An equipment inspection system receives data captured by a sensor of an autonomous vehicle (AV). The captured data describes a current state of equipment for servicing the AV. The equipment inspection system compares the captured data to a model describing an expected state of the equipment. The equipment inspection system determines, based on the comparison, that the equipment differs from the expected state. The equipment inspection system may transmit data describing the current state of the equipment to an equipment manager. The equipment manager may schedule maintenance for the equipment based on the current state of the equipment.

Systems and methods for controlling autonomous vehicles are provided. Assisted autonomy tasks facilitated by operators for a plurality of autonomous vehicles can be tracked in order to generate operator attributes for each of a plurality of operators. The attributes for an operator can be based on tracking one or more respective assisted autonomy tasks facilitated by the operator. The operator attributes can be used to facilitate enhanced remote operations for autonomous vehicles. For example, request parameters can be obtained in response to a request for remote assistance associated with an autonomous vehicle. An operator can be selected to assist with autonomy tasks for the autonomous vehicle based at least in part on the operator attributes for the operator and the request parameters associated with the request. Remote assistance for the first autonomous vehicle can be initiated, facilitated by the first operator in response to the request for remote assistance.

Provided is an information processing apparatus including: a motion control unit (107) that controls a motion of an autonomous moving body (10), in which, when transmitting/receiving internal data related to the autonomous moving body, the motion control unit causes the autonomous moving body to express execution of the transmission/reception of the internal data by an action.

Provided is an information processing apparatus including: a motion control unit (107) that controls a motion of an autonomous moving body (10), in which, when transmitting/receiving internal data related to the autonomous moving body, the motion control unit causes the autonomous moving body to express execution of the transmission/reception of the internal data by an action.

Techniques and systems are disclosed for using swept volume profile data cached in association with a PRM to improve various aspects of motion planning for a robot. In some implementations, a first probabilistic road map representing possible paths to be travelled by a robot within a physical area is generated. An initial path for the robot within the first probabilistic road map is determined. Data indicating a second probabilistic road map representing a path to be travelled by a movable object within the physical area is obtained. A potential obstruction associated with one or more edges included in the subset of edges is detected. An adjusted path for the robot within the first probabilistic road map is then determined based on the potential obstruction.

Techniques and systems are disclosed for using swept volume profile data cached in association with a PRM to improve various aspects of motion planning for a robot. In some implementations, a first probabilistic road map representing possible paths to be travelled by a robot within a physical area is generated. An initial path for the robot within the first probabilistic road map is determined. Data indicating a second probabilistic road map representing a path to be travelled by a movable object within the physical area is obtained. A potential obstruction associated with one or more edges included in the subset of edges is detected. An adjusted path for the robot within the first probabilistic road map is then determined based on the potential obstruction.

A system, a transportation vehicle, a network component, apparatuses, methods, and computer programs for a transportation vehicle and a network component. The method for a transportation vehicle to determine a route section includes operating the transportation vehicle in an automated driving mode and determining an exceptional traffic situation. The method also includes transmitting information related to the exceptional traffic situation to a network component using a mobile communication system and receiving information related to driving instructions for the route section to overcome the exceptional traffic situation from the network component, wherein the receiving of the driving instructions includes tele-operating the transportation vehicle along the route section to overcome the exceptional traffic situation.

A travel control device is configured to make an autonomous driving vehicle travel in such a way that the autonomous driving vehicle arrives at a specified position specified by a user who intends to board the autonomous driving vehicle, when the autonomous driving vehicle has reached a predetermined range from the specified position, transmit an information sending request notifying the user terminal that the autonomous driving vehicle has reached a vicinity of the specified position and requesting sending of position identifying information for identifying a position at which the user intends to board the autonomous driving vehicle to the user terminal via a communication circuit, and when receiving the position identifying information via the communication circuit, change a position of the autonomous driving vehicle, based on the position identifying information in such a way that the autonomous driving vehicle comes close to the user.

Techniques are described for compensating for movements of sensors. A method includes receiving two sets of sensor data from two sets of sensors, where a first set of sensors are located on a roof of a cab of a semi-trailer truck and a second set of sensor data are located on a hood of the semi-trailer truck. The method also receives from a height sensor a measured value indicative of a height of the rear of a rear portion of the cab of the semi-trailer truck relative to a chassis of the semi-trailer truck, determines two correction values, one for each of the two sets of sensor data, and compensates for the movement of the two sets of sensors by generating two sets of compensated sensor data. The two sets of compensated sensor data are generated by adjusting the two sets of sensor data based on the two correction values.

A computer-implemented method for controlling a vehicle includes receiving, via a processor, from two or more IX control devices disposed at a two or more stationary positions having known latitudes longitudes and orientations, first sensory data identifying the position and dimensions of a feature in a mapped region. The processor generates a plurality of IX nodes based on the first sensory data received from the IX control devices, and receives LiDAR point cloud that includes LiDAR and other vehicle sensory device data such as Inertial Measurement Unit (IMU) data received from a Vehicle (AV) driving in the mapped region. The LiDAR point cloud includes a simultaneous localization and mapping (SLAM) map having second dimension information and second position information associated with the feature in the mapped region. The processor generates, without GPS and/or real-time kinematics information, an optimized High-Definition (HD) map having Absolute accuracy using batch optimization and map smoothing.