Abstract: Systems and methods of generating and updating a world model for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive sensor data from a plurality of sensors of an autonomous vehicle, where the sensor data is captured during operation of the autonomous vehicle; access a world model generated based at least on map information corresponding to a location of the operation of the autonomous vehicle; determine at least one semantic correction for the world model based on the sensor data; determine at least one geometric correction for the world model based on the sensor data and the map information; and generate an updated world model based on the at least one semantic correction and the at least one geometric correction.

Abstract: Fluid release detection is provided. A vehicle can determine, via a first sensor, a flow rate of a first fluid, such as a headspace gas, between an environment and a reservoir of the vehicle. The vehicle can receive a fluid consumption rate for a second fluid, such as a hydrocarbon fuel associated with the reservoir. The vehicle can detect, based on the flow rate and the fluid consumption rate, a fluid release of the reservoir. The vehicle can execute an action responsive to the detection of the fluid release.

Abstract: A method comprises identifying a set of radar data captured by at least one autonomous vehicle when the at least one autonomous vehicle was positioned in a lane of a roadway, and respective ground truth localization data of the at least one autonomous vehicle; filtering one or more objects identified via the set of radar data in accordance with an attribute of the one or more objects; generating a map layer to be included within a high definition map; identifying second set of radar data captured by a second autonomous vehicle when the at least one autonomous vehicle was positioned in the lane of the roadway, and respective ground truth localization data of the at least one autonomous vehicle; and localizing the second autonomous vehicle by executing a matching protocol to match an object within the second set of radar data with an object within the map layer.

Abstract: Systems and methods of generating and updating a world model for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive sensor data from a plurality of sensors of an autonomous vehicle, where the sensor data is captured during operation of the autonomous vehicle; access a world model generated based at least on map information corresponding to a location of the operation of the autonomous vehicle; determine at least one semantic correction for the world model based on the sensor data; determine at least one geometric correction for the world model based on the sensor data and the map information; and generate an updated world model based on the at least one semantic correction and the at least one geometric correction.

Abstract: Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive, from a first autonomous vehicle traveling on a road, a first correction to map data identifying a location in the map data; generate a modified parameter of the map data based on the first correction and a second correction identifying the location, the second correction received from a second autonomous vehicle traveling on the road; update the map data based on the modified parameter; and provide the updated map data to the first autonomous vehicle.

Abstract: A method may include obtaining, from a kinematic vehicle model including a differential equation, by one or more processors associated with an autonomous vehicle, one or more candidate trajectories that satisfy one or more constraints relating to movement of the vehicle. The method may include iteratively updating, by the one or more processors, a score of each of the one or more candidate trajectories. The method may include determining, by the one or more processors among the one or more candidate trajectories, a target trajectory having a score that is greater than a threshold. The method may include executing, by the one or more processors, the target trajectory to cause the vehicle to move in the candidate trajectory.

Abstract: Autonomous vehicles including sensors and electronic control units (ECUs) that communicate via ring networks are disclosed. A system includes an ECU of an autonomous vehicle that includes an ECU network interface. The system includes a plurality of sensors disposed on the autonomous vehicle, and each of the plurality of sensors includes a respective sensor network interface. The system includes a plurality of network links communicatively coupling the plurality of sensors to the ECU network interface of the ECU via the respective sensor network interface in a ring configuration.

Abstract: Aspects of this technical solution can include transmitting, from a first vehicle to a second vehicle by a communication interface coupled with the first vehicle and the second vehicle, first data indicating a positioning of an antenna of a location sensor of the first vehicle, transmitting, from the first vehicle to the second vehicle by the communication interface, second data of the first vehicle indicating a physical location of the first vehicle, during movement of the first vehicle caused by the second vehicle, and receiving, at the first vehicle from the second vehicle by the communication interface in response to the movement of the first vehicle, third data generated at the second vehicle based on the first data and the second data, the third data corresponding to a calibration of the physical location of the first vehicle.

Abstract: A system can include one or more processors. The processors can be configured to receive, from a vehicle via first one or more sensors of the vehicle, first images or video of a physical environment surrounding the vehicle; receive, from an energy supply station via second one or more sensors of the energy supply station, second images or video of the physical environment surrounding the vehicle; and responsive to receiving the first images or video and the second images or video, generate a virtual interface depicting the physical environment based on the first images or video and the second images or video, the virtual interface enabling a user to provide input to control a mechanical arm of the energy supply station to supply energy to the vehicle.

Abstract: Aspects of this technical solution can include a method of calibration of a positioning system of a vehicle. The method can include transmitting, from a first vehicle at a second vehicle by a communication interface coupled with the first vehicle and the second vehicle, first data indicating a direction of gravity relative to a physical orientation of the first vehicle. The method can include transmitting, from the first vehicle at the second vehicle by the communication interface, second data of the first vehicle indicating the physical orientation of the first vehicle, during movement of the first vehicle caused by the second vehicle. And the method can include receiving, at the first vehicle from the second vehicle during the movement of the first vehicle and based on the first data and the second data, third data corresponding to a calibration of the physical orientation of the first vehicle.

Abstract: An autonomous vehicle can include first one or more sensors located on a first side of the autonomous vehicle; second one or more sensors located on a second side of the autonomous vehicle; and one or more processors. The can processors be configured to automatically control, using images generated by the first one or more sensors, the autonomous vehicle to an energy supply station responsive to determining to resupply energy to the autonomous vehicle; detect, using the images generated by the first one or more sensors, an arrival of the autonomous vehicle at the energy supply station; and responsive to detecting the arrival of the autonomous vehicle at the energy supply station, switch from processing images generated by the first one or more sensors to processing images generated by the second one or more sensors.

Abstract: Aspects of this technical solution can include a method of calibration of a inertial navigation system of a vehicle. The method can include obtaining, from a first vehicle at a second vehicle by a communication interface coupled with the first vehicle and the second vehicle, with first data indicating a direction of gravity relative to a physical orientation of the first vehicle. The method can include obtaining, from the first vehicle at the second vehicle by the communication interface, with second data of the first vehicle indicating the physical orientation of the first vehicle, during movement of the first vehicle caused by the second vehicle. And the method can include generating, at the second vehicle during the movement of the first vehicle and based on the first data and the second data, third data corresponding to a calibration of the physical orientation of the first vehicle.

Abstract: Aspects of this technical solution can include obtaining, from a first vehicle at a second vehicle by a communication interface coupled with the first vehicle and the second vehicle, first data indicating a positioning of an antenna of a location sensor of the first vehicle, obtaining, from the first vehicle at the second vehicle by the communication interface, second data of the first vehicle indicating a physical location of the first vehicle, during movement of the first vehicle caused by the second vehicle, and generating, at the second vehicle during the movement of the first vehicle and based on the first data and the second data, third data corresponding to a calibration of the physical location of the first vehicle.

Abstract: An autonomous vehicle can include a sensor and one or more processors. The processors can be configured to automatically control the autonomous vehicle to an energy supply station responsive to determining to resupply energy to the autonomous vehicle; detect, via the sensor, an object corresponding to the energy supply station, the object indicating to initiate energy supply to the autonomous vehicle; responsive to detecting the object, control the autonomous vehicle to stop at a defined position relative to the energy supply station; and open an energy input receptacle of the autonomous vehicle to receive energy from the energy supply station.

Abstract: Embodiments herein include an automated vehicle performing localization functions using particle scoring and particle filters. The automated vehicle performs a phase correlation operation that transforms image data of a sensed map and pre-stored base map from a spatial to frequency domain and combines the transformed maps to generate image data of a correlation map. Estimated location information of particles are compared against sensed data or other data in sensed sub-maps or the correlation map. The automated vehicle may apply an image-convolution scoring map by combining image data of the sensed and base maps in the spatial domain. The autonomy system may calculate entropies for the correlation map and image-convolution scoring map and combines these maps based upon the respective entropies.

Abstract: Embodiments described herein implement an improved autonomy system with a beneficial approach to implementation a localization loop. When the automated vehicle loses access to geolocation data updates, the autonomy system invokes a geo-denied localization loop that performs map localization and motion estimation functions without geolocation data. The localization loop feeds map localizer outputs into a motion estimator of the INS and/or the IMU, and feeds motion estimation outputs from the motion estimator back into the map localizer. When executing the localization loop, the autonomy system detects outlier measurements as errors in the map localizer and mitigates the errors in the map localizer or the motion estimator. The autonomy system executes programming in an error detection phase for monitoring and detecting errors in the localization loop, and an error mitigation phase for mitigating or resolving errors, such as applying a covariance boosting value on outputted data values.

Abstract: An energy supply station can include a mechanical arm, an energy delivery receptacle, and one or more processors. The processors can be configured to detect an arrival of a vehicle at the energy supply station; transmit an indication of the arrival to a remote computer, causing activation of a virtual interface; and move the mechanical arm, based on input at the virtual interface, to cause the energy delivery receptacle to contact or couple with an energy input receptacle of the vehicle.

Abstract: A vehicle can receive audio data indicating speech from a citizens band (CB) radio channel. The vehicle can determine a location corresponding to information in the speech. The vehicle can determine an event corresponding to the location based on the information in the speech. The vehicle can automatically adjust operation according to the event and the location.

Abstract: A system can receive audio data indicating speech from a citizens band (CB) radio channel. The system can determine a location corresponding to first information in the speech. The system can determine an event at the location based on the first information. The system can predict one or more autonomous vehicles that have a path including the location. The system can transmit second information containing the event and the location to the one or more autonomous vehicles. The second information can cause the one or more autonomous vehicles to adjust operation according to the event and the location.

Abstract: A vehicle comprises a sensor configured to capture images and one or more processors. The one or more processors can be configured to receive a single image from the sensor, the single image captured by the sensor as the autonomous vehicle was moving; execute a machine learning model using the single image as input to generate a change in pose of the autonomous vehicle, the machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images; determine a global position of the autonomous vehicle based on the generated change in pose of the autonomous vehicle; and transmit the global position to an autonomous vehicle controller configured to control the autonomous vehicle.