Abstract: A method may include receiving at least one camera frame of a first radar target and a second radar target, determining an estimated radar pose based at least in part on the at least one received camera frame, receiving a first radar cross-section (RCS) response from the first radar target and second radar target, determining an estimated elevation angle based at least in part on the first RCS response, and determining an estimated radar angle by refining the estimated radar pose and the estimated elevation angle based on at least in part on the first RCS response.

Abstract: Methods and systems for determining whether a camera of an autonomous vehicle (AV) is calibrated are disclosed. The method includes using image frames captured by a camera of the AV and LIDAR point clouds captured by a LIDAR system of the AV to calculate an AV pose calibration metric. The method also includes: measuring a distance metric between a three-dimensional bounding box around an object and a two-dimensional bounding box in an image captured by the camera; using the AV pose calibration metric and the distance calibration metric to generate a confidence score; and in response to the confidence score being below a threshold, generating a signal indicating that the camera is not calibrated.

Abstract: A filter driving device includes a seat, at least one driving module, at least one filter module, and at least one linking member. The seat has a front side and a rear side, which are opposite to each other, and a light-transmission hole passing through the front side and the rear side. The at least one driving module provided on the front side of the seat. The at least one filter module provided on the rear side of the seat. The at least one linking member which is provided on the seat and connected to the at least one driving module and the at least one filter module; the at least one driving module drives the at least one linking member to move the at least one filter module to switch different light filtering modes for light passing through the light-transmission hole.

Abstract: Systems and methods for generating operating an autonomous vehicle. The methods comprise: obtaining LiDAR point cloud data generated by a LiDAR system of the autonomous vehicle; inspecting the LiDAR point cloud data to infer a health of LiDAR beams; identifying bad quality point cloud data based on the inferred health of the LiDAR beams; removing the bad quality point cloud data from the LiDAR point cloud data to generate modified LiDAR point cloud data; and causing the autonomous vehicle to perform at least one autonomous driving operation or mode change based on the modified LiDAR point cloud data.

Abstract: A method for calibration of vision sensors includes, by a processor: selecting a calibration sequence that has a base calibration pattern and calibration angles, generating a calibrating target that may include the calibration pattern at a selected calibration angle of the angles, and causing a display screen to display a digital image representative of the target at the selected angle relative to an originating border of the selected angle. Each of the calibration angles is associated with a different originating border of the screen. The method includes by a vision sensor capturing at least one image of the calibrating target displayed on the screen. The calibration is repeated for each calibration angle of the sequence. The method includes performing calibration of the vision sensor in response to image signal processing of extracted calibration features in the at least one image of the calibrating targets.