Tusimple, Inc.

Chenyang Li

Xue Mei

Xiaodi Hou

Siyuan Liu

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

A vehicle position and velocity estimation based on camera and LIDAR data are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device; determining a two-dimensional (2D) position of a proximate object near the autonomous vehicle using the image data received from the image generating device; tracking a three-dimensional (3D) position of the proximate object using the distance data received from the distance measuring device over a plurality of cycles and generating tracking data; determining a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data; determining a velocity of the proximate object using the 3D position and the tracking data; and outputting the 3D position and velocity of the proximate object relative to the autonomous vehicle.

A method of LiDAR-based vehicle tracking 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 including taking point clusters that belong to vehicles in a single LiDAR frame as input, tracking each vehicle as a probabilistic distribution, assigning each tracked vehicle to a corresponding one of the point clusters, and recalculating the probabilistic distribution of each tracked vehicle.

Tracking and modeling processing of image data for LiDAR-based vehicle tracking system and method

A method of LiDAR-based vehicle tracking 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 including: calculating the probability of a cluster pair that belongs to a same vehicle using tracking information from a previous frame and spatial information from current frame, determining that the clusters of the cluster pair are merged into one by taking both spatial information and tracking information into consideration, and merging the clusters into a single, complete cluster.

Point cluster refinement processing of image data for LiDAR-based vehicle tracking system and method

A method of LiDAR-based vehicle tracking 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: projecting points in a processed point cloud to a unit sphere, for each of the projected points, determining that a distance between one and each of its neighboring points is smaller than a threshold, and forming a cluster of the one and its neighboring points.

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San Diego, CA, United States of America

Chenyang Li