Gm Cruise Holdings LLC

Gm Global Technolgoy Operations, Inc.

Adam Samuel Cadien

Ankit Rohatgi

Elliot Branson

Andrea Allais

Micah Christopher Chambers

William Gongshu Xie

Daniel H Lee

Zhi Yuan Sherwin Lau

Haven Lau

Nick Semansky

Zhichang Yan

Nicholas Semansky

Jun-Wei Hew

Robert Vets

Zhonghua Ma

Peter Megson

Jabeer Ahmed

Mariana Rihl

Radu Raduta

Zhizhong Yan

Elisha Ross Atkins

Zhichang (Henry) Yan

Zhizhong (Joe) Yan

Sensors coupled to a vehicle are calibrated, optionally using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. One vehicle sensor captures a representation of one feature of a sensor target, while another vehicle sensor captures a representation of a different feature of the sensor target, the two features of the sensor target having known relative positioning on the target. The vehicle generates a transformation that maps the captured representations of the two features to positions around the vehicle based on the known relative positioning of the two features on the target.

Extrinsic calibration of multiple vehicle sensors using combined target detectable by multiple vehicle sensors

Various aspects of the subject technology relate to a lidar validation system. The lidar validation system is configured to acquire one or more lidar point clouds from a lidar unit, wherein the lidar unit comprises one or more lasers, and wherein each point cloud is a representation of a scene according to a laser in the lidar unit, the scene comprising a fiducial target. The lidar validation system is configured to generate, based on points in the one or more point clouds, a target cloud that correspond to the fiducial target, perform a rigid body processing to minimize a sum of distances from each point in the target cloud to the fiducial target, and generate lidar validation measurements based on the distances from each point in the target cloud to the fiducial target.

Lidar sensor validation

An autonomous vehicle is described herein. The autonomous vehicle includes a lidar sensor system. The autonomous vehicle additionally includes a computing system that executes a lidar segmentation system, wherein the lidar segmentation system is configured to identify objects that are in proximity to the autonomous vehicle based upon output of the lidar sensor system. The computing system further includes a deep neural network (DNN), where the lidar segmentation system identifies the objects in proximity to the autonomous vehicle based upon output of the DNN.

Autonomous vehicle controlled based upon a lidar data segmentation system

Sensors coupled to a vehicle are calibrated using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. The sensors capture data at each orientation along the rotation. The vehicle's computer identifies representations of the sensor targets within the data captured by the sensors, and calibrates the sensor based on these representations. The motorized turntable may confirm that rotation has stopped to the vehicle to trigger sensor capture, and the vehicle may communicate completion of sensor capture at an orientation to the motorized turntable to trigger further rotation.

Control of dynamic scene motion by vehicle based on vehicle sensor capture

Autonomous vehicle controlled based upon a LIDAR data segmentation system

Sensors coupled to a vehicle are calibrated using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. The sensors capture data at each vehicle orientation along the rotation. The vehicle's computer identifies representations of the sensor targets within the data captured by the sensors, and calibrates the sensor based on these representations, for example by comparing these representations to previously stored information about the sensor targets and generating a correction based on differences, the correction applied to post-calibration sensor data.

Vehicle sensor calibration using sensor calibration targets

Systems and methods provide for adaptive real-time streaming of Autonomous Vehicle (AV) data. In some embodiments, the AV can receive a request from the remote computing system for real-time streaming of a first type of AV data and adaptively streaming a second type of AV data when one or more streaming conditions are satisfied. The first type of AV data and the second type of AV data can be captured as raw data by sensors, actuators, transducers, and other components of the AV. The AV can stream the first type of AV data to the remote computing system in real-time for a first time period. When the AV determines the streaming conditions are satisfied, the AV can automatically determine the second type of AV data to stream to the remote computing system in real-time for a second time period.

Partial calibration target detection for improved vehicle sensor calibration

An autonomous vehicle is described herein. The autonomous vehicle includes a lidar sensor system. The autonomous vehicle additionally includes a computing system that executes a lidar segmentation system, wherein the lidar segmentation system is configured to identify objects that are in proximity to the autonomous vehicle based upon output of the lidar sensor system. The computing system further includes a deep neural network (DNN), where the lidar segmentation system identifies the objects in proximity to the autonomous vehicle based upon output of the DNN. The computing system is further configured to align a heightmap to lidar data output by the lidar sensor system based upon output of the DNN. The lidar segmentation system can identify objects in proximity to the autonomous vehicle based upon the aligned heightmap.

Autonomous vehicle controlled based upon a lidar data segmentation system and an aligned heightmap

Compensation in vehicle sensor calibration for sloped vehicle calibration environment

Sensors coupled to a vehicle are calibrated using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. The sensors capture data at each vehicle orientation along the rotation. The vehicle's computer identifies representations of the sensor targets within the data captured by the sensors, and calibrates the sensor based on these representations, for example by generating a transformation based on mapping positions of these representations in the data to known positions of the sensor targets, such as to correct lens distortion, the transformation applied to post-calibration sensor data.

Vehicle sensor calibration system using dynamic scene

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San Francisco, CA, United States of America

Adam Samuel Cadien