公开(公告)号：US20230182774A1

公开(公告)日：2023-06-15

申请号：US18070102

申请日：2022-11-28

Applicant: TuSimple, Inc.

Inventor： Panqu WANG ,  Yu WANG ,  Xiangchen ZHAO ,  Dongqiangzi YE

IPC: B60W60/00 ,  B60W40/04 ,  G06V20/70 ,  G06V20/58 ,  G01S17/89 ,  G01S17/931

CPC classification number: B60W60/0011 ,  B60W40/04 ,  G01S17/89 ,  G01S17/931 ,  G06V20/58 ,  G06V20/70 ,  B60W2300/145 ,  B60W2420/42 ,  B60W2420/52 ,  B60W2556/20

Abstract: 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.

公开(公告)号：US20250086802A1

公开(公告)日：2025-03-13

申请号：US18434501

申请日：2024-02-06

Applicant: TuSimple, Inc.

Inventor： Dongqiangzi YE ,  Zixiang ZHOU ,  Weijia CHEN ,  Yufei XIE ,  Yu WANG ,  Panqu WANG ,  Lingting GE

IPC: G06T7/10 ,  G06T9/00 ,  G06T17/00

Abstract: A method of processing point cloud information includes converting points in a point cloud obtained from a lidar sensor into a voxel grid, generating, from the voxel grid, sparse voxel features by applying a multi-layer perceptron and one or more max pooling layers that reduce dimension of input data; applying a cascade of an encoder that performs a N-stage sparse-to-dense feature operation, a global context pooling (GCP) module, and an M-stage decoder that performs a dense-to-sparse feature generation operation. The GCP module bridges an output of a last stage of the N-stages with an input of a first stage of the M-stages, where N and M are positive integers. The GCP module comprises a multi-scale feature extractor; and performing one or more perception operations on an output of the M-stage decoder and/or an output of the GCP module.