公开(公告)号：US10762635B2

公开(公告)日：2020-09-01

申请号：US15623323

申请日：2017-06-14

Applicant: TuSimple, Inc.

Inventor： Zhipeng Yan ,  Zehua Huang ,  Pengfei Chen ,  Panqu Wang

IPC: G06K9/34 ,  G06K9/62 ,  G06T7/11 ,  G06T7/143

Abstract: A system and method for actively selecting and labeling images for semantic segmentation are disclosed. A particular embodiment includes: receiving image data from an image generating device; performing semantic segmentation or other object detection on the received image data to identify and label objects in the image data and produce semantic label image data; determining the quality of the semantic label image data based on prediction probabilities associated with regions or portions of the image; and identifying a region or portion of the image for manual labeling if an associated prediction probability is below a pre-determined threshold.

公开(公告)号：US10685239B2

公开(公告)日：2020-06-16

申请号：US15924249

申请日：2018-03-18

Applicant: TuSimple, Inc.

Inventor： Zhipeng Yan ,  Lingting Ge ,  Pengfei Chen ,  Panqu Wang

IPC: B60W30/00 ,  G05D1/00 ,  G05D1/02 ,  G06K9/00 ,  G06K9/62 ,  G06T3/00 ,  G08G1/04

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：US12242967B2

公开(公告)日：2025-03-04

申请号：US18536677

申请日：2023-12-12

Applicant: TuSimple, Inc.

Inventor： Tian Li ,  Panqu Wang ,  Pengfei Chen

IPC: G06N3/00 ,  G06F18/21 ,  G06F18/214 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06V10/44 ,  G06V10/764 ,  G06V20/56

Abstract: A system and method for instance-level roadway feature 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; extracting roadway features from the image data, causing a plurality of trained tasks to generate instance-level roadway feature 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 roadway feature detection results; and providing the instance-level roadway feature detection results to an autonomous vehicle subsystem to control operation of the autonomous vehicle.

公开(公告)号：US12190465B2

公开(公告)日：2025-01-07

申请号：US18437734

申请日：2024-02-09

Applicant: TUSIMPLE, INC.

Inventor： Zhipeng Yan ,  Pengfei Chen ,  Panqu Wang

IPC: G06T3/047 ,  G05D1/00 ,  G05D1/249 ,  G06T5/20 ,  G06T5/80

Abstract: A system and method for fisheye image processing can be configured to: receive fisheye image data from at least one fisheye lens camera associated with an autonomous vehicle, the fisheye image data representing at least one fisheye image frame; partition the fisheye image frame into a plurality of image portions representing portions of the fisheye image frame; warp each of the plurality of image portions to map an arc of a camera projected view into a line corresponding to a mapped target view, the mapped target view being generally orthogonal to a line between a camera center and a center of the arc of the camera projected view; combine the plurality of warped image portions to form a combined resulting fisheye image data set representing recovered or distortion-reduced fisheye image data corresponding to the fisheye image frame; generate auto-calibration data representing a correspondence between pixels in the at least one fisheye image frame and corresponding pixels in the combined resulting fisheye image data set; and provide the combined resulting fisheye image data set as an output for other autonomous vehicle subsystems.

公开(公告)号：US11967140B2

公开(公告)日：2024-04-23

申请号：US17983129

申请日：2022-11-08

Applicant: TuSimple, Inc.

Inventor： Panqu Wang ,  Pengfei Chen

IPC: G06V10/82 ,  G06F18/2413 ,  G06T7/194 ,  G06V10/20 ,  G06V10/44 ,  G06V10/764 ,  G06V20/56 ,  G06V20/58

CPC classification number: G06V10/82 ,  G06F18/24133 ,  G06T7/194 ,  G06V10/255 ,  G06V10/454 ,  G06V10/764 ,  G06V20/56 ,  G06V20/58 ,  G06T2207/20081 ,  G06T2207/30252

Abstract: A system and method for vehicle wheel detection is disclosed. A particular embodiment can be configured to: receive training image data from a training image data collection system; obtain ground truth data corresponding to the training image data; perform a training phase to train one or more classifiers for processing images of the training image data to detect vehicle wheel objects in the images of the training image data; receive operational image data from an image data collection system associated with an autonomous vehicle; and perform an operational phase including applying the trained one or more classifiers to extract vehicle wheel objects from the operational image data and produce vehicle wheel object data.

公开(公告)号：US11610406B2

公开(公告)日：2023-03-21

申请号：US17377206

申请日：2021-07-15

Applicant: TUSIMPLE, INC.

Inventor： Zhipeng Yan ,  Lingting Ge ,  Pengfei Chen ,  Panqu Wang

IPC: G05D1/00 ,  G05D1/02 ,  G06T3/00 ,  G06T3/40 ,  G06V10/75 ,  G06V20/56 ,  G06V20/58 ,  G08G1/04 ,  G08G1/16 ,  H04N7/18

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：US11587304B2

公开(公告)日：2023-02-21

申请号：US16159060

申请日：2018-10-12

Applicant: TuSimple, Inc.

Inventor： Panqu Wang ,  Pengfei Chen ,  Zehua Huang

IPC: G06V10/44 ,  G05D1/02 ,  G06T7/12 ,  G06K9/62 ,  G06V10/26 ,  G06V20/58

Abstract: A system and 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：US10796402B2

公开(公告)日：2020-10-06

申请号：US16165951

申请日：2018-10-19

Applicant: TuSimple, Inc.

Inventor： Zhipeng Yan ,  Pengfei Chen ,  Panqu Wang

IPC: G06T3/00 ,  G06T5/00 ,  G05D1/02 ,  G06T5/20

Abstract: A system and method for fisheye image processing is disclosed. A particular embodiment can be configured to: receive fisheye image data from at least one fisheye lens camera associated with an autonomous vehicle, the fisheye image data representing at least one fisheye image frame; partition the fisheye image frame into a plurality of image portions representing portions of the fisheye image frame; warp each of the plurality of image portions to map an arc of a camera projected view into a line corresponding to a mapped target view, the mapped target view being generally orthogonal to a line between a camera center and a center of the arc of the camera projected view; combine the plurality of warped image portions to form a combined resulting fisheye image data set representing recovered or distortion-reduced fisheye image data corresponding to the fisheye image frame; generate auto-calibration data representing a correspondence between pixels in the at least one fisheye image frame and corresponding pixels in the combined resulting fisheye image data set; and provide the combined resulting fisheye image data set as an output for other autonomous vehicle subsystems.