Ford Global Technolgoies, LLC

Uatc, LLC

Aurora Operations, Inc.

Volkswagen Group of America Investments, LLC

Hatem Alismail

Peter Hansen

Richard L Kwant

Dikpal Reddy

Hsin Miao

Parul Kothari

Michael Schoenberg

Methods and systems calibrating LiDAR sensors mounted on a vehicle and having non-overlapping fields of view (FOVs). The methods include collecting first sensor data corresponding to a calibration environment from a first LiDAR sensor mounted on the vehicle, and second sensor data corresponding to the calibration environment from a second sensor LiDAR sensor mounted on the vehicle. The methods also include transforming the first sensor data to generate a first aligned frame in a global reference frame, and transforming the second sensor data to generate a second aligned frame in the global reference frame. The first reference frame is aligned with the second reference frame to extrinsically calibrate the first LiDAR sensor and the second LiDAR sensor.

Systems and methods for calibration and validation of range sensors and LiDAR sensors mounted on a vehicle that have non-overlapping fields of view (FOV)

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.

Systems and methods for autonomous vehicle sensor calibration and validation

A system such as an autonomous vehicle's perception system will identify and classify an obstruction in a field of view of an image capturing device. The system will receive a sequence of image frames from the image capturing device. For each of the image frames, the system will segment the image frame into a regions of interest (ROIs), and the system will use a classifier to assign a classification to each ROI. The classification indicates whether the ROI is clear or obstructed. The system will aggregate the classifications for each ROI to determine an aggregate classification. When an obstructed classification persists for a threshold number of image frames, the system will classify the image capturing device as obstructed, and it will generate a function request that, when executed, will cause a system of which the image capturing device is a component to perform a function.

Systems and methods for vehicle camera obstruction detection

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.

Systems and methods for validating camera calibration in real-time

A system for determining a pose of a vehicle and building maps from vehicle priors processes received ground intensity LIDAR data including intensity data for points believed to be on the ground and height information to form ground intensity LIDAR (GIL) images including pixels in 2D coordinates where each pixel contains an intensity value, a height value, and x- and y-gradients of intensity and height. The GIL images are formed by filtering aggregated ground intensity LIDAR data falling into a same spatial bin on the ground and using a registration algorithm to align two GIL images relative to one another by estimating a 6-degree-of-freedom pose with associated uncertainty that minimizes error between the two GIL images. The aligned GIL images are provided as a pose estimate to a localizer. The system may provide online localization and pose estimation, prior building, and prior to prior alignment pose estimation using image-based techniques.

Ground intensity LIDAR localizer

Lidar intensity calibration from multi-beam lidar sensors uses calibration panels with known reflectance. The intensity response curves of each lidar beam are learned and modeled as a function of the known reflectivity, the transmission power level, and the distance. Contrary to the lidar equation, characterization of multi-beam lidar scanners commonly used for autonomous driving applications reveals that intensity does not fall-off according to the theoretically expected inverse distance-squared. Instead, a maximum intensity response is attained at a specific distance (focal distance). While the focal distance varies across beams, it is independent of the transmission power. Outside of the focal distance, distance intensity exhibits a sharp fall off. The intensity response curve of the lidar is well-approximated by a parametric form. Learned splines as functions of distance and power level determine the most likely mapping from raw data to surface reflectance along with a measure of uncertainty.

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Pittsburgh, PA, United States of America

Hatem Alismail