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1113results about "Autonomous decision making process" patented technology
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Perception system error detection and re-verification
ActiveUS12299077B2Control safety arrangementsAutonomous decision making processPerception systemData mining
A sensor data evaluation device may include an artificial neural network, configured to receive and process first testing data, and output a first testing data output, wherein the first testing data represent sensor data of a first sensor detecting a sensor input; and one or more processors, configured to receive sensor deficiency information representing a deficiency of a second sensor; generate second testing data, wherein the second testing data are based on the sensor deficiency information; implement the artificial neural network to receive and process the second testing data; and output from the output layer a second testing data output; determine a difference between the first testing data output and the second testing data output; and if the difference is outside of one or more predetermined ranges or does not satisfy one or more predetermined criteria, send a signal representing a result of the determined difference.
Owner:INTEL CORP
Using the presence of road surface and surrounding area illumination to detect occluded objects
ActiveUS12291236B1Autonomous decision making processAnti-collision systemsComputer graphics (images)Road user
The technology relates to detection of a nearby occluded object based on illumination emitted from that object. Illumination by the occluded object of one or more areas in the surrounding area, for instance by headlights of an occluded vehicle, is detected by a perception system of a self-driving vehicle. The self-driving vehicle can classify the detected object to determine whether the illumination is caused by a vehicle or other road user, or from objects in the surrounding environment. Illumination data and other information can be evaluated by the self-driving vehicle, for instance to identify a type of the object, a location of the object along a roadway, to disambiguate the direction of travel of the other object, etc. As a result, the self-driving vehicle may infer the behavior of the other object and modify its own driving operations to account for the other object's presence and likely behavior.
Owner:WAYMO LLC
Curvature sensing and guidance control system for an agricultural vehicle
ActiveUS12372961B2Autonomous decision making processCharacter and pattern recognitionGuidance controlControl system
An autonomous vehicle control system includes one or more sensors configured for coupling with an agricultural vehicle, the one or more sensors configured to determine kinematics of the agricultural vehicle relative to a crop row. The system includes a guidance control module configured to coordinate steering of one or more steering mechanisms of the agricultural vehicle. The guidance control module includes a sensor input configured to receive kinematics of the agricultural vehicle, a vehicle kinematics comparator configured to determine one or more error values using the received vehicle kinematics, a crop curvature generator configured to determine crop row curvature using the one or more error values, and a steering interface configured to provide instructions to a vehicle steering controller to guide the agricultural vehicle using the crop row curvature.
Owner:RAVEN INDUSTRIES INC
Multi-view deep neural network for LiDAR perception
A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and / or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and / or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and / or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.
Owner:NVIDIA CORP
Routing multiple autonomous vehicles using local and general route planning
ActiveUS12305997B2Instruments for road network navigationAutonomous decision making processRoute planningDistributed computing
Various examples are directed to systems and methods for routing a plurality of autonomous vehicles. A batch routing system may receive a first general route cost request from a first autonomous vehicle. The batch routing system may determine a first set of general routes for the first autonomous vehicle. The batch routing system may send a first general route cost reply to the first autonomous vehicle. The first general route cost reply may indicate a first set of costs associated with the first set of general routes. The first set of costs may comprise a first general cost for the first general route and a second general cost for the second general route.
Owner:AURORA OPERATIONS INC +1
Autonomous control of operations of earth-moving vehicles using data from simulated vehicle operation
ActiveUS12487596B1Autonomous decision making processSoil-shifting machines/dredgersSimulationMachine
Systems and techniques are described for implementing autonomous control of earth-moving construction and / or mining vehicles, including to automatically determine and control autonomous movement of part or all of one or more such vehicles (e.g., a vehicle's arm(s) and / or attachment(s), such as a digging bucket, claw, hammer, blade, etc.) to move materials or perform other actions in a manner that is based at least in part on data from simulated operation of the vehicle(s). For example, the systems / techniques may include using data from simulated operation of the earth-moving vehicle(s) in various manners, such as for use in training one or more machine learning models that are used in implementing the autonomous operations, determining optimal or otherwise preferred hardware component configurations to use, determining optimal or otherwise preferred implementation plans to use for one or more tasks and / or multi-task jobs, enabling user what-if experimentation activities, etc.
Owner:AIM INTELLIGENT MACHINES INC
Field configurable autonomous vehicle
ActiveUS12454338B1Autonomous decision making processPermanent magnetsReal-time computingAutomotive engineering
Owner:OFFICE OF NAVAL RES
Methods, apparatus, and articles of manufacture to display acquisition paths
PendingUS20250271857A1Autonomous decision making processNavigational calculation instrumentsSimulationReal time display
Methods, apparatus, systems, and articles of manufacture are disclosed to display acquisition paths. An example apparatus includes a display handler to, in response to a directional condition of an autonomous vehicle satisfying a threshold, render an acquisition path for the autonomous vehicle, the acquisition path specifying a route along which the autonomous vehicle is to travel to acquire a guidance path; and a vehicle condition controller to, in response to determining that a real time display criterion is satisfied, redetermine whether the directional condition of the autonomous vehicle satisfies the threshold to cause the display handler to re-render the acquisition path.
Owner:DEERE & CO
Real-time adjustment of vehicle sensor field of view volume
ActiveUS12439003B2Autonomous decision making processDetection of traffic movementTesting MethodsField of view
Disclosed are systems and methods that can be used for adjusting the field of view of one or more sensors of an autonomous vehicle. In the systems and methods, each sensor of the one or more sensors is configured to operate in accordance with a field of view volume up to a maximum field of view volume. The systems and methods include determining an operating environment of an autonomous vehicle. The systems and methods also include based on the determined operating environment of the autonomous vehicle, adjusting a field of view volume of at least one sensor of the one or more sensors from a first field of view volume to an adjusted field of view volume different from the first field of view volume. Additionally, the systems and methods include controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume.
Owner:WAYMO LLC
Agricultural characteristic confidence and control
ActiveUS12364181B2Agricultural vehiclesAutonomous decision making processAgricultural engineeringSupplementary data
A mobile agricultural machine obtains an agricultural characteristic map indicative of agricultural characteristics of a field, wherein the agricultural characteristic map is based on data collected at or prior to a first time. The mobile agricultural machine obtains supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time. An agricultural characteristic confidence output, indicative of a confidence level in the agricultural characteristics indicated by the agricultural characteristic map, is generated based on the agricultural characteristic map and the supplemental data. In some examples, an action signal is generated to control an action of the mobile agricultural machine based on the agricultural characteristic confidence output.
Owner:DEERE & CO
Systems and methods to explain an artificial intelligence policy of behavior with causal reasoning
PendingUS20250130567A1Mathematical modelsAutonomous decision making processCausal reasoningEngineering
A method includes generating a data structure including states and actions to be executed at those states as determined by an AI policy of behavior, determining, with a first computing system that is offline, state factors associated with the states and responsibility scores for the state factors, each responsibility score indicating a causal impact for each of the actions associated with one of the states, generating, with the first computing system, a causal ML model based on the state factors and the responsibility scores, determining, with a second computing system that is online based on the causal ML model, state factors associated with a current state, and identifying one or more of the state factors as a causal reason for an action resulting from the current state. Other example methods and systems for providing explanation of an AI policy of behavior with causal reasoning are also disclosed.
Owner:GM GLOBAL TECHNOLOGY OPERATIONS LLC
Ultraviolet lamp apparatuses which move their lamp / s while emitting light
ActiveUS12362165B2Autonomous decision making processLighting support devicesProgram instructionUltraviolet lights
Apparatuses are disclosed which include a lamp assembly having a germicidal lamp configured to emit ultraviolet light and a base supporting the germicidal lamp. The apparatuses further include a support structure holding operational components for the apparatus and an automated actuator for moving the base and the germicidal lamp relative to the support structure. In addition, the apparatuses include a processor and a storage medium having program instructions which are executable by the processor for activating the automated actuator such that the base and the germicidal lamp are repositioned relative to the support structure while the germicidal lamp is emitting ultraviolet light.
Owner:XENEX DISINFECTION SERVICES
Systems and methods for using reinforcement learning agents to track targets based on a reward including an information metric reward
An estimated target position is generated based on agent data received from an agent sensor system of a reinforcement learning (RL) agent and teammate data received from a plurality of teammate RL agents. An information metric reward is generated based on a confidence level associated with the estimated target position. The confidence level is based on the estimated target position and historical estimated target positions. A distance metric reward is generated based on an agent position and the estimated target position. A combined reward is generated based on the information metric reward and the distance metric reward. A movement action is generated for the RL agent based on the agent position, the estimated target position, and the combined reward in accordance with a multi-agent reinforcement learning (MARL) algorithm.
Owner:GENERAL DYNAMICS MISSION SYSTEMS INC
Methods, apparatus, and articles of manufacture to display acquisition paths
ActiveUS12326729B2Autonomous decision making processNavigational calculation instrumentsSimulationReal time display
Methods, apparatus, systems, and articles of manufacture are disclosed to display acquisition paths. An example apparatus includes a display handler to, in response to a directional condition of an autonomous vehicle satisfying a threshold, render an acquisition path for the autonomous vehicle, the acquisition path specifying a route along which the autonomous vehicle is to travel to acquire a guidance path; and a vehicle condition controller to, in response to determining that a real time display criterion is satisfied, redetermine whether the directional condition of the autonomous vehicle satisfies the threshold to cause the display handler to re-render the acquisition path.
Owner:DEERE & CO
Monitoring driver behaviour
ActiveGB2635163AAutonomous decision making processCharacter and pattern recognitionControl systemSimulation
A control system for monitoring a divergence from a baseline driving behaviour of an operator of a vehicle. The control system is configured to: calculate a trajectory score based on a comparison of a
Owner:JAGUAR LAND ROVER LTD
Intermediate waypoint generator
ActiveUS12442640B2Programme-controlled manipulatorProgramme controlComputer graphics (images)Engineering
A method for generating intermediate waypoints for a navigation system of a robot includes receiving a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and is based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving image data of an environment about the robot from an image sensor and generating at least one intermediate waypoint based on the image data. The method also includes adding the at least one intermediate waypoint to the series of high-level waypoints of the navigation route and navigating the robot from the starting location along the series of high-level waypoints and the at least one intermediate waypoint toward the destination location.
Owner:BOSTON DYNAMICS INC
Safe autonomous overtaking with intention estimation
ActiveUS12304487B2Autonomous decision making processAnti-collision systemsData processing systemOvertaking
The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a vehicle overtaking system. The method includes causing a vehicle control system in an ego vehicle to execute at least a portion of an input control sequence, receiving, from a first plurality of sensors coupled to the ego vehicle, lead vehicle data about a lead vehicle, estimating an intention of the lead vehicle based on the lead vehicle data, and causing the vehicle control system to perform a vehicle maneuver based on the intention of the lead vehicle.
Owner:AZ BRD REGENTS ON BEHALF OF AZ STATE UNIV +1
Dual agent reinforcement learning based system for autonomous operation of aircraft
A dual agent reinforcement learning autonomous system (DARLAS) for the autonomous operation of aircraft and / or provide pilot assistance. DARLAS includes an artificial neural network, safe agent, and cost agent. The safe agent is configured to calculate safe reward Q values associated with landing the aircraft at a predetermined destination or calculated emergency destination. The cost agent is configured to calculate cost reward Q values associated with maximum fuel efficiency and aircraft performance. The safe and cost reward Q values are based on state-action vectors associated with an aircraft, which may include state data and action data. The system may include a user output device that provides an indication of an action to a user. The action corresponds to an agent action having the highest safe reward Q value and the highest cost require Q value. DARLAS prioritizes the highest safe reward Q value in the event of conflict.
Owner:THE BOEING CO
Free space mapping and navigation
ActiveUS12441353B2Controlling traffic signalsInstruments for road network navigationSimulationPlace Identifier
A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.
Owner:MOBILEYE VISION TECH LTD
Techniques for authorizing vehicle control systems
Techniques and methods for securing vehicle systems. For instance, an authorization system may store data representing frequencies at which destination locations are associated pick-up locations for a fleet of autonomous vehicles. The authorization system may then receive a request for an autonomous vehicle to pick up a passenger at a first location and drop off the passenger at a second location. Based on the first location and the second location, the authorization system may determine a frequency for the request. The authorization system may then determine whether a control system for the fleet of autonomous vehicles is compromised based on whether the frequency is less than or equal to a threshold frequency. If the authorization system determines that the control system is compromised, the authorization system may perform a remedial action, such as notifying a teleoperator.
Owner:ZOOX INC
System And Method For Adjusting Vehicle Location
PendingUS20250147506A1Instruments for road network navigationAutonomous decision making processControl engineeringReal-time computing
Owner:RIDECELL INC
Mobile body, information processor, mobile body system, information processing method, and information processing program
ActiveUS12298771B2Instruments for road network navigationAutonomous decision making processTime informationComputer graphics (images)
An information processing method of an information processor includes: obtaining information received from a mobile body through a wireless communication, the mobile body including a movement mechanism and an imaging unit configured to capture image data, the information received from the mobile body including captured image data obtained by the imaging unit, with the captured image data being updated periodically; and generating route guidance information for use in moving the mobile body by the movement mechanism. The captured image data is stored together with data update time information. The route guidance information includes at least two selectable routes. The route guidance information is generated based on the captured image data, position information of the mobile body, and the data update time information.
Owner:SONY GROUP CORP
Latent belief space planning using a trajectory tree
Techniques for latent belief space planning include: during execution of an autonomous agent configured to control operation of a physical mechanism, obtaining a current observation of a physical environment; based at least on the current observation of the physical environment, generating a trajectory tree that represents possible trajectories in a belief space, wherein nodes of the trajectory tree represent values of a continuous observation, a continuous state, and a continuous control, each node being associated with one of multiple timesteps along the plurality of possible trajectories, and wherein branches from inner nodes to child nodes correspond to possible outcomes and observations of a multi-modal latent state; determining a current value of the continuous control associated with a current node; and applying the current value of the continuous control to operation of the physical mechanism.
Owner:ISEE
Deep neural network for segmentation of road scenes and animate object instances for autonomous driving applications
A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and / or other sensor data may be stitched together, stacked, and / or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and / or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and / or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.
Owner:NVIDIA CORP
Systems and methods for reconstruction of a vehicular crash
ActiveUS12420777B1Control safety arrangementsAutonomous decision making processExternal dataVirtual reconstruction
A system for detecting a vehicular collision (i) receives occupant data from at least one internal sensor; (ii) receives external data from at least one external sensor; (iii) determines positional information for at least one occupant of a vehicle; and (iv) generates a virtual reconstruction of the vehicle collision. The virtual reconstruction indicates a severity of vehicle damage and a severity of injury to the at least one occupant caused by the vehicle collision. The system may also utilized vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle systems, and / or perform at least one action to reduce a severity of a potential injury.
Owner:STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY
Performance testing for robotic systems
ActiveUS12416921B2Control safety arrangementsAutonomous decision making processRobotic systemsGround truth
Herein, a “perception statistical performance model” (PSPM) for modeling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety / performance testing. A PSPM is configured to: receive a computed perception ground truth t; determine from the perception ground truth t, based on a set of learned parameters, a probabilistic perception uncertainty distribution of the form p(e|t), p(e|t,c), in which p(e|t,c) denotes the probability of the perception slice computing a particular perception output e given the computed perception ground truth t and the one or more confounders c, and the probabilistic perception uncertainty distribution is defined over a range of possible perception outputs, the parameters learned from a set of actual perception outputs generated using the perception slice to be modeled, wherein each confounder is a variable of the PSPM whose value characterized a physical condition on which p(e|t,c) depends.
Owner:FIVE AI LTD
Detecting Conflict Along a Route of a Robot
PendingUS20250265936A1Autonomous decision making processPosition/course control in two dimensionsComputer graphics (images)Computer vision
A method is provided for detecting conflict along a route for a robot to travel. The method includes generating a predicted trajectory of a nearby moving object, generating a trajectory of the robot, and performing a comparison of the nearby moving object and the robot on their projected trajectory and trajectory to detect a conflict that may be avoided. The comparison includes determining updated positions and velocities of the nearby moving object and the robot. The comparison also includes determining if the nearby moving object is within a clear region that includes the robot from the updated positions, and if a time to closest point of approach between nearby moving object and the robot is less than a time threshold value from the updated velocities. The conflict is then detected when the nearby moving object is within the clear region, and the time to closest point of approach is less than the time threshold value.
Owner:AURORA FLIGHT SCIENCES CORP
Estimating autonomous vehicle performance metrics in real world from simulation scenarios
ActiveUS12444247B1Mathematical modelsAutonomous decision making processSimulationOperations research
Evaluating the performance of an autonomous vehicle includes determining a plurality of simulation scenarios, determining a set of features correlated to a performance metric of interest for the autonomous vehicle, executing a simulation for each simulation scenario in the plurality of simulation scenarios, determining, by a machine learning model, a weight for each simulation scenario in the set of simulation scenarios subject to a constraint that a simulated expected value of each feature in the plurality of simulation scenarios falls within a threshold range of an observed expected value of each feature in an operation design domain of interest of the autonomous vehicle, and estimating an expected value of the performance metric of interest of the autonomous vehicle based on the determined weight and the execution of the simulation for each simulation scenario in the plurality of simulation scenarios.
Owner:AURORA OPERATIONS INC
Systems and methods for generic visual odometry using learned features via neural camera models
Systems and methods for self-supervised learning for visual odometry using camera images, may include: estimating correspondences between keypoints of a target camera image and keypoints of a context camera image; based on the keypoint correspondences, lifting a set of 2D keypoints to 3D, using a neural camera model; and projecting the 3D keypoints into the context camera image using the neural camera model. Some embodiments may use the neural camera model to achieve the lifting and projecting of keypoints without a known or calibrated camera model.
Owner:TOYOTA JIDOSHA KK