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公开(公告)号：US10685244B2
公开(公告)日：2020-06-16
申请号：US15906561
申请日：2018-02-27
Applicant: TuSimple, Inc.
Inventor： Lingting Ge , Pengfei Chen , Panqu Wang
Abstract: A system and method for online real-time multi-object tracking is disclosed. A particular embodiment can be configured to: receive image frame data from at least one camera associated with an autonomous vehicle; generate similarity data corresponding to a similarity between object data in a previous image frame compared with object detection results from a current image frame; use the similarity data to generate data association results corresponding to a best matching between the object data in the previous image frame and the object detection results from the current image frame; cause state transitions in finite state machines for each object according to the data association results; and provide as an output object tracking output data corresponding to the states of the finite state machines for each object.
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公开(公告)号：US11935210B2
公开(公告)日：2024-03-19
申请号：US17018627
申请日：2020-09-11
Applicant: TUSIMPLE, INC.
Inventor： Zhipeng Yan , Pengfei Chen , Panqu Wang
CPC classification number: G06T3/0018 , G05D1/0246 , G06T5/006 , G06T5/20 , G06T2207/30252
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.
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公开(公告)号：US11853883B2
公开(公告)日：2023-12-26
申请号：US17214828
申请日：2021-03-27
Applicant: TuSimple, Inc.
Inventor： Tian Li , Panqu Wang , Pengfei Chen
IPC: G06N3/00 , G06N3/08 , G06N20/00 , G06V20/56 , G06V10/44 , G06F18/214 , G06F18/21 , G06F18/2413 , G06N3/045 , G06V10/764
CPC classification number: G06N3/08 , G06F18/214 , G06F18/2178 , G06F18/24143 , G06N3/045 , G06N20/00 , G06V10/454 , G06V10/764 , G06V20/588
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.
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公开(公告)号：US11010616B2
公开(公告)日：2021-05-18
申请号：US16867472
申请日：2020-05-05
Applicant: TUSIMPLE, INC.
Inventor： Zehua Huang , Pengfei Chen , Panqu Wang
Abstract: A system and method for semantic segmentation using hybrid dilated convolution (HDC) are disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image; performing a convolution operation on the feature map and producing multiple convolution layers; grouping the multiple convolution layers into a plurality of groups; applying different dilation rates for different convolution layers in a single group of the plurality of groups; and applying a same dilation rate setting across all groups of the plurality of groups.
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公开(公告)号：US10679074B2
公开(公告)日：2020-06-09
申请号：US16209262
申请日：2018-12-04
Applicant: TuSimple, Inc.
Inventor： Zehua Huang , Pengfei Chen , Panqu Wang
Abstract: A system and method for semantic segmentation using hybrid dilated convolution (HDC) are disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image; performing a convolution operation on the feature map and producing multiple convolution layers; grouping the multiple convolution layers into a plurality of groups; applying different dilation rates for different convolution layers in a single group of the plurality of groups; and applying a same dilation rate setting across all groups of the plurality of groups.
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Patent Agency Ranking
公开(公告)号：US10671873B2
公开(公告)日：2020-06-02
申请号：US15917331
申请日：2018-03-09
Applicant: TuSimple, Inc.
Inventor： Panqu Wang , Pengfei Chen
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.
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公开(公告)号：US12276982B2
公开(公告)日：2025-04-15
申请号：US18167993
申请日：2023-02-13
Applicant: TuSimple, Inc.
Inventor： Xiaoling Han , Chenzhe Qian , Chiyu Zhang , Charles A. Price , Joshua Miguel Rodriguez , Lei Nie , Lingting Ge , Panqu Wang , Pengfei Chen , Shuhan Yang , Xiangchen Zhao , Xiaodi Hou , Zehua Huang
IPC: B60W50/023 , G05D1/00
Abstract: A system installed in a vehicle includes a first group of sensing devices configured to allow a first level of autonomous operation of the vehicle; a second group of sensing devices configured to allow a second level of autonomous operation of the vehicle, the second group of sensing devices including primary sensing devices and backup sensing devices; a third group of sensing devices configured to allow the vehicle to perform a safe stop maneuver; and a control element communicatively coupled to the first group of sensing devices, the second group of sensing devices, and the third group of sensing devices. The control element is configured to: receive data from at least one of the first group, the second group, or the third group of sensing devices, and provide a control signal to a sensing device based on categorization information indicating a group to which the sensing device belongs.
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公开(公告)号：US11295146B2
公开(公告)日：2022-04-05
申请号：US16868400
申请日：2020-05-06
Applicant: TUSIMPLE, INC.
Inventor： Lingting Ge , Pengfei Chen , Panqu Wang
Abstract: A system and method for online real-time multi-object tracking is disclosed. A particular embodiment can be configured to: receive image frame data from at least one camera associated with an autonomous vehicle; generate similarity data corresponding to a similarity between object data in a previous image frame compared with object detection results from a current image frame; use the similarity data to generate data association results corresponding to a best matching between the object data in the previous image frame and the object detection results from the current image frame; cause state transitions in finite state machines for each object according to the data association results; and provide as an output object tracking output data corresponding to the states of the finite state machines for each object.
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公开(公告)号：US11074462B2
公开(公告)日：2021-07-27
申请号：US16865800
申请日：2020-05-04
Applicant: TUSIMPLE, INC.
Inventor： Zhipeng Yan , Lingting Ge , Pengfei Chen , Panqu Wang
IPC: G05D1/00 , G05D1/02 , G06K9/00 , G06K9/62 , G06T3/00 , G06T3/40 , G08G1/04 , G06G1/16 , 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.
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公开(公告)号：US10970564B2
公开(公告)日：2021-04-06
申请号：US15959167
申请日：2018-04-20
Applicant: TuSimple, Inc.
Inventor： Tian Li , Panqu Wang , Pengfei Chen
Abstract: A system and method for instance-level lane detection for autonomous vehicle control includes: receiving training image data from a training image data collection system; performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting roadway lane marking 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 roadway lane marking features from the image data, causing the plurality of trained tasks to generate instance-level lane detection results.
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