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公开(公告)号：US20250166364A1
公开(公告)日：2025-05-22
申请号：US18518222
申请日：2023-11-22
Applicant: TuSimple, Inc.
Inventor： Hao XIAO , Yiqian GAN , Xin YE , Dongqiangzi YE , JingHao MIAO , Lingting GE
Abstract: Devices, systems, and methods a method for simulating a trajectory of an object are described. An example method includes obtaining a context feature representation corresponding to context information, wherein the context information comprises information describing an environment of the object; obtaining a control feature representation corresponding to control information, wherein the control information comprises information that the simulated trajectory needs to satisfy; determining a latent variable using an input encoder based on the context feature representation and the control feature representation; and determining the simulated trajectory by inputting the latent variable, the context feature representation, and the control feature representation into a decoder.
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公开(公告)号：US20250086828A1
公开(公告)日：2025-03-13
申请号：US18818790
申请日：2024-08-29
Applicant: TuSimple, Inc.
Inventor： Dongqiangzi YE , Yufei XIE , Weijia CHEN , Zixiang ZHOU , Lingting GE
Abstract: An image processing method includes performing, using images obtained from one or more sensors onboard a vehicle, a 2-dimensional (2D) feature extraction; performing, a 3-dimensional (3D) feature extraction on the images; detecting objects in the images by fusing detection results from the 2D feature extraction and the 3D feature extraction.
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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
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.
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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
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.
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