Tusimple, Inc.

Beijing Tusen Weilai Technology Co., Ltd.

Xiaodi Hou

Siyuan Liu

Lingting Ge

Mingdong Wang

Zehua Huang

Xue Mei

Yijie Wang

Xiaoling Han

Charles A Price

Chenzhe Qian

Yiqian Gan

Pengfei Chen

Yujie Wei

Dazhou Guo

Naiyan Wang

Chenyang Li

Nan Yu

Cheng Zhang

Sven Kratz

Wutu Lin

Todd B Skinner

Zhipeng Yan

Ruiliang Zhang

Jianan Hao

Zhujia Shi

Jason Wallace

Cheng Lu

Ming Hsi Wang

Panqu Wang

Yufei Zhao

Jie Sun

Yi Luo

Ke Xu

Lei Nie

Xiangchen Zhao

Shuhan Yang

Kai Zhou

Joshua Miguel Rodriguez

Chiyu Zhang

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 method of lane detection for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: generating a ground truth associated with lane markings expressed in god's view; receiving features from at least one of a hit-map image and a fitted lane marking, wherein the hit-map image includes a classification of pixels that hit a lane marking, and the fitted lane marking includes pixels optimized based on the hit-map image; and training a confidence module based on the features and the ground truth, the confidence module configured to determine on-line whether a fitted lane marking is reasonable, using parameters that express a lane marking in an arc.

Verification module system and method for motion-based lane detection with multiple sensors

Disclosed are distributed computing systems and methods for controlling multiple autonomous control modules and subsystems in an autonomous vehicle. In some aspects of the disclosed technology, a computing architecture for an autonomous vehicle includes distributing the complexity of autonomous vehicle operation, thereby avoiding the use of a single high-performance computing system and enabling off-the-shelf components to be use more readily and reducing system failure rates.

Distributed computing systems for autonomous vehicle operations

Disclosed are devices, systems and methods for processing an image. In one aspect a method includes receiving an image from a sensor array including an x-y array of pixels, each pixel in the x-y array of pixels having a value selected from one of three primary colors, based on a corresponding x-y value in a mask pattern. The method may further include generating a preprocessed image by performing preprocessing on the image. The method may further include performing perception on the preprocessed image to determine one or more outlines of physical objects.

Images for perception modules of autonomous vehicles

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

System and method for large-scale lane marking detection using multimodal sensor data

Disclosed are devices, systems and methods for an audio assistant in an autonomous or semi-autonomous vehicle. In one aspect the informational audio assistant receives a first set of data from a vehicle sensor and identifies an object or condition using the data from the vehicle sensor. Audio is generated representative of a perceived danger of an object or condition. A second set of data from the vehicle sensor subsystem is received and the informational audio assistant determines whether an increased danger exists based on a comparison of the first set of data to the second set of data. The informational audio assistant will apply a sound profile to the generated audio based on the increased danger.

Auditory assistant module for autonomous vehicles

Techniques are described to estimate orientation of one or more cameras located on a vehicle. The orientation estimation technique can include obtaining an image from a camera located on a vehicle while the vehicle is being driven on a road, determining, from a terrain map, a location of a landmark located at a distance from a location of the vehicle on the road, determining, in the image, pixel locations of the landmark, selecting one pixel location from the determined pixel locations; and calculating values that describe an orientation of the camera using at least an intrinsic matrix and a previously known extrinsic matrix of the camera, where the intrinsic matrix is characterized based on at least the one pixel location and the location of the landmark.

Camera orientation estimation

Image processing techniques are described to obtain an image from a camera located on a vehicle while the vehicle is being driven, cropping a portion of the obtained image corresponding to a region of interest, detecting an object in the cropped portion, adding a bounding box around the detected object, determining position(s) of reference point(s) on the bounding box, and determining a location of the detected object in a spatial region where the vehicle is being driven based on the determined one or more positions of the second set of one or more reference points on the bounding box.

Perception system for autonomous vehicles

The present disclosure provides an image capturing control method and apparatus, an image capturing system and a Time-of-Flight (TOF) camera, capable of solving the problem in the related art that a clear image of an object at a specific distance cannot be captured in harsh weather. The method includes: determining infrared light emission time, exposure start time and exposure end time for a TOF camera based on a predetermined target distance range; transmitting, to the TOF camera, parameter control information containing the infrared light emission time, exposure start time and exposure end time; and receiving an image containing an object located within the target distance range that is captured by the TOF camera in accordance with the parameter control information.

Image capturing control method and apparatus, image capturing system and TOF camera

A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

System and method for proximate vehicle intention prediction for autonomous vehicles

A vehicle position and velocity estimation system based on camera and LIDAR data is disclosed. An embodiment includes: receiving input object data from a subsystem of a vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device, the distance measuring device comprising one or more LIDAR sensors; determining a first position of a proximate object near the vehicle from the image data; determining a second position of the proximate object from the distance data; correlating the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data; determining a three-dimensional (3D) position of the proximate object using the correlated first and second positions; and using the 3D position of the proximate object to navigate the vehicle.

System and method for vehicle position and velocity estimation based on camera and LIDAR data

Disclosed are devices, systems and methods for using a rotating camera for vehicular operation. One example of a method for improving driving includes determining, by a processor in the vehicle, that a trigger has activated, orienting, based on the determining, a single rotating camera towards a direction of interest, and activating a recording functionality of the single rotating camera, where the vehicle comprises the single rotating camera and one or more fixed cameras, and where the single rotating camera provides a redundant functionality for, and consumes less power than, the one or more fixed cameras.

Vehicle-based rotating camera methods and systems

A method includes receiving a series of road images from a side-view camera sensor of the autonomous driving vehicle. For each object from objects captured in the series of road images, a series of bounding boxes in the series of road images is generated, and a direction of travel or stationarity of the object is determined. The methods and apparatus also include determining a speed of each object for which the direction of travel has been determined and determining, based on the directions of travel, speeds, or stationarity of the objects, whether the autonomous driving vehicle can safely move in a predetermined direction. Furthermore, one or more control signals is sent to the autonomous driving vehicle to cause the autonomous driving vehicle to move or to remain stationary based on determining whether the autonomous driving vehicle can safely move in the predetermined direction.

Autonomous driving crash prevention

Techniques are described for estimating pose of a camera located on a vehicle. An exemplary method of estimating camera pose includes obtaining, from a camera located on a vehicle, an image including a lane marker on a road on which the vehicle is driven, and estimating a pose of the camera such that the pose of the camera provides a best match according to a criterion between a first position of the lane marker determined from the image and a second position of the lane marker determined from a stored map of the road.

Growing community of inventors

San Diego, CA, United States of America

Xiaodi Hou