A remote computer system may receive data associated with an autonomous vehicle traversing an environment along a route in accordance with a planned trajectory and data associated with an event within the environment and cause a display to display a representation of the autonomous vehicle. A request to generate an intermittent stopping action message comprising one or more of a position or orientation, a period of time, or a condition is received by the remote computer system and transmitted to the autonomous vehicle, wherein the autonomous vehicle is configured to move to the one or more of position or orientation for the period of time or until the condition is met, such that the vehicle moves to or is at the one or more of position or orientation prior to the event occurring.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60Q 1/52 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking for indicating emergencies
B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
A vehicle seat comprises a controllable deformable material and control apparatus is provided for controlling the state of a controllable deformable material. The control apparatus is configured to generate a signal to cause a control element in proximity to the controllable deformable material to change the characteristic of the controllable deformable material and thereby change the state of the controllable deformable material. The controllable deformable material is controllable to change state from a first state to at least a second state and is controllable to change state from at least the second state to the first state, the controllable deformable material being more deformable per unit force in the second state than in the first state.
Techniques for segmenting and classifying a representation of aggregated sensor data from a scene are discussed herein. Sensor data may be collected during multiple traversals of a same scene, and the sensor data may be filtered to remove portions of the sensor data not relevant to road network maps. In some examples, the filtered data may be aggregated and represented in voxels of a three-dimensional voxel space, from which an image representing a top-down view of the scene may be generated, though other views are also contemplated. Operations may include segmenting and/or classifying the image e.g., by a trained machine-learned model, to associate class labels indicative of map elements (e.g., driving lane, stop line, turn lane, and the like) with segments identified in the image. Additionally, techniques may create or update road network maps based on segmented and semantically labeled image(s) of various portions of an environment.
G01S 7/48 - RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - Details of systems according to groups , , of systems according to group
G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
Techniques relating to performance-based metrics for evaluating system quality are described. In an example, sensor data associated with an environment within which a vehicle is positioned is received. A performance metric associated with a trajectory, indicative of a performed behavior of the vehicle, can be determined and a correctness metric can be determined based at least in part on the performance metric. The correctness metric can represent a correctness of the performed behavior. Modification(s) to component(s) and/or system(s), or portions thereof, of the vehicle can be affected based at least in part on the correctness metric.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
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 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
An autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a data structure generated based at least in part on sensor data that may indicate occupied space in an environment surrounding an autonomous vehicle. The guidance system may receive a grid and generate a grid associated with the grid and the data structure. The guidance system may additionally or alternatively sub-sample the grid (latterly and/or longitudinally) dynamically based at least in part on characteristics determined from the data structure. The guidance system may identify a path based at least in part on a set of precomputed motion primitives, costs associated therewith, and/or a heuristic cost plot that indicates a cheapest cost to move from one pose to another.
This disclosure describes techniques for detecting multipath radar returns and modifying radar data. A vehicle may use radar devices to receive radar data while traversing within an environment. The vehicle may process the radar data using a virtual array based on an arraignment of the antennae within an aperture of the radar device. Using the virtual array, the vehicle may determine an elevated noise level that may be indicative of a multipath radar return. Based on the elevated noise level, the vehicle may determine a second virtual array associated with multipath radar returns, and may process the radar data using the second virtual array. Based on determining that the noise level associated with the second virtual array is lower than the initial noise level, the vehicle may determine that the radar data includes a multipath radar return, and may modify the radar data to correct or mitigate the error caused by the multipath return.
G01S 13/931 - Radar or analogous systems, specially adapted for specific applications for anti-collision purposes of land vehicles
G01S 13/00 - Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
G01S 13/87 - Combinations of radar systems, e.g. primary radar and secondary radar
Driving simulations may be generated based on driving log data and used to validate autonomous vehicle safety systems. A driving simulation system may receive driving log data from autonomous vehicles and determine events which caused a vehicle to diverge from a planned trajectory. Driving simulations may be generated based on the driving log data, and executed using a simulated vehicle that follows the initial planned trajectory of the autonomous vehicle. The driving simulations may be analyzed to detect potential collisions and near misses, and to determine success conditions for the behaviors of vehicle safety systems. Driving simulations and associated success conditions may be aggregated and used in validation suites for vehicle controllers and/or vehicle safety systems, thereby providing more comprehensive and robust autonomous vehicle validation based on real-world driving scenarios.
B60W 50/035 - Bringing the control units into a predefined state, e.g. giving priority to particular actuators
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
G07C 5/00 - Registering or indicating the working of vehicles
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
Techniques are discussed for modifying map elements associated with map data. Map data can include three-dimensional data (e.g., LIDAR data) representing an environment, while map elements can be associated with the map data to identify locations and semantic information associated with an environment, such as regions that correspond to driving lanes or crosswalks. A trajectory associated with the map data can be updated, such as when aligning one or more trajectories in response to a loop closure, updated calibration, etc. The transformation between a trajectory and an updated trajectory can be applied to map elements to warp the map elements so that they correspond to the updated map data, thereby providing automatic and accurate techniques for updating map elements associated with map data.
Systems and techniques for determining a sideslip vector for a vehicle that may have a direction that is different from that of a heading vector for the vehicle. The sideslip vector in a current vehicle state and sideslip vectors in predicted vehicles states may be used to determine paths for a vehicle through an environment and trajectories for controlling the vehicle through the environment. The sideslip vector may be based on a vehicle position that is the center point of the wheelbase of the vehicle and may include lateral velocity, facilitating the control of four-wheel steered vehicle while maintaining the ability to control two-wheel steered vehicles.
B62D 7/15 - Steering linkage; Stub axles or their mountings for individually-pivoted wheels, e.g. on king-pins the pivotal axes being situated in more than one plane transverse to the longitudinal centre line of the vehicle, e.g. all-wheel steering characterised by means varying the ratio between the steering angles of the steered wheels
Vehicles may be associated with a variety of different types of events, including events associated with vehicle operations and/or events associated with vehicle passengers. The present disclosure is related to, when an event is detected, exchanging information with a vehicle passenger, such as via the passenger's mobile device and/or wearable device. In some instances, an event may be detected, and examples of the present disclosure may provide an application notification presenting various information. The notification may, in some examples, be configured to help the passenger locate the passenger device, to alert the passenger to the event, to provide instructions, and/or to provide a control interface for controlling a vehicle operation. In some examples, the notification may supersede operations of the passenger device, such as by being presented even if the device is in a locked state or has an unrelated application open and in the foreground.
H04W 76/50 - Connection management for emergency connections
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G07C 5/00 - Registering or indicating the working of vehicles
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
H04W 4/02 - Services making use of location information
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
H04W 4/90 - Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
13.
LATENT VARIABLE DETERMINATION BY A DIFFUSION MODEL
Techniques for predicting an object trajectory or scene information are described herein. For example, the techniques may include inputting latent variable data into a machine learned model. The machine learned model may output an object trajectory (e.g., position data, velocity data, acceleration data, etc.) for one or more objects in the environment based on the latent variable data. The object trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.
Techniques for predicting an object trajectory or scene information are described herein. For example, the techniques may include inputting tokens representing discrete behavior into a machine learned model. The machine learned model may output a sequence of tokens that is usable by another machine learned model to generate an object trajectory (e.g., position data, velocity data, acceleration data, etc.) for one or more objects in the environment. The object trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
Techniques for training a codebook usable by a machine learned model to predict an object trajectory or scene data are described herein. For example, the techniques may include generating tokens representing discrete object behavior into a machine learned model that outputs a sequence of tokens that is usable by another machine learned model to generate the object trajectory (e.g., position data, velocity data, acceleration data, etc.) or the scene data associated with the environment. The object trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
B60W 40/10 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to vehicle motion
16.
Adaptive vehicle seat back stiffness restraint system
A seat for a vehicle may include a seatback including a variable resistance support disposed within a volume of the seatback. The variable resistance support may further include one or more configurable arrays of resistive elements including collapsible structures and/or inflatable bladders that may be configured to provide energy dissipation and/or absorption during a collision event. One or more array subsets may be configured to be independently controllable and configured to provide adjustable support at a region of the seatback, the adjustable support corresponding to a mass of the occupant. Moreover, when at least a portion of the back of an occupant pushes against a front surface of the seatback due to the collision event, the arrays of resistive elements may be configured to compress and/or collapse, at least partially, to absorb an energy applied from an occupant's back associated with the collision event.
B60N 2/02 - Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
B60N 2/42 - Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles for particular purposes or particular vehicles the seat constructed to protect the occupant from the effect of abnormal g-forces, e.g. crash or safety seats
B60N 2/90 - Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles - Details or parts not otherwise provided for
An occupant constraint system may include a belt marker on a seat belt in a passenger compartment of the vehicle for restraining a passenger during a collision. The occupant constraint system may include a camera system for producing marker position data that is indicative of a position of the marker, and a physical characteristic estimator that is uses the marker position data to produce a physical characteristic estimate of a physical characteristic estimate of the passenger. The occupant constraint system may include a passenger related system having a passenger function and the passenger related system may control the passenger function based at least in part on the physical characteristic estimate.
B60R 21/01 - Electrical circuits for triggering safety arrangements in case of vehicle accidents or impending vehicle accidents
B60R 21/015 - Electrical circuits for triggering safety arrangements in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, e.g. for disabling triggering
B60R 22/48 - Control systems, alarms, or interlock systems, for the correct application of the belt or harness
Techniques for validating operation of a perception system that is configured to detect objects in an environment of a vehicle are described herein. The techniques may include receiving log data representing a real scenario in which the vehicle was traversing an environment. Based at least in part on the log data, an error may be identified that is associated with an output received from the perception system while the vehicle was traversing the environment. In some examples, a determination may be made that a magnitude of the error violates a perception system requirement, the requirement established based on a determination that the magnitude of the error would contribute to an adverse event in an alternative scenario. Based on the magnitude of the error contributing to the adverse event, data associated with the error may be output for use in updating the perception system to at least meet the requirement.
Validating a component of an autonomous vehicle may comprise determining, via simulation, a likelihood that operation of the component will result in an adverse event. Such simulations may be based on log data developed from real world driving events to, for example, accurately model a likelihood that a scenario will occur during real-world driving. Because adverse events may be exceedingly rare, the techniques may include modifying a probability distribution associated the likelihood that a scenario is simulated, determining a metric associated with an adverse event (e.g., a likelihood that operating the vehicle or updating a component thereof will result in an adverse event), and applying a correction to the metric based on the modification to the probability distribution.
Techniques for verification of interchangeable connectors are disclosed which include applying one or more identification tags to connectors. The identification tags have identifiers which may be associated with one another for connectors that correspond. The identifiers may be read from the identification tags when the connectors are connected to determine if the connected connectors are associated.
H01R 13/66 - Structural association with built-in electrical component
G06K 7/10 - Methods or arrangements for sensing record carriers by corpuscular radiation
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
An active suspension control system for a vehicle includes a mathematical model based on a modal expansion of the vehicle. Model parameters of the vehicle can be extracted from the modal expansion using sensor data generated on the vehicle, e.g., on demand and/or in real time. The model parameters and the modal expansion can be used to determine a vehicle state, predict future vehicle states, and control aspects of an active suspension system based on the predicted future vehicle states. The model parameters may also be used to update the mathematical model, e.g., to account for component wear over time, and/or to detect anomalies or defects in the active suspension system.
B60G 17/0165 - Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or s the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input to an external condition, e.g. rough road surface, side wind
B60G 17/018 - Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or s the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
22.
Pedestrian protection system for sensor pod camera impact
A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may include a housing and extend from a portion of a body of a vehicle. The sensor pod housing may have energy absorbing structures configured to absorb and dissipate energy during an impact in order to protect a pedestrian.
H04N 23/52 - Elements optimising image sensor operation, e.g. for electromagnetic interference [EMI] protection or temperature control by heat transfer or cooling elements
H04N 23/54 - Mounting of pick-up tubes, electronic image sensors, deviation or focusing coils
H04N 23/55 - Optical parts specially adapted for electronic image sensors; Mounting thereof
H04N 23/57 - Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices
23.
Display screen or portion thereof having a graphical user interface
Techniques for determining a probability that a first sensor is miscalibrated with respect a second sensor are discussed herein. For example, a computing device may receive calibrated extrinsics of a camera to a lidar, determine a plurality of sets of perturbed extrinsics based on the calibrated extrinsics, determine respective costs for perturbed extrinsics of the plurality of sets of perturbed extrinsics based on image data captured by the camera, the plurality of sets of perturbed extrinsics, and lidar data captured by the lidar, and determine a local maxima score for the calibrated extrinsics based at least in part on the respective costs for the perturbed extrinsics of the plurality of sets of perturbed extrinsics and a cost of the calibrated extrinsics. The computing device may then determine a probability that the camera is miscalibrated based on a Bayes probability and the local maxima score.
Techniques for validating or determining trajectories for a vehicle are discussed herein. A trajectory management component can receive status and/or error data from other safety system components and select or otherwise determine safe and valid vehicle trajectories. A perception component of a safety system can validate a trajectory upon which the trajectory management component can wait for selecting a vehicle trajectory, validate trajectories stored in a queue, and/or utilize kinematics for validation of trajectories. A filter component of the safety system can filter out objects based on trajectories stored in a queue. A collision detection component of the safety system can determine the collision states based on trajectories stored in a queue or determine a collision state upon which the trajectory management component can wait for selecting or otherwise determining a vehicle trajectory.
A modified Kalman filter may include one or more neural networks to augment or replace components of the Kalman filter in such a way that the human interpretability of the filter's inner functions is preserved. The neural networks may include a neural network to account for bias in measurement data, a neural network to account for unknown controls in predicting a state of an object, a neural network ensemble that is trained differently based on different sensor data, a neural network for determining the Kalman gain, and/or a set of Kalman filters including various neural networks that determine independent estimated states, which may be fused using Bayesian fusion to determine a final estimated state.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06N 3/04 - Architecture, e.g. interconnection topology
Techniques are discussed herein for determining optimal driving trajectories for autonomous vehicles in complex multi-agent driving environments. A baseline trajectory may be perturbed and parameterized into a vector of vehicle states associated with different segments (or portions) of the trajectory. Such a vector may be modified to ensure the resultant perturbed trajectory is kino-dynamically feasible. The vectorized perturbed trajectory may be input, including a representation of the current driving environment and additional agents, into a prediction model trained to output a predicted future driving scene. The predicted future driving scene, including predicted future states for the vehicle and predicted trajectories for the additional agents in the environment, may be evaluated to determine costs associated with each perturbed trajectory. Based on the determined costs, the optimization algorithm may determine subsequent perturbations and/or the optimal trajectory for controlling the vehicle in the driving environment.
Techniques for determining a vehicle trajectory that causes a vehicle to navigate in an environment relative to one or more objects are described herein. For example, the techniques may include a computing device determining a decision tree having nodes to represent different object intents and/or nodes to represent vehicle actions at a future time. A tree search algorithm can search the decision tree to evaluate potential interactions between the vehicle and the one or more objects over a time period, and output a vehicle trajectory for the vehicle. The vehicle trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.
Techniques for representing a scene or map based on statistical data of captured environmental data are discussed herein. In some cases, the data (such as covariance data, mean data, or the like) may be stored as a multi-resolution voxel space that includes a plurality of semantic layers. In some instances, individual semantic layers may include multiple voxel grids having differing resolutions. Multiple multi-resolution voxel spaces may be merged or aligned to generate combined scenes based on detected voxel covariances at one or more resolutions.
Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To validate safe operation of a system, a simulation may be executed including determining a relative location of a simulated object within the simulation with respect to a location of a simulated vehicle, determining, based on the relative location of the simulated object, an adjusted location of the simulated object within the simulation, controlling, by the autonomous vehicle controller and based on the relative location of the simulated object, the simulated vehicle to follow a trajectory within the simulation, and performing a collision check between the simulated vehicle and the simulated object at the adjusted location. The safety metric associated with the autonomous vehicle controller may then be determined based at least in part an outcome of the collision check.
G09B 9/048 - Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles a model being viewed and manoeuvred from a remote point
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
G05D 1/02 - Control of position or course in two dimensions
31.
MAP ANNOTATION MODIFICATION USING SPARSE POSE GRAPH NODE UPDATES
Relocating and/or re-sizing map elements using an updated pose graph without introducing abnormalities to the map data may comprise determining a transformation between a source node of a first pose graph and a target node of a second pose graph and determining a modification to a map element based at least in part on the transformation. The techniques may include determining a stress on the map based at least in part on one or more modifications to map elements and determining if the stress meets or exceeds a threshold. In instances where the stress meets or exceeds a threshold, a modification may be altered, reversed, and/or indicated in a notification transmitted to a user interface.
Techniques for receiving and processing sensor data captured by a fleet of vehicle are discussed herein. In some examples, a fleet dashcam system can receive sensor data captured by electronic devices on a fleet of vehicles and can use that data to detect collision and near-collision events. The data of the collision or near-collision event can be used to determine a simulation scenario and a response of an autonomous vehicle control to the simulation scenario and/or it can be used to create a collision heat map to aid in operation of an autonomous vehicle.
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
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 pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
G08G 1/00 - Traffic control systems for road vehicles
A system for disinfecting an autonomous vehicle includes a light assembly having LEDs configured to emit UVC light into a passenger compartment of the autonomous vehicle. The light assembly includes a heat sink to dissipate heat generated by the LEDs. In examples, the heat sink is disposed in the path of forced air associated with a climate control system of the vehicle. The climate control system may be controlled based on a temperature proximate the LEDs and/or the heat sink. In some examples, the light assembly is integrated into the ceiling of the vehicle and can further include visible light emitters and/or other features.
Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. Steering angle constraints may be used to limit or prevent saturation of steering by only one set of wheels as well as to reduce sideslip. The steering angle constraints are vehicle independent and applicable across a fleet of different vehicles based on vehicle speed. For instance, the steering angle constraints establish angle limits that dynamically adjust based on vehicle speed to ensure vehicle velocity and acceleration remain within predefined limits as established by autonomous vehicle system design.
B62D 7/15 - Steering linkage; Stub axles or their mountings for individually-pivoted wheels, e.g. on king-pins the pivotal axes being situated in more than one plane transverse to the longitudinal centre line of the vehicle, e.g. all-wheel steering characterised by means varying the ratio between the steering angles of the steered wheels
B62D 6/00 - Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
Simulating output of a perception system may comprise receiving scenario data indicating a position associated with a simulated sensor and a position and/or identifier of an object, and instantiating a three-dimensional representation of an environment and the object (i.e., a simulated environment). The system may generate depth data indicating distances and/or positions of surfaces in the simulated environment relative to the simulated sensor position and determine a three-dimensional region of interest based at least in part on the depth data associated with at least a portion of the object. In some examples, the three-dimensional region of interest may be smaller than a size of the object, due to an occlusion by topology of the simulated environment and/or another object in the simulated environment.
G06F 30/20 - Design optimisation, verification or simulation
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06F 30/15 - Vehicle, aircraft or watercraft design
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 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
Techniques are discussed herein for detecting and measuring faults within vehicle drive systems. In various implementations, actuator feedback monitors and/or vehicle performance monitors are used individually or in combination to determine faults within acceleration systems, braking systems, and/or steering systems of a vehicle. Different monitors may use different algorithms, models, and input data from the vehicle during operation, to compare a predicted drive system output to a corresponding measured output. Additionally, monitors may include associated sets of disable conditions used to distinguish drive system faults from other driving conditions. The outputs from actuator feedback monitors, vehicle performance monitors, and disable conditions may be analyzed to detect drive system faults and control the vehicle to address the faults and control the safe operation of the vehicle.
B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
A lens assembly for systems such as imaging devices. The lens assembly may include at least five lens elements, where one or more of the lens elements includes positive optical power and one or more of the lenses includes negative optical power. The lens assembly may further include an aperture stop located between the lens elements. In some instances, the lens assembly provides a horizontal field of view that is at least 80 degrees while still including a total track length that is less than or equal to 50 millimeters and a diameter that is less than or equal to 13 millimeters. Additionally, in some instances, the lens assembly may cause all rays which impinge the sensor to be less than or equal to approximately 10 degrees.
Techniques for generating simulations to evaluate an update to a controller. The controller may be configured to control one or more functionalities of an autonomous and/or a semi-autonomous vehicle. A simulation computing system may receive a request to evaluate a first controller. The simulation computing system may generate a simulation based on data associated with a previous operation of the vehicle in an environment, the previous operation being controlled by a second controller (e.g., standard for evaluation, control version, etc.). The simulation computing device may cause the first controller to control a simulated vehicle in the simulation and may determine whether to validate the update to the controller based on a difference between first metrics associated with a control of the simulated vehicle by the first controller and second metrics associated with a control of the vehicle by the second controller.
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
B60W 50/04 - Monitoring the functioning of the control system
B60W 50/06 - Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
G06F 30/20 - Design optimisation, verification or simulation
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
42.
Suspension system with rotation inducing component
A suspension system includes a rotation inducing member for causing a cylinder to rotate relative to piston rod in response to an axially-applied force. In examples, the rotation inducing member may be embodied as a bushing structure. In other examples, the cylinder and/or the piston rod may include the rotation inducing member. The inducement of rotational motion may help overcome stiction or similar frictional forces between the piston rod and the cylinder.
B60G 15/06 - Resilient suspensions characterised by arrangement, location, or type of combined spring and vibration- damper, e.g. telescopic type having mechanical spring and fluid damper
B60G 15/04 - Resilient suspensions characterised by arrangement, location, or type of combined spring and vibration- damper, e.g. telescopic type having mechanical spring and mechanical damper
A door interface system for a vehicle door includes a sensor and a visual indicator. The sensor may be a proximity sensor and may be positioned proximate to the visual indicator such that it will detect an object proximate to the proximity sensor. The visual indicator may convey the position of the sensor and/or a status of the vehicle door. The door interface system is configured to control the vehicle door based at least in part on detecting an object proximate the visual indicator.
B60R 25/01 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles operating on vehicle systems or fittings, e.g. on doors, seats or windscreens
B60R 25/30 - Detection related to theft or to other events relevant to anti-theft systems
B60R 25/34 - Detection related to theft or to other events relevant to anti-theft systems of conditions of vehicle components, e.g. of windows, door locks or gear selectors
B60R 25/31 - Detection related to theft or to other events relevant to anti-theft systems of human presence inside or outside the vehicle
B60Q 9/00 - Arrangement or adaptation of signal devices not provided for in one of main groups
B60R 25/10 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
Techniques for analyzing driving scenarios are discussed herein. For example, techniques may include determining a level of exposure associated with scenarios, searching for similar scenarios, and generating new additional scenarios. A driving scenario may be represented as top-down multi-channel data. The top-down multi-channel data may be provided as input to a neural network trained to output a prediction of future events. A multi-dimensional vector representing the scenario can be received as an intermediate output from the neural network and may be stored to represent the scenario. Multi-dimensional vectors representing different scenarios may be stored in a multi-dimensional space, and similar scenarios may be identified by proximity searching of the multi-dimensional space.
Techniques for integrating sensor data into a scene or map based on statistical data of captured environmental data are discussed herein. The data may be stored as a multi-resolution voxel space and the techniques may comprise first applying a pre-alignment or localization technique prior to fully integrating the sensor data.
Techniques to use a trained model to determine a yaw of an object are described. For example, a system may implement various techniques to generate multiple representations for an object in an environment. Each representation vary based on the technique and data used. An estimation component may estimate a representation from the multiple representations. The model may be implemented to output a yaw for the object using the multiple representations, the estimated representation, and/or additional information. The output yaw may be used to track an object, generate a trajectory, or otherwise control a vehicle.
Systems and techniques for determining deceleration controls to use in a trajectory for use in stopping a vehicle are described. A deceleration determination system may receive a trajectory from a trajectory determination system and determine, based on various deceleration parameters, the appropriate controls to configure in a longitudinal profile of the trajectory and the suitable implementation points for implementing the controls. The deceleration determination system may determine deceleration data for various types of trajectories that a vehicle computing system may select from for operating the vehicle based on current conditions.
Techniques described are related to determining when a discrepancy between data of multiple sensors (e.g., IMUs) might be attributable to a sensor error, as opposed to operating conditions, such as sensor bias or noise. For example, the sensor data is passed through one or more filters (e.g., bandpass filter) that model the bias or noise, and the filtered data may then be compared for consistency. In some examples, consistency may be based on residuals or some other metric describing discrepancy among the filtered sensor data.
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
G01C 21/16 - Navigation; Navigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
50.
Vehicle ride dynamics active comfort tuning system
A vehicle may include an active ride comfort tuning system that reactively and/or proactively alters a parameter of a system of the autonomous vehicle to mitigate or avoid interruptions to ride smoothness. For example, the comfort tuning system may alter a parameter of a drive system, suspension, and/or a trajectory cost function. The comfort tuning system may alter the parameter based at least in part on detecting and/or receiving a comfort indication, determined based on sensor data, user input, or the like.
A system may schedule a vehicle for accommodating preferences of occupants within the vehicle. In some instances, an identity of the occupant may be determined and using the identity, the preference of the occupant may be determined. The vehicle may be schedule for the occupant in instances where the vehicle is able to accommodate the preference. The vehicle may be adjusted or setting(s) of the vehicle may be adjusted based on the preference. The vehicle may travel along a route according to the preferences.
G06Q 10/02 - Reservations, e.g. for tickets, services or events
B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
A soft-constraint technique for refining an initial pose graph may eschew using a hard constraint that identifies different sensor data and/or poses as necessarily being associated with a same portion of an environment. Instead, the soft-constraint technique may employ a loss function with a convergence basin that may be defined based at least in part on an object classification that strongly penalizes candidate locations within a distance associated with the convergence basin. These candidate locations may be based at least in part on one or more object detections associated (1:1) with one or more poses of the initial pose graph. This may result in one or more candidate locations that do not merge with other candidate locations, giving the pose graph optimization the permissiveness or softness according to the techniques described herein.
A reward determined as part of a machine learning technique, such as reinforcement learning, may be used to control an adversarial agent in a simulation such that a component for controlling motion of the adversarial agent is trained to reduce the reward. Training the adversarial agent component may be subject to one or more constraints and/or may be balanced against one or more additional goals. Additionally or alternatively, the reward may be used to alter scenario data so that the scenario data reduces the reward, allowing the discovery of difficult scenarios and/or prospective events.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
G07C 5/00 - Registering or indicating the working of vehicles
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
Techniques for determining whether to limit an operation of a vehicle while operating in a manually assisted mode of operation are described herein. A vehicle computing system can monitor components of the vehicle and identify a fault associated with a component. The vehicle computing system can determine whether the fault is associated with a manual operation of the vehicle. Based on a determination that the fault is not associated with the manual operation of the vehicle (e.g., fault associated with an autonomous control component), the vehicle computing system can override the fault and enable continued operation of the vehicle in the manually assisted mode of operation. Based on a determination that the fault is associated with the manual operation of the vehicle, the vehicle computing system can cause the vehicle to cease operating.
Techniques for identifying a constraint to apply to an operation of a vehicle are described herein. A vehicle computing system receives diagnostics and constraints associated with components of the vehicle. The vehicle computing system identifies constraints to apply to vehicular operation based on the received diagnostics and constraints. The vehicle computing system may determine whether a received constraint is valid, based on associated diagnostics. Based on a determination that the constraint is valid, the vehicle computing system may include the constraint in vehicle control considerations. Based on a determination that the constraint is invalid, the vehicle computing system may withhold the constraint from vehicle control considerations.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
Systems and techniques for determining a trajectory for use in controlling a vehicle are described. A trajectory determination system may generate a variety of trajectories for potential use in controlling a vehicle, including a maximum braking trajectory that enables the maximum application of the vehicle's brakes. A vehicle computing system may determine a distance between vehicle and an obstacle and stopping distances for the various trajectories and implement the maximum braking trajectory after determining that the distance to stop for that trajectory is the same as, but not substantially greater than, the distance between the vehicle and the obstacle.
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
B60T 7/22 - Brake-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
B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
A remote operations system receives a request for remote operator assistance and adds the request to a queue of additional requests. The queue may be ordered based on time of receipt, priority, criticality, and the like. The remote operations system determines a remote operator of a set of remote operators to provide a response to the request in the queue based at least in part on one or more of a status of the remote operator (e.g., indicative of availability, whether they are in training, etc.), criteria associated with the remote operator (e.g., skills in responding to various requests, preferences for a geographic area, mission types, etc.), and information associated with the request received from the vehicle (e.g., mission type, sensor data, messages, vehicle status, etc.). If the request is not accepted in a threshold period of time, the request may be rerouted to an additional remote operator.
Techniques for determining that a reference trajectory is free of intersections or potential collisions with objects are discussed herein. Trajectories being generated by a primary computing device of a vehicle can be utilized to select or determine a reference trajectory to be followed by the vehicle. A secondary computing device of the vehicle can identify a current offset from the reference trajectory and utilize the offset with a kinematics model to determine a trajectory that is predicted for the vehicle to drive to return to the reference trajectory. Validation of the reference trajectory may be based on predicted collision data determined using the tracker trajectory. The predicted collision data can be utilized to control the vehicle to follow the reference trajectory or a stop trajectory.
A vehicle seat comprises a controllable deformable material and control apparatus is provided for controlling the state of a controllable deformable material. The control apparatus is configured to generate a signal to cause a control element in proximity to the controllable deformable material to change the characteristic of the controllable deformable material and thereby change the state of the controllable deformable material. The controllable deformable material is controllable to change state from a first state to at least a second state and is controllable to change state from at least the second state to the first state, the controllable deformable material being more deformable per unit force in the second state than in the first state.
B60N 2/42 - Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles for particular purposes or particular vehicles the seat constructed to protect the occupant from the effect of abnormal g-forces, e.g. crash or safety seats
B60N 2/427 - Seats or parts thereof displaced during a crash
Techniques for increasing performance of machine-learned models while conserving computational resources generally required by ensemble machine-learning methods are described herein. The techniques may include determining multiple views of a scene that is to be input into a machine-learned model. In some examples, a scene data input may be rotated by 90, 180, and 270 degrees to generate four scene inputs (e.g., 0-, 90-, 180-, and 270-degree rotated inputs) that can be passed through the machine-learned model and the results per scene can be aggregated to determine a final prediction/decision. Similarly, scene inputs may be shifted, reflected, translated, and/or the like before being input into the machine-learned model. The predictions may be associated with one or more objects in the environment that are represented in the scenes.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include monitoring a fleet of vehicles, at least one of which is configured to autonomously transit from a first geographic region to a second geographic region, detecting data indicating an event associated with the vehicle having a calculated confidence level, receiving data representing a subset of candidate trajectories responsive to detecting the event, which is associated with a planned path for the vehicle, identifying guidance data to select from one or more of the candidate trajectories as a guided trajectory, receiving data representing a selection of a candidate trajectory, and transmitting the selection of the candidate trajectory as of the guided trajectory to the vehicle.
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
G05D 1/02 - Control of position or course in two dimensions
B60Q 1/26 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic
G07C 5/00 - Registering or indicating the working of vehicles
G08G 1/005 - Traffic control systems for road vehicles including pedestrian guidance indicator
G01S 17/875 - Combinations of systems using electromagnetic waves other than radio waves for determining attitude
G01S 15/931 - Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
G08G 1/00 - Traffic control systems for road vehicles
G01S 15/86 - Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
B60Q 1/50 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to output data indicating costs for potential intersection points between the object and the vehicle in the future. The model may employ a control policy and a time-step integrator to determine whether an object may intersect with the vehicle, in which case the techniques may include predicting vehicle actions by the vehicle computing device to control the vehicle.
Techniques for capturing and recording processor events and scheduler data in a production system on a per processing resource basis are discussed herein. In some examples, a process metric collection component may be associated with the scheduler and the processing resource such that the process metric collection component can capture real time data associated with the processes or threads both executed by the processing resource and waiting to execute on the processing resource. The captured data may be used by the system to monitor operations.
Techniques for updating data operations in a perception system are discussed herein. A vehicle may use a perception system to capture data about an environment proximate to the vehicle. The perception system may receive image data, lidar data, and/or radar data to determine information about an object in the environment. As different sensors may be associated with different time periods for capturing and/or processing operations, the techniques include updating object data with data from sensors associated with a shorter time period to generate intermediate object data.
G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
G01S 19/39 - Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
An occupant protection system may comprise an airtight, deformable container having a pressure sensor configured to generate a vehicle collision signal. A side airbag may be configured to expand between a stowed state and a deployed state. A deployment control system may be configured to receive the vehicle collision signal from the pressure sensor, and based upon the vehicle collision signal, cause an inflator to provide a gas to the airbag to thereby cause the side airbag to expand from the stowed state to the deployed state.
A vehicle computing system may identify an obstruction along a route of travel and may connect to a service computing device for guidance. The service computing device may include a guidance system configured to receive waypoint and/or orientation input from an operator. The operator may evaluate the scenario and determine one or more waypoints and/or associated orientations for the vehicle to navigate the scenario. In some examples, the guidance system may validate the waypoint(s) and/or associated orientation(s). The service computing device may send the waypoint(s) and/or associated orientation(s) to the vehicle computing system. The vehicle computing system may validate the waypoint(s) and/or associated orientation(s) and, based on the validation, control the vehicle according to the input. Based on a determination that the vehicle has navigated the scenario, the guidance system may release vehicle guidance back to the vehicle computing system.
There is provided a network comprising a network switch comprising a pre-configured routing table and a first physical port coupled to an endpoint sensor device initiated with an initial network address. One or more processors are coupled to a third physical port of the network switch and are configured to: transmit configuration data for the endpoint sensor device to the third physical port via a first virtual area network. The first virtual area network is associated by the pre-configured routing table with the first physical port and a second virtual area network is associated with a second physical port of the network switch. A command to the endpoint sensor to use a different network address is transmitted by the network switch via the first physical port based at least in part on receiving the configuration data.
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
There is provided a method and system for vehicle security protection. The method comprises: determining, at a detector, a first sensor signal from a flex sensor in an exterior panel of a vehicle, wherein the flex sensor and detector are powered by a power supply of the vehicle, and wherein a magnitude of the first sensor signal is based on an amount of bending of the flex sensor; determining, at the detector, based on the first sensor signal, that the exterior panel of the vehicle is deformed; and outputting, from the detector, a first alert signal, wherein a first controller of the vehicle is configured to transition from a low-power state to a high-power state based at least in part on the first sensor signal.
B60R 21/0136 - Electrical circuits for triggering safety arrangements in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over responsive to actual contact with an obstacle
G07C 5/00 - Registering or indicating the working of vehicles
G01L 1/22 - Measuring force or stress, in general by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress using resistance strain gauges
B60R 21/017 - Electrical circuits for triggering safety arrangements in case of vehicle accidents or impending vehicle accidents including arrangements for providing electric power to the safety arrangements
B60R 16/03 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for supply of electrical power to vehicle subsystems
70.
System for Generating Scene Context Data Using a Reference Graph
Techniques for improving operational decisions of an autonomous vehicle are discussed herein. In some cases, a system may generate reference graphs associated with a route of the autonomous vehicle. Such reference graphs can comprise precomputed feature vectors based on grid regions and/or lane segments. The feature vectors are usable to determine scene context data associated with static objects to reduce computational expenses and compute time.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
Techniques for controlling a vehicle are described herein. A system may receive a route to navigate from a start position to an end position in an environment. The system may receive map data based on the route and determine a lattice based on the map data. The lattice comprises nodes and connections therebetween. The nodes may represent various states of the vehicle. The connections may represent various feasible transitions between the nodes. The lattice may further comprise a set of connections representing a trajectory from the start position to the end position. The system may receive sensor data representing an object in the environment and determine a state of the object based on the sensor data. The system may modify, based on the object state and as an updated cost, a precomputed cost associated with the trajectory. The system may further control the vehicle based on the updated cost.
A wind tunnel test may be performed on a vehicle to determine accumulation of substances (e.g., water) on sensors of the vehicle. Control surfaces may be created for the sensors based images representing the accumulations, where the control surfaces represent the include obstructions located where accumulations were detected during the test. The vehicle may then navigate around an environment using the control surfaces in order to determine a drivability of the vehicle. Also, a simulation may be performed, where the simulation outputs images representing simulated accumulations on the sensors. The outputs from the simulation may be compared to the results from the test in order to determine how accurately the simulation represents the test, determine domains in which the vehicle may safely operate, and/or improve the simulation.
A vehicle computing system may implement techniques to determine whether two objects in an environment are related as an articulated object. The techniques may include applying heuristics and algorithms to object representations (e.g., bounding boxes) to determine whether two objects are related as a single object with two portions that articulate relative to each other. The techniques may include predicting future states of the articulated object in the environment. One or more model(s) may be used to determine presence of the articulated object and/or predict motion of the articulated object in the future. Based on the presence and/or motion of the articulated object, the vehicle computing system may control operation of the vehicle.
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
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
Techniques for determining right of way through an intersection are discussed herein. Routes through the intersection may be associated with respective priorities. The route associated with an inbound lane devoid of yield or stop markers may be determined as being associated with the highest priority. The hierarchy of the other priorities may be organized based on whether the number of times routes associated with each respective priority intersects the route associated with the highest priority. The routes and the priorities are saved in a data structure, and the data structure is transmitted to an autonomous vehicle for controlling the autonomous vehicle through the intersection.
Techniques for presenting a user interface on a display for monitoring and/or controlling a vehicle. The user interface may include a digital representation of an environment in which the vehicle is operation. Additionally, the user interface may include system interface that is configured to display one or more notifications associated with systems or components of the vehicle. The system interface may additionally, or alternatively, comprise one or more control inputs for controlling systems or components of the vehicle. The user interface may also include a mission interface that includes a map interface and a mission selection interface for selecting routes the vehicle is to navigate. The user interface may additionally cause presentation of visual indicators that indicate the presence of an object that is not shown on the user interface, but that is moving in a direction such that the object will soon be visibly displayed on the user interface.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
B60W 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 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
G01C 21/26 - Navigation; Navigational instruments not provided for in groups specially adapted for navigation in a road network
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
Techniques for utilizing microphone or audio data to detect and responding to low velocity impacts to a system such as an autonomous vehicle. In some cases, the system may be equipped with a plurality of microphones that may be used to detect impacts that fail to register on the data captured by the vehicle's inertial measurement units and may go undetected by the vehicle's perception system and sensors. In one specific example, the perception system of the autonomous vehicle may identify a period of time in which a potential low velocity impact may occur. The autonomous vehicle may then utilize the microphone or audio data associated with the period of time to determine if an impact occurred.
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
Techniques for determining a safety margin by which to limit trajectory(ies) generated by a vehicle control system such that the vehicle will not exceed the safety margin more than a target occurrence rate. The techniques may include determining a first spectrum associated with trajectory data generated by one or more vehicles, generating a model of a vehicle, and determining a spectrum of an error signal based at least in part on the model and the first spectrum. Determining the safety margin may be based at least in part on the spectrum of the error signal and a target occurrence rate. Operation characteristics of components of the vehicle (e.g., controller, steering actuator) may be tuned based at least in part on the model, first spectrum, and/or second spectrum. The techniques enable determining safety margins for untested vehicles and/or for different operating states of a vehicle.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
Techniques for assisting a passenger to identify a vehicle and for assisting a vehicle to identify a passenger are discussed herein. Also discussed herein are techniques for capturing data via sensors on a vehicle or user device and for presenting such data in various formats. For example, in the context of a ride hailing service using autonomous vehicles, the techniques discussed herein can be used to identify a passenger of the autonomous vehicle at the start of a trip, and can be used to assist a passenger to identify an autonomous vehicle that has been dispatched for that particular passenger. Additionally, data captured by sensors of the vehicle and/or by sensors of a user device can be used to initiate a ride, determine a pickup location, orient a user within an environment, and/or provide visualizations or augmented reality elements to provide information and/or enrich a user experience.
A vehicle includes an expandable curtain disposed in a roof of the vehicle and is configured to selectively deploy from a stowed configuration to a deployed configuration. An expandable bladder is configured to inflate at least partially during deployment of the expandable curtain. The expandable bladder includes a neck portion mechanically coupled to the expandable curtain and a head portion extending from the neck portion.
B60R 21/232 - Curtain-type airbags deploying mainly in a vertical direction from their top edge
B60R 21/233 - Inflatable members characterised by their shape, construction or spatial configuration comprising two or more bag-like members, one within the other
B60R 21/214 - Arrangements for storing inflatable members in their non-use or deflated condition; Arrangement or mounting of air bag modules or components in roof panels
B60R 21/231 - Inflatable members characterised by their shape, construction or spatial configuration
Techniques for reducing the wear on vehicles. For instance, a vehicle may include a bidirectional vehicle that operates in a first mode at which a first end of the vehicle operates as a front of the vehicle and a second mode at which a second end of the vehicle operates as the front of the vehicle. As such, the vehicle may store data representing a first distance that the vehicle has traveled while operating in the first mode and a second distance that the vehicle has traveled while operating in the second mode. The vehicle may then detect the occurrence of an event. Based on the occurrence of the event, the vehicle may select a mode for operating the vehicle using the first distance and the second distance. For example, the vehicle may select the mode that is associated with the shortest distance.
Techniques for estimating a ground plane based on lidar data and/or attributes of the ground plane are discussed herein. A vehicle captures radar data, e.g., 4D radar data including height information, as it traverses an environment. The radar data can include direct returns from an object and reflected or multipath returns, e.g., that reflect off a ground surface and the object. A position of the ground plane can be estimated based at least in part on a distance between direct returns and the reflected returns. Attributes of the ground plane may be determined from differences between the direct returns and the reflected returns.
G01S 7/41 - RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisation; Target signature; Target cross-section
G01S 13/931 - Radar or analogous systems, specially adapted for specific applications for anti-collision purposes of land vehicles
A method and system of determining whether a stationary vehicle is a blocking vehicle to improve control of an autonomous vehicle. A perception engine may detect a stationary vehicle in an environment of the autonomous vehicle from sensor data received by the autonomous vehicle. Responsive to this detection, the perception engine may determine feature values of the environment of the vehicle from sensor data (e.g., features of the stationary vehicle, other object(s), the environment itself). The autonomous vehicle may input these feature values into a machine-learning model to determine a probability that the stationary vehicle is a blocking vehicle and use the probability to generate a trajectory to control motion of the autonomous vehicle.
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G05D 1/02 - Control of position or course in two dimensions
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 operat
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
G06F 18/243 - Classification techniques relating to the number of classes
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
84.
Reducing hydraulic fluid pressure based on predicted collision
A vehicle may receive sensor data captured by a sensor, determine that the sensor data represents an object in the environment, and determine a collision probability associated with a collision between the vehicle and the object. Based at least in part on the collision probability, the vehicle may determine one or more mitigating actions to perform prior to, during, and/or after the collision. The mitigating action may be associated with adjusting a hydraulic fluid pressure in at least a portion of a hydraulic fluid system of the vehicle.
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
B60G 17/019 - Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or s the regulating means comprising electric or electronic elements characterised by the type of sensor or the arrangement thereof
Techniques for determining error models for use in simulations are discussed herein. Ground truth perception data and vehicle perception data can be determined from vehicle log data. Further, objects in the log data can be identified as relevant objects by signals output by a planner system or based on the object being located in a driving corridor. Differences between the ground truth perception data and the vehicle perception data can be determined and used to generate error models for the relevant objects. The error models can be applied to objects during simulation to increase realism and test vehicle components.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G07C 5/00 - Registering or indicating the working of vehicles
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
Techniques for charging a battery associated with a vehicle are discussed herein. A dense charging station for charging the battery may have lanes arranged in parallel, and each of the lanes may have sequential charging locations. A vehicle utilizing the charging station may position itself at the first available charging location, being receiving energy, and determine if a subsequent charging station becomes available in the lane, and then position itself at the subsequent charging location, once available. Multiple charging stations may be required to maintain a threshold power state for individual vehicles in a fleet of vehicles providing a service for a geographic region. A charging coordinator may determine when a battery of a vehicle does not satisfy a threshold power state and requires a recharge. Additionally, the charging coordinator may determine a candidate charging station from among multiple charging stations associated with the geographic region.
H02J 7/00 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
H02J 50/10 - Circuit arrangements or systems for wireless supply or distribution of electric power using inductive coupling
B60L 53/30 - Constructional details of charging stations
H02J 50/90 - Circuit arrangements or systems for wireless supply or distribution of electric power involving detection or optimisation of position, e.g. alignment
H02J 50/40 - Circuit arrangements or systems for wireless supply or distribution of electric power using two or more transmitting or receiving devices
87.
Simulating autonomous driving using map data and driving data
Testing autonomous vehicle control systems in the real world can be difficult, because creating and re-creating physical scenarios for repeated testing may be impractical. In some implementations, detailed map data and data acquired through driving in a region can be used to identify similar segments of a drivable surface. Simulation scenarios used to test one of the similar segments may be used to test other of the similar segments. The driving data may also be used to generate and/or validate the simulation scenarios, e.g., by re-creating scenarios encountered while driving in a first segment in a simulation scenario for use in the second segment and comparing simulated driving behavior with the driving data.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
A suspension including one or more elasto-hydraulic bushings that can enable adjustment of the kinematic pivot point. The suspension can include a bushing system having a single buishing or a coupled bushing pair interactively arranged and configured to transform rotational motion into translational motion or vice versa by the expansion and contraction of one or more reservoirs.
Techniques for procedurally generating scenarios that verify and validate predictions from or operation of a vehicle safety system are discussed herein. Sensors of a vehicle may detect one or more objects in the environment. A model may determine intersection values indicative of probabilities that the object will intersect with a trajectory of the vehicle. A vehicle may receive one or more intersection values from a model usable by a computing device to validate a prediction from a vehicle safety system.
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 30/095 - Predicting travel path or likelihood of collision
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
90.
System processing scenario objects during simulation
Techniques associated with improving performance and realism of simulation instances associated with simulation testing of autonomous vehicles. In some cases, a simulation system may be configured to run a pre-simulation test to identify and store occlusion data to improve the performance of subsequent simulations associated with a shared scene or route.
Techniques for modeling the probability distribution of errors in perception systems are discussed herein. For example, techniques may include modeling error distribution for attributes such as position, size, pose, and velocity of objects detected in an environment, and training a mixture model to output specific error probability distributions based on input features such as object classification, distance to the object, and occlusion. The output of the trained model may be used to control the operation of a vehicle in an environment, generate simulations, perform collision probability analyses, and to mine log data to detect collision risks.
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06F 18/213 - Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
92.
SYSTEMS AND METHODS FOR DETERMINING BUFFER REGIONS
Systems and techniques for determining a buffer region for use in controlling a vehicle and avoiding collisions are disclosed herein. A predicted region of travel of a vehicle front bumper may be determined. The position of the front bumper may be determined at points along a center curve of the predicted region of travel and polygons may be determined for the positions. The polygons may be joined and modified using a convex shape-based algorithm to determine a convex polygonal buffer region that is used in collision detection.
B60W 30/095 - Predicting travel path or likelihood of collision
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
93.
Efficient climate control for multi-user autonomous vehicles
There is provided a method comprising: determining an occupancy status of a first region of a vehicle; determining, based at least in part on the occupancy status, a first climate control setting for the first region; controlling a climate control system of the vehicle to adjust a climate of the first region according to the first climate control setting; determining that a second region of the vehicle is unoccupied, wherein the second region is fluidly connected to the first region; determining a second climate control setting, wherein the second climate control setting is based at least in part on the occupancy status of the first region and characteristic data associated with a predicted potential change in occupancy status for the second region; and controlling the climate control system to adjust a climate of the second region according to the second climate control setting.
A vehicle computing system may implement techniques to determine an action for a vehicle to perform based on a cost associated therewith. The cost may be based on a detected object (e.g., another vehicle, bicyclist, pedestrian, etc.) operating in the environment and/or a possible object associated with an occluded region (e.g., a blocked area in which an object may be located). The vehicle computing system may determine two or more actions the vehicle could take with respect to the detected object and/or the occluded region and may generate a simulation associated with each action. The vehicle computing system may run the simulation associated with each action to determine a safety cost, a progress cost, a comfort cost, an operational rules cost, and/or an occlusion cost associated with each action. The vehicle computing system may select the action for the vehicle to perform based on an optimal cost being associated therewith.
Techniques associated with generating and maintaining sparse geographic and map data. In some cases, the system may maintain a factor graph comprising a plurality of nodes. In some cases, the nodes may comprise pose data and sensor data associated with an autonomous vehicle at the geographic position represented by the node. The nodes may be linked based on shared trajectories and shared sensor data.
A location of an occupant within a vehicle and/or an activity engaged in by the occupant may be determined. Based on the location and/or the activity, a point of interest associated with the occupant and/or the vehicle may be determined. One or more systems of the vehicle, such as the steering and/or suspension, may be controlled to minimize acceleration associated with the point of interest, thereby increasing a comfort of the occupant. In instances where the vehicle includes more than one occupant, the vehicle may be adjusted to accommodate the multiple occupants.
B62D 9/00 - Steering deflectable wheels not otherwise provided for
B60W 10/20 - Conjoint control of vehicle sub-units of different type or different function including control of steering systems
B60W 10/22 - Conjoint control of vehicle sub-units of different type or different function including control of suspension systems
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
B60W 10/04 - Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
Techniques for determining a warped occupancy grid fit to a vehicle trajectory are discussed herein. In some examples, a portion of memory may be allocated to an occupancy grid. Further, a warped occupancy grid can be warped and associated with an environment that an autonomous vehicle is traversing according to a trajectory and/or throughway. A transformation maybe be determined between the warped occupancy grid and the memory allocated to the occupancy grid. Sensor data can be received from a sensor associated with the autonomous vehicle and may be associated with the warped occupancy grid and stored in the occupancy grid. The autonomous vehicle may be controlled according to the warped occupancy grid by identifying sensor data returns in cells of the warped occupancy grid that may indicate a detection of an object in a path of travel of the vehicle.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
98.
Robust numerically stable Kalman filter for autonomous vehicles
The techniques discussed herein include modifying a Kalman filter to additionally include a loss component that dampens the effect measurements with large errors (or measurements indicating states that are rather different than the predicted state) have on the Kalman filter and, in particular, the updated uncertainty and/or updated prediction. In some examples, the techniques include scaling a Kalman gain based at least in part on a loss function that is based on the innovation determined by the Kalman filter. The techniques additionally or alternatively include a reformulation of a Kalman filter that ensures that the uncertainties determined by the Kalman filter remain symmetric and positive definite.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
An occupant protection system may comprise an expandable curtain, expandable bladder and tether. The curtain may be configured to expand from a stowed state to a deployed state. In the deployed state, the curtain may comprise a transverse portion having a support side face and a rear side face. The bladder may be configured to expand from a stowed state to a deployed, wherein in the deployed state, the bladder comprises an occupant facing surface and a rear surface configured to face the support side face of the curtain. The tether may be attached at a first location to the bladder and at a second location such that in the deployed state of the curtain and bladder, the tether extends behind the rear side face of the curtain and frictionally engages the rear side face of the curtain, thereby creating a resistance to lateral movement of the bladder.
B60R 21/214 - Arrangements for storing inflatable members in their non-use or deflated condition; Arrangement or mounting of air bag modules or components in roof panels
B60R 21/232 - Curtain-type airbags deploying mainly in a vertical direction from their top edge
B60R 21/231 - Inflatable members characterised by their shape, construction or spatial configuration
100.
System for determining a state of an object using thermal data
Techniques associated with predicting behaviors and states of objects in a physical environment using thermal data.. In some cases, the system may be configured to determine heat signatures of individual features of an object and based on a combination of heat signatures determine a predicted behavior and/or a state of the object. The system may also utilize the thermal data to determine a confidence associated with predicted behavior and/or states of the object.
patents
