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：US20210342602A1

公开(公告)日：2021-11-04

申请号：US17377206

申请日：2021-07-15

Applicant: TUSIMPLE, INC.

Inventor： Zhipeng YAN ,  Lingting GE ,  Pengfei CHEN ,  Panqu WANG

IPC: G06K9/00 ,  G08G1/04 ,  G06T3/00 ,  G06K9/62

Abstract: A system and method for lateral vehicle detection is disclosed. A particular embodiment can be configured to: receive lateral image data from at least one laterally-facing camera associated with an autonomous vehicle; warp the lateral image data based on a line parallel to a side of the autonomous vehicle; perform object extraction on the warped lateral image data to identify extracted objects in the warped lateral image data; and apply bounding boxes around the extracted objects.

公开(公告)号：US20210216792A1

公开(公告)日：2021-07-15

申请号：US17214828

申请日：2021-03-27

Applicant: TuSimple, Inc.

Inventor： Tian LI ,  Panqu WANG ,  Pengfei CHEN

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06N20/00 ,  G06N3/04 ,  G06N3/08

Abstract: A system and method for instance-level lane detection for autonomous vehicle control are disclosed. A particular embodiment includes: receiving image data from an image data collection system associated with an autonomous vehicle; performing an operational phase comprising extracting roadway lane marking features from the image data, causing a plurality of trained tasks to execute concurrently to generate instance-level lane detection results based on the image data, the plurality of trained tasks having been individually trained with different features of training image data received from a training image data collection system and corresponding ground truth data, the training image data and the ground truth data comprising data collected from real-world traffic scenarios; causing the plurality of trained tasks to generate task-specific predictions of feature characteristics based on the image data and to generate corresponding instance-level lane detection results; and providing the instance-level lane detection results to an autonomous vehicle subsystem of the autonomous vehicle.

公开(公告)号：US20200334476A1

公开(公告)日：2020-10-22

申请号：US16916488

申请日：2020-06-30

Applicant: TUSIMPLE, INC.

Inventor： Panqu WANG ,  Tian Li

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46 ,  G06N3/08 ,  G05D1/00 ,  G05D1/02 ,  G06N3/04

Abstract: A system and method for taillight signal recognition using a convolutional neural network is disclosed. An example embodiment includes: receiving a plurality of image frames from one or more image-generating devices of an autonomous vehicle; using a single-frame taillight illumination status annotation dataset and a single-frame taillight mask dataset to recognize a taillight illumination status of a proximate vehicle identified in an image frame of the plurality of image frames, the single-frame taillight illumination status annotation dataset including one or more taillight illumination status conditions of a right or left vehicle taillight signal, the single-frame taillight mask dataset including annotations to isolate a taillight region of a vehicle; and using a multi-frame taillight illumination status dataset to recognize a taillight illumination status of the proximate vehicle in multiple image frames of the plurality of image frames, the multiple image frames being in temporal succession.

公开(公告)号：US20200082180A1

公开(公告)日：2020-03-12

申请号：US16129040

申请日：2018-09-12

Applicant: TuSimple

Inventor： Panqu WANG

IPC: G06K9/00 ,  G06T7/62 ,  G06T7/80 ,  G06F17/30 ,  G06F15/18

Abstract: A system and method for three-dimensional (3D) object detection is disclosed. A particular embodiment can be configured to: receive image data from at least one camera associated with an autonomous vehicle, the image data representing at least one image frame; use a trained deep learning module to determine pixel coordinates of a two-dimensional (2D) bounding box around an object detected in the image frame; use the trained deep learning module to determine vertices of a three-dimensional (3D) bounding box around the object; use a fitting module to obtain geological information related to a particular environment associated with the image frame and to obtain camera calibration information associated with the at least one camera; and use the fitting module to determine 3D attributes of the object using the 3D bounding box, the geological information, and the camera calibration information.

公开(公告)号：US20190108384A1

公开(公告)日：2019-04-11

申请号：US15725747

申请日：2017-10-05

Applicant: TuSimple

Inventor： Yijie WANG ,  Panqu WANG ,  Pengfei CHEN

IPC: G06K9/00 ,  G06K9/20 ,  G06K9/62 ,  B64C39/02 ,  B64D47/08

CPC classification number: G06K9/0063 ,  B64C39/024 ,  B64C2201/123 ,  B64D47/08 ,  G06K9/00765 ,  G06K9/209 ,  G06K9/3241 ,  G06K9/4652 ,  G06K9/6256 ,  G06K2209/21 ,  G06T7/11 ,  G08G1/012

Abstract: A system and method for aerial video traffic analysis are disclosed. A particular embodiment is configured to: receive a captured video image sequence from an unmanned aerial vehicle (UAV); clip the video image sequence by removing unnecessary images; stabilize the video image sequence by choosing a reference image and adjusting other images to the reference image; extract a background image of the video image sequence for vehicle segmentation; perform vehicle segmentation to identify vehicles in the video image sequence on a pixel by pixel basis; determine a centroid, heading, and rectangular shape of each identified vehicle; perform vehicle tracking to detect a same identified vehicle in multiple image frames of the video image sequence; and produce output and visualization of the video image sequence including a combination of the background image and the images of each identified vehicle.

公开(公告)号：US20190101927A1

公开(公告)日：2019-04-04

申请号：US15721797

申请日：2017-09-30

Applicant: TuSimple

Inventor： Xiangchen ZHAO ,  Tian LI ,  Panqu WANG ,  Pengfei CHEN

IPC: G05D1/02 ,  G05D1/00 ,  G06K9/00

Abstract: A system and method for multitask processing for autonomous vehicle computation and control are disclosed. A particular embodiment includes: receiving training image data from a training image data collection system; obtaining ground truth data corresponding to the training image data; performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting common features from the training image data, causing the plurality of tasks to generate task-specific predictions based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including extracting common features from the image data, causing the plurality of trained tasks to concurrently generate task-specific predictions based on the image data, and output the task-specific predictions to an autonomous vehicle subsystem of the autonomous vehicle.

公开(公告)号：US20190050667A1

公开(公告)日：2019-02-14

申请号：US16159060

申请日：2018-10-12

Applicant: TuSimple

Inventor： Panqu WANG ,  Pengfei CHEN ,  Zehua HUANG

IPC: G06K9/46 ,  G06K9/62 ,  G06K9/00 ,  G06T7/12 ,  G05D1/02

Abstract: A system method for occluding contour detection using a fully convolutional neural network is disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image by semantic segmentation; learning an array of upscaling filters to upscale the feature map into a final dense feature map of a desired size; applying the array of upscaling filters to the feature map to produce contour information of objects and object instances detected in the input image; and applying the contour information onto the input image.

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.

公开(公告)号：US20250046075A1

公开(公告)日：2025-02-06

申请号：US18518156

申请日：2023-11-22

Applicant: TuSimple, Inc.

Inventor： Long SHA ,  Junliang ZHANG ,  Rundong GE ,  Xiangchen ZHAO ,  Fangjun ZHANG ,  Yizhe ZHAO ,  Panqu WANG

IPC: G06V10/98 ,  B60W50/02 ,  B60W50/029 ,  B60W60/00 ,  G06V10/26 ,  G06V10/28 ,  G06V10/48 ,  G06V10/75 ,  G06V20/56

Abstract: A unified framework for detecting perception anomalies in autonomous driving systems is described. The perception anomaly detection framework takes an input image from a camera in or on a vehicle and identifies anomalies as belonging to one of three categories. Lens anomalies are associated with poor sensor conditions, such as water, dirt, or overexposure. Environment anomalies are associated with unfamiliar changes to an environment. Finally, object anomalies are associated with unknown objects. After perception anomalies are detected, the results are sent downstream to cause a behavior change of the vehicle.