object nodes based on the first navigation data; generate a second list of object nodes based on the second navigation data; generate edge links between the first list of object nodes and the second list of object nodes to form a graph; and provide the graph to a trained graph neural network configured to use the graph to correlate object nodes from the first list with object nodes from the second list and output a fused list of embeddings representative of correlated and combined information from the first sensor and the second sensor.
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
A system for detecting road topography changes in a road segment using a trained model, the system comprising: at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive at least one drive data packet from at least one vehicle that traversed the road segment, wherein the at least one drive data packet includes one or more representations of at least one road topography object associated with the road segment, generate a collected data representation of road topography for the road segment based on the one or more representations of the at least one road topography object included in the at least one drive data packet, obtain, from a road topography database, a mapped data representation for the road segment based on stored road topography information, provide to the trained model an input including the collected data representation and the mapped data representation of road topography for the road segment, wherein the trained model is configured to automatically identify at least one change in road topography of the road segment based on an inferred difference between the collected data representation and the mapped data representation of road topography, and wherein the trained model is also configured to provide an output identifying the at least one change in road topography and indicating whether the at least one change in road topography would impact at least one vehicle drivable path representation stored in the road topography database by more than a threshold amount, and update the road topography database based on the at least one change in road topography after a determination that the at least one change in road topography would impact the at least one vehicle drivable path representation stored in the road topography database by more than a threshold amount.
The disclosed embodiments include a system for identifying objects in an environment of a host vehicle, the system comprising: at least one processor programmed to: receive from a camera onboard the host vehicle a captured image representative of the environment of the host vehicle; identify an image segment within the captured image that includes a representation of an object of interest; input the image segment into a neural network trained to emulate operation of a Contrastive Language-Image Pre-Training (CLIP) model; and receive from the neural network an identifier associated with the object of interest.
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
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
A system navigated a host vehicle relative to a road segment. The system may receive a first image frame acquired at a first time by a camera onboard the host vehicle; receive a second image frame acquired at a second time by the camera onboard the host vehicle, wherein the second time is later than the first time; based on analysis of the first image frame, generate a point cloud of 3D points for the first image frame, wherein the generated point cloud includes at least a predicted range value for each of a plurality of pixels included in the first image frame, the predicted range value for each of the plurality of pixels being indicative of distance between the camera and one or more objects in an environment of the host vehicle; generate a synthentic image frame based on the generated point cloud of 3D points and known ego motion characteristics of the host vehicle from the first time to the second time; compare the synthentic image frame to the second image frame; determine movement information associated with at least one object in the environment of the host vehicle based on the comparison of the synthentic image frame to the second image frame; generate a navigational action for the host vehicle based on the determined movement information; and cause at least one component associated with the host vehicle to implement the navigational action; wherein generation of the synthentic image frame includes dividing the synthentic image frame into a plurality of tiles, and for each of the plurality of tiles, determining at least one corresponding bounding box in the first image and populating pixels within each of the plurality of tiles based on pixels included in at least one corresponding bounding box.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06T 15/00 - 3D [Three Dimensional] image rendering
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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
79.
APPARATUS, SYSTEM, AND METHOD OF DETERMINING A PREDICTED BEHAVIOR DETECTION BASED ON POINT CLOUD INFORMATION
For example, a processor may be configured to process Point Cloud (PC) information including velocity information corresponding to a plurality of points. For example, velocity information corresponding to a point of the plurality of points may include a velocity value corresponding to the point. For example, the processor may be configured to identify a relative movement between a first element of a detected target and a second element of the detected target based on a first plurality of velocity values and a second plurality of velocity values. For example, the first plurality of velocity values may correspond to a plurality of first points corresponding to the first element, and the second plurality of velocity values may correspond to a plurality of second points corresponding to the second element. For example, the processor may determine a predicted behavior detection corresponding to the detected target, for example, based on the relative movement.
For example, polarization-control circuitry may be configured to control a polarization for a communicated signal according to a polarization setting. The polarization-control circuitry may include a first Radio Frequency (RF) path configured to communicate a first RF signal corresponding to the communicated signal via a first antenna port according to a first polarization; a second RF path configured to communicate a second RF signal corresponding to the communicated signal via a second antenna port according to a second polarization; phase-offsetting circuitry including at least one phase shifter in at least one path of the first RF path or the second RF path, the phase-offsetting circuitry configurable to apply a phase offset between the first RF signal in the first RF path and the second RF signal in the second RF path; and a controller to configure the phase-offsetting circuitry to apply the phase offset based on the polarization setting.
A navigational system for use in assisting a host vehicle relative to a road segment, based on real-world scenarios. The system includes a processor, comprising circuitry and memory. On board the host vehicle a camera captures images of the road segment and surrounding vehicle environment. The captured images are used to create graph representations showing the semantic links between subjects represented in the images. The GNN-based system uses nodes to signify objects and the edges to signify relationships from the captured images. The GNN output predicts the likelihood of a predetermined event type occurring. The host vehicle makes a navigational response relative to the predicted likelihood of an event occurring. The result is an autonomous and advanced driver assistance system that accurately predicts the actions of pedestrians, vehicles, and other road entities to ensure the safest host vehicle navigation.
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
B60W 30/095 - Predicting travel path or likelihood of collision
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/40 - ScenesScene-specific elements in video content
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
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
A system for navigating a host vehicle relative to a road segment includes: at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive a captured image acquired by a camera onboard the host vehicle; generate a representation in embedding space of at least a portion of the captured image; determine whether the representation in embedding space of the at least a portion of the captured image falls outside of a predetermined embedding space region, wherein the predetermined embedding space region is defined as a non-anomalous embedding space region; determine a navigational action for the host vehicle based on a determination that the representation in embedding space of the at least a portion of the captured image falls outside of the predetermined embedding space region; and cause at least one system associated with the host vehicle to implement the navigational action.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
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
83.
GRAPHICAL NEURAL NETWORK IN ALIGNMENT AND ROAD FEATURE GENERATOR
The present disclosure generally relates to vehicle navigation and provide systems and methods for generating a map for use in navigating a host vehicle relative to a road segment, and systems and methods for navigating a host vehicle relative to a road segment.
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
The present disclosure relates generally to vehicle navigation and provides a method for navigating a host vehicle relative to a road segment may comprise receiving a captured image representative of at least a portion of the road segment; receiving a planned drivable path representation based on one or more map sections associated with the road segment, wherein the drivable path is determined for navigating the host vehicle.
The embodiments of the present disclosure relate to a system for updating a map for use in navigating a host vehicle relative to a road segment. The system may comprise a processor configured to receive drive information from a vehicle, provide a representation of the drive information to a trained neural network configured to provide an output that includes identification of an update to make to a road topography feature representation stored in the map; generate an updated road topography feature representation in response to the identification of the update to make to the road topography feature representation; and distribute the updated map to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the updated road topography feature representation.
A system for generating map information for use in navigating a host vehicle relative to a road segment may include at least one processor comprising circuitry and a memory. The at least one processor receives drive information from each of a plurality of vehicles that traversed a road segment. The drive information may include indicators representative of road topography features associated with the road segment. The indicators may be aggregated, and an image representation of road topography of the road segment based on the aggregated indicators may be generated. The image representation may be provided as input to at least one trained model configured to generate an output including at least one target trajectory for the road segment. The at least one target trajectory may be stored in a map. The map may be provided to at least one host vehicle navigation system for use in navigating the host vehicle.
In one implementation, a method for navigating a host vehicle relative to a road segment includes receiving a captured image representative of at least a portion of the road segment; receiving a planned drivable path representation based on one or more map sections associated with the road segment, wherein the drivable path is determined for navigating the host vehicle; generating an overlaid representation of the captured image with the planned drivable path; providing the overlaid representation to a trained network, wherein the trained network is configured to receive the overlaid representation as input and provide, based on the combination of the captured image and the planned drivable path, an output that includes an indication of whether the host vehicle drivable path represents a valid path along the road segment; and causing a navigation system of the host vehicle to determine a navigational change for the host vehicle based on the indication of whether the host vehicle drivable path represents a valid path along the road segment.
A system for training a student neural network using a trained supervisory neural network. The system includes at least one processor comprising circuitry and a memory. The memory includes instructions that when executed by the circuitry cause the at least one processor to: receive an image including a representation of a feature of interest, provide the image as input to the trained supervisory neural network, provide the image as input to the student neural network, receive a first output from the trained supervisory neural network indicative of at least one characteristic of the feature of interest, receive a second output from the student neural network indicative of the at least one characteristic of the feature of interest, compare the first output to the second output, and based on a detected difference between the first output and the second output, automatically update at least one aspect of the student neural network.
In one implementation, a system for generating a map for use in navigating a host vehicle relative to a road segment includes at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive drive information from each of a plurality of harvesting vehicles that traversed the road segment, wherein the drive information received from each of the plurality of harvesting vehicles includes at least one location indicator associated with an actual trajectory traveled by the harvesting vehicle, as the harvesting vehicle traversed the road segment; provide the drive information received from each of the plurality of harvesting vehicles to a trained model, wherein the trained model is configured to receive the drive information as input and output normalized drive information for each of the plurality of harvesting vehicles, wherein the normalized drive information includes the at least one location indicator aligned relative to a predetermined reference location; aggregate the normalized drive information provided for each of the plurality of harvesting vehicles to determine one or more target drivable paths through the road segment; store in the map the one or more target drivable paths; and distribute the map data to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the one more mapped target drivable paths.
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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
The embodiments of the present disclosure relate to a system for updating a map for use in navigating a host vehicle relative to a road segment. The system may comprise a processor configured to receive drive information from a vehicle, provide a representation of the drive information to a trained neural network configured to provide an output that includes identification of an update to make to a road topography feature representation stored in the map; generate an updated road topography feature representation in response to the identification of the update to make to the road topography feature representation; and distribute the updated map to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the updated road topography feature representation.
For example, a current-steering Digital to Analog Converter (DAC) may be configured to convert a digital signal into an analog signal. For example, the current- steering DAC may include a thermometer-decoded current-steering DAC including a two-dimensional (2D) array of current-steering cells. For example, the 2D array may include a plurality of first-dimension subarrays and a plurality of second-dimension subarrays. For example, a current-steering cell of the 2D array may be switchable between a first steering state to steer a current of the current-steering cell to a first current path, a second steering state to steer the current of the current-steering cell to a second current path, and a third steering state to steer the current of the current-steering cell to a switch path of a plurality of switch paths corresponding to the plurality of second-dimension subarrays.
H03M 1/68 - Digital/analogue converters with conversions of different sensitivity, i.e. one conversion relating to the more significant digital bits and another conversion to the less significant bits
Techniques are disclosed for performing residual image compression techniques used in conjunction with image and/or video predictors. The techniques utilize a compression scheme that implements a noise model to estimate noise values of pixels in an originally acquired image. These noise value estimates are then used to perform residual image compression more efficiently by performing a non-uniform reduction in resolution of the residual image. The resolution reduction includes dropping least significant bits (LSBs) used to encode each pixel on a pixel-by-pixel basis based upon the noise value estimates of the originally acquired image.
For example, an imaging-device cleaning system may include a blower configured to provide an airflow to be applied onto a surface of an imaging device. For example, the imaging-device cleaning system may include a sprinkler configured to sprinkle a liquid onto the surface of the imaging device. For example, the imaging-device cleaning system may include a controller configured to control activation and deactivation of the blower and the sprinkler. For example, the controller may be configured to control activation of at least one of the blower or the sprinkler, for example, based on identification of a predefined blockage scenario in which at least part of a field of view of the imaging device is to be blocked by a substance on the surface.
Techniques are disclosed to enable an adaptive vehicle advanced driver assistance system (ADAS) unit, which is also referred to as a “smart” ADAS. The smart ADAS unit transmits vehicle ADAS messages, which are received and aggregated by a remote computing system. The remote computing system may optionally include, in the aggregated data set, supplemental data such as weather information, traffic data, etc. The remote computing system identifies, from the aggregated data set, ADAS alert events and their corresponding locations, and uses predetermined rule sets to identify potential ADAS alert configuration settings that may be updated by vehicles in a service range. The ADAS configuration messages provide each vehicle with instructions regarding if, when, and how the ADAS configuration settings should be adjusted, which may comprise the adjustment of ADAS alert sensitivity settings to dynamically adjust the manner in which ADAS alerts are issued per each ADAS alert event.
Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.
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
B60T 7/12 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger
B60T 7/22 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle
B60T 8/32 - Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force responsive to a speed condition, e.g. acceleration or deceleration
B60W 10/18 - Conjoint control of vehicle sub-units of different type or different function including control of braking systems
B60W 10/20 - Conjoint control of vehicle sub-units of different type or different function including control of steering systems
B60W 30/00 - Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
B60W 30/08 - Predicting or avoiding probable or impending collision
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
B60W 30/095 - Predicting travel path or likelihood of collision
B60W 30/165 - Control of distance between vehicles, e.g. keeping a distance to preceding vehicle automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G05D 1/249 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons from positioning sensors located off-board the vehicle, e.g. from cameras
G05D 1/81 - Handing over between on-board automatic and on-board manual control
H04N 23/90 - Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
B62D 6/00 - Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations
G05D 1/243 - Means capturing signals occurring naturally from the environment, e.g. ambient optical, acoustic, gravitational or magnetic signals
G05D 1/248 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons generated by satellites, e.g. GPS
G05D 1/617 - Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards
G08G 1/00 - Traffic control systems for road vehicles
G08G 1/09 - Arrangements for giving variable traffic instructions
H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle includes at least one processor programmed to: receive, from a camera of the host vehicle, one or more images captured from an environment of the host vehicle; analyze the one or more images to detect an indicator of an intersection; determine, based on output received from at least one sensor of the host vehicle, a stopping location of the host vehicle relative to the detected intersection; analyze the one or more images to determine an indicator of whether one or more other vehicles are in front of the host vehicle; and send the stopping location of the host vehicle and the indicator of whether one or more other vehicles are in front of the host vehicle to a server for use in updating a road navigation model.
G01C 21/00 - NavigationNavigational instruments not provided for in groups
H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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
A system for generating a map for use in navigating a host vehicle relative to a road segment may include at least one processor programmed to: receive drive information from each of a plurality of harvesting vehicles that traversed the road segment, wherein the drive information received from each of the plurality of harvesting vehicles includes at least one indicator of an actual trajectory traveled by the harvesting vehicle, as the harvesting vehicle traversed the road segment, and wherein the drive information received from each of the plurality of harvesting vehicles also includes lane type associated with at least one lane of the road segment; aggregate the drive information to determine a host vehicle drivable path for one or more lanes associated with the road segment; store in the map the host vehicle drivable path for the one or more lanes associated with the road segment; determine, based on the lane type information and for each determined host vehicle drivable path, a lane type indicator; store in the map the lane type indicator determined for each host vehicle drivable path; and distribute the map data to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to at least one mapped host vehicle drivable path and further based on a lane type indicator associated with the at least one mapped host vehicle drivable path.
Techniques are disclosed for improving the manner in which multiple HDR images are generated having different exposure times. The technicism allow for the use of a single imaging sensor to generate HDR images with a reduced latency required to do so. The result is that both long and high exposure HDR images may be generated in a much faster time frame than that required for traditional HDR sensors, which may be a time offset of the shorter exposure time of the two HDR images. This allows for the images to capture more similar scenes given the proximity in time in which both are generated, allowing for more accurate vehicle-based functions to be implemented that rely upon such HDR images, such as object classification.
H04N 25/585 - Control of the dynamic range involving two or more exposures acquired simultaneously with pixels having different sensitivities within the sensor, e.g. fast or slow pixels or pixels having different sizes
H04N 25/42 - Extracting pixel data from image sensors by controlling scanning circuits, e.g. by modifying the number of pixels sampled or to be sampled by switching between different modes of operation using different resolutions or aspect ratios, e.g. switching between interlaced and non-interlaced mode
H04N 25/78 - Readout circuits for addressed sensors, e.g. output amplifiers or A/D converters
Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may be programmed to receive, from a camera, a captured image representative of features in an environment of the vehicle. The processor may generate a warped image based on the received captured image, which may simulate a view of the features in the environment of the vehicle from a simulated viewpoint elevated relative to an actual position of the camera. The processor may further identify a road feature represented in the warped image, which may be transformed in one or more respects relative to a representation of the road feature in the captured image. The processor may then determine a navigational action for the vehicle based on the identified feature represented in the warped image and cause at least one actuator system of the vehicle to implement the determined navigational action.
Systems and methods for analyzing wheel spin and rotation for vehicle navigation. In one implementation, a system includes a processor programmed to receive a plurality of images acquired by an image capture device associated with the host vehicle, wherein the plurality of images are representative of an environment of the host vehicle; analyze at least two of the plurality of images to identify a representation of at least one wheel in the at least two of the plurality of images; analyze the at least two of the plurality of images to determine whether the at least one wheel is spinning; and cause the host vehicle to initiate at least one navigational action in response to the determination of whether the at least one wheel is spinning.
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G06T 7/55 - Depth or shape recovery from multiple images
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
