Abstract: A method includes receiving detection data and Inertial Measurement Unit (IMU) data from a vehicle as the vehicle traverses a testing lane, wherein the detection data corresponds to lidar detections, camera detections, and radar detections, by one or more sensors of the vehicle, and the IMU data corresponds to a determination of one or more motion constraints from the one or more sensors and an IMU and a determination of one or more constraints between the IMU and the vehicle to generate. A plurality of targets are arranged with respect to the testing lane and have a plurality of calibration elements configured to allow detection by the one or more sensors of the vehicle. The method further includes processing the received detection data and IMU data using a calibration algorithm, and generating one or more calibration parameters for the one or more sensors of the vehicle based on the processing.

Abstract: A vehicle computer can access a stored map. The map includes respective vehicle operating permissions for zones included in the map. A changed vehicle operating permission for one of the zones included in the map may be received. When the vehicle is within a specified distance of the one of the zones, the vehicle is operated according to the changed operating permission. The changed vehicle operating permission can downgrade autonomous control of vehicle speed and/or steering.

Abstract: Disclosed herein are systems, methods, and computer program products for generating and using map information. For example, the method includes: identifying data collection mission area(s) (DCMAs) within a geographic location that is to be covered by robotic device(s) during a data collection mission (DCM); generating a route to be traversed by robotic device(s) in DCMAs (the route being configured to cause robotic device(s) to traverse each two-way road at least one time in two opposing directions); causing robotic device(s) to perform DCM by following the route and collecting data; causing robotic device(s) to discontinue collecting data in response to a trigger event; and using the data collected during DCM to generate or update the map information. The map information may be used to facilitate controlled movement of a vehicle.

Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to plot three-dimensional positions of labeled cuboids from successive frames between a first keyframe and a second keyframe of a densely labeled video sequence. A sparsely labeled video sequence can be generated by removing labeled cuboids from the densely labeled video sequence with positions that are within a user-selected distance from a line extending from a position of the labeled cuboid in the first keyframe to a position of the labeled cuboid in the second keyframe. The sparsely labeled video sequence can be output for training a machine learning system.

Abstract: Methods and systems for determining whether a camera of an autonomous vehicle (AV) is calibrated are disclosed. The method includes determining a relative positional range for a calibration target with respect to the AV, capturing a plurality of images of the calibration target, using the camera when the calibration target and the AV are positioned within the relative positional range, measuring a camera-based calibration factor and a motion-based validation factor based on the plurality of images for generating a confidence score, and generating a signal indicating that the camera is not calibrated when the confidence score is below a threshold.

Abstract: This document discloses system, method, and computer program product embodiments for preparing a fleet of vehicles for service. For example, the method includes receiving: a set of mission types, a set of mission requests, vehicle information related to vehicles of a fleet, and personnel information. The method further includes applying the set of mission types and the set of mission requests to one or more priority engines to produce a prioritized list of mission requests. The method further includes applying the received vehicle information, the received personnel information, and the prioritized list of mission requests to one or more rules engines to produce a proposed schedule of missions for a period of time, generating software and/or map data for one or more of the vehicles, and providing the generated software and/or map data to the vehicles prior to the period of time.

Abstract: This document discloses system, method, and computer program product embodiments for preparing a fleet of vehicles for service. For example, the method includes receiving: a set of mission types, a set of mission requests, vehicle information related to vehicles of a fleet, and personnel information. The method further includes applying the set of mission types and the set of mission requests to one or more priority engines to produce a prioritized list of mission requests. The method further includes applying the received vehicle information, the received personnel information, and the prioritized list of mission requests to one or more rules engines to produce a proposed schedule of missions for a period of time, generating software and/or map data for one or more of the vehicles, and providing the generated software and/or map data to the vehicles prior to the period of time.

Abstract: Methods and systems for calibrating sensors of an autonomous vehicle are disclosed. The method includes using a target that includes a plurality of uniquely identifiable fiducials positioned on a panel to form a pattern, and at least one tag. Each tag corresponds to and is positioned relative one of the plurality of uniquely identifiable fiducials and includes information for determining a location of its corresponding uniquely identifiable fiducial with respect to the panel.

Abstract: Methods and systems for determining whether a camera of an autonomous vehicle (AV) is calibrated are disclosed. The method includes determining a relative positional range for a calibration target with respect to the AV, capturing a plurality of images of the calibration target, using the camera when the calibration target and the AV are positioned within the relative positional range, measuring a camera-based calibration factor and a motion-based validation factor based on the plurality of images for generating a confidence score, and generating a signal indicating that the camera is not calibrated when the confidence score is below a threshold.

Abstract: Disclosed herein are systems, methods, and computer program products for generating and using map information. For example, the method includes: identifying data collection mission area(s) (DCMAs) within a geographic location that is to be covered by robotic device(s) during a data collection mission (DCM); generating a route to be traversed by robotic device(s) in DCMAs (the route being configured to cause robotic device(s) to traverse each two-way road at least one time in two opposing directions); causing robotic device(s) to perform DCM by following the route and collecting data; causing robotic device(s) to discontinue collecting data in response to a trigger event; and using the data collected during DCM to generate or update the map information. The map information may be used to facilitate controlled movement of a vehicle.