公开(公告)号：US20250085115A1

公开(公告)日：2025-03-13

申请号：US18501362

申请日：2023-11-03

Applicant: TuSimple, Inc.

Inventor： Hao XIAO ,  Yiqian GAN ,  Ethan ZHANG ,  Xin YE ,  Yizhe ZHAO ,  Zhe HUANG ,  Lingting GE ,  Robert August ROSSI, JR.

IPC: G01C21/30

Abstract: A computer-implemented method of trajectory prediction includes obtaining a first cross-attention between a vectorized representation of a road map near a vehicle and information obtained from a rasterized representation of an environment near the vehicle by processing through a first cross-attention stage; obtaining a second cross-attention between a vectorized representation of a vehicle history and information obtained from the rasterized representation by processing through a second cross-attention stage; operating a scene encoder on the first cross-attention and the second cross-attention; operating a trajectory decoder on an output of the scene encoder; obtaining one or more trajectory predictions by performing one or more queries on the trajectory decoder.

公开(公告)号：US20250074463A1

公开(公告)日：2025-03-06

申请号：US18434630

申请日：2024-02-06

Applicant: TuSimple, Inc.

Inventor： Ethan ZHANG ,  Hao XIAO ,  Yiqian GAN ,  Yizhe ZHAO ,  Zhe HUANG ,  Lingting GE

IPC: B60W60/00 ,  B60W30/18 ,  B60W50/00 ,  G06N3/0442 ,  G06N3/0464

Abstract: A method of predicting vehicle trajectory includes operating a scene encoder on an environmental representation surrounding a vehicle; concatenating an output of the scene encoder with a history trajectory; applying a sequence encoder to a result of the concatenating; refining an output of the sequence encoder based on the history trajectory; and generating one or more predicted future trajectories by operating a decoder on an output of the refining.

公开(公告)号：US20250054286A1

公开(公告)日：2025-02-13

申请号：US18475988

申请日：2023-09-27

Applicant: TuSimple, Inc.

Inventor： Zhe HUANG ,  Lingting GE ,  Yizhe ZHAO

IPC: G06V10/80 ,  G06V10/77 ,  G06V20/58

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.

公开(公告)号：US20250029274A1

公开(公告)日：2025-01-23

申请号：US18488657

申请日：2023-10-17

Applicant: TuSimple, Inc.

Inventor： Yizhe ZHAO ,  Zhe CHEN ,  Ye FAN ,  Lingting GE ,  Zhe HUANG ,  Panqu WANG ,  Xue MEI

IPC: G06T7/73 ,  G06V20/58

Abstract: The present disclosure provides methods and systems of sampling-based object pose determination. An example method includes obtaining, for a time frame, sensor data of the object acquired by a plurality of sensors; generating a two-dimensional bounding box of the object in a projection plane based on the sensor data of the time frame; generating a three-dimensional pose model of the object based on the sensor data of the time frame and a model reconstruction algorithm; generating, based on the sensor data, the pose model, and multiple sampling techniques, a plurality of pose hypotheses of the object corresponding to the time frame, generating a hypothesis projection of the object for each of the pose hypotheses by projecting the pose hypothesis onto the projection plane; determining evaluation results by comparing the hypothesis projections with the bounding box; and determining, based on the evaluation results, an object pose for the time frame.

公开(公告)号：US20230065647A1

公开(公告)日：2023-03-02

申请号：US17822735

申请日：2022-08-26

Applicant: TUSIMPLE, INC.

Inventor： Scott Douglas FOSTER ,  Neil M. OVERMON ,  Erik Orlando PORTILLO ,  Zhujia SHI ,  Joyce TAM ,  Mohammad POORSARTEP ,  Christopher MILLER ,  Pengji DUAN ,  Lingting GE ,  Navid SARMADNIA ,  Panqu PANQU WANG ,  Zhe HUANG

IPC: G08G1/0965 ,  G08G1/0967 ,  G01S15/58 ,  G01S15/931 ,  G06V20/58 ,  G06N3/08

Abstract: An autonomous vehicle includes audio sensors configured to detect audio in an environment around the autonomous vehicle and to generate audio signals based on the detected audio. A processor in the autonomous vehicle receives the audio signals and compares a time domain or frequency domain representation of the audio signals to a corresponding representation of a known emergency vehicle siren. The comparison causes the processor to output a first determination indicating whether the audio signals are indicative of an emergency vehicle siren. The processor also applies a trained neural network to the audio signals that causes the processor to output a second determination indicating whether the audio signals are indicative of the emergency vehicle siren. If the first determination or the second determination indicates presence of an emergency vehicle siren in the environment around the autonomous vehicle, the autonomous vehicle is caused to perform an action.