Abstract: Methods and apparatus consistent with the present disclosure may compare position data associated with a Light Detection and Ranging (LiDAR) device and location data associated with a radar device. Changes in relative locations of objects detected along a roadway over time may be used to identify whether sensing capabilities of a sensing apparatus are acceptable. Changes in positions detected by operation of a LiDAR device and locations detected by operation of a radar device may be considered normal or acceptable when those changes are below a threshold value. In instances when changes in relative positions/locations detected by operation the LiDAR device and the radar device at an AV change beyond a threshold value, a sensing system may be considered as being out of calibration. Once a processor detects that a sensing system has changed close to or beyond a threshold level, that processor may initiate a corrective action.

Abstract: System, methods, and computer-readable media for a calibration check process of sensors on an autonomous vehicle (AV) via a drive-through environment mapped with a series of targets. The targets are used to check the calibration of the sensors on the AV as a quality check before the AV is determined fit to drive autonomously. Supervising a calibration process that collects data from the sensors on the AV based on exposure to a series of targets or augmented camera targets. The collected data may be used for extrinsic calibration aimed to determine that extrinsic parameters that define the rigid relationship between sensors are in set checker bounds.

Abstract: System, methods, and computer-readable media for a calibration check process of sensors on an autonomous vehicle (AV) via a drive-through environment mapped with a series of targets. The targets are used to check the calibration of the sensors on the AV as a quality check before the AV is determined fit to drive autonomously. Supervising a calibration process that collects data from the sensors on the AV based on exposure to a series of targets or augmented camera targets. The collected data may be used for extrinsic calibration aimed to determine that extrinsic parameters that define the rigid relationship between sensors are in set checker bounds.

Abstract: System, methods, and computer-readable media for extrinsically calibrating sensors on an autonomous vehicle (AV) by leveraging map data. By leveraging offline data, such as the map data on the AV, the extrinsic calibration of the sensors may be performed on road to update or check previous calibrations. Static objects are determined in the scene by leveraging a map with a semantic map layer and sensors are extrinsically calibrating using a portion of a set of static points associated with the determined static objects by determining relative positions and angles the sensors.

Abstract: Methods and apparatus consistent with the present disclosure may compare position data associated with a Light Detection and Ranging (LiDAR) device and location data associated with a radar device. Changes in relative locations of objects detected along a roadway over time may be used to identify whether sensing capabilities of a sensing apparatus are acceptable. Changes in positions detected by operation of a LiDAR device and locations detected by operation of a radar device may be considered normal or acceptable when those changes are below a threshold value. In instances when changes in relative positions/locations detected by operation the LiDAR device and the radar device at an AV change beyond a threshold value, a sensing system may be considered as being out of calibration. Once a processor detects that a sensing system has changed close to or beyond a threshold level, that processor may initiate a corrective action.