Tusimple, Inc.

Xiangchen Zhao

Panqu Wang

Pengfei Chen

Xiaoling Han

Zehua Huang

Xiaodi Hou

Charles A Price

Lingting Ge

Chenzhe Qian

Zhujia Shi

Tian Li

Lei Nie

Scott Douglas Foster

Dishi Li

Hunter Scott Willoughby

Shuhan Yang

Joshua Miguel Rodriguez

Chiyu Zhang

Dongqiangzi Ye

Yizhe Zhao

Long Sha

Rundong Ge

Fangjun Zhang

Junliang Zhang

Yu Wang

A unified framework for detecting perception anomalies in autonomous driving systems is described. The perception anomaly detection framework takes an input image from a camera in or on a vehicle and identifies anomalies as belonging to one of three categories. Lens anomalies are associated with poor sensor conditions, such as water, dirt, or overexposure. Environment anomalies are associated with unfamiliar changes to an environment. Finally, object anomalies are associated with unknown objects. After perception anomalies are detected, the results are sent downstream to cause a behavior change of the vehicle.

Perception anomaly detection for autonomous driving

Autonomous vehicles can include systems and apparatus for performing signal processing on point cloud data from Light Detection and Ranging (LiDAR) devices located on the autonomous vehicles. A method includes obtaining, by a computer located in an autonomous vehicle, a combined point cloud data that describes a plurality of areas of an environment in which the autonomous vehicle is operating; determining that a first set of points from the combined point cloud data are located within fields of view of cameras located on the autonomous vehicle; assigning one or more labels to a second set of points from the first set of points in response to determining that the second set of points are located within bounding box(es) around object(s) in images obtained from the cameras; and causing the autonomous vehicle to operate based on characteristic(s) of the object(s) determined from the second set of points.

Autonomous driving LiDAR technology

An autonomous vehicle includes an under-chassis object detection system for identifying the presence of an object on a road that the autonomous vehicle is travelling upon. The under-chassis object detection system may include a LIDAR system. The object on the road that the autonomous vehicle is travelling upon is of a size that allows the vehicle's chassis to pass over the object on the road. The autonomous vehicle may react to the detected object on the road to operate the autonomous vehicle safely, such as by altering the vehicle's trajectory, by stopping the vehicle, or by communicating with a control center for further instructions.

Detection of small objects under an autonomous vehicle chassis

A system installed in a vehicle includes a first group of sensing devices configured to allow a first level of autonomous operation of the vehicle; a second group of sensing devices configured to allow a second level of autonomous operation of the vehicle, the second group of sensing devices including primary sensing devices and backup sensing devices; a third group of sensing devices configured to allow the vehicle to perform a safe stop maneuver; and a control element communicatively coupled to the first group of sensing devices, the second group of sensing devices, and the third group of sensing devices. The control element is configured to: receive data from at least one of the first group, the second group, or the third group of sensing devices, and provide a control signal to a sensing device based on categorization information indicating a group to which the sensing device belongs.

Sensor layout techniques

A system and method for multitask processing for autonomous vehicle computation and control includes: receiving training image data from a training image data collection system; performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting common features from the training image data, causing the plurality of tasks to generate task-specific predictions based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including extracting common features from the image data, causing the plurality of trained tasks to concurrently generate task-specific predictions based on the image data.

System and method for multitask processing for autonomous vehicle computation and control

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San Diego, CA, United States of America

Xiangchen Zhao