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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
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.
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公开(公告)号：US12073324B2
公开(公告)日：2024-08-27
申请号：US18233802
申请日：2023-08-14
Applicant: TuSimple, Inc.
Inventor： Panqu Wang , Tian Li
IPC: G06V20/58 , G05D1/00 , G06F18/214 , G06N3/04 , G06N3/08 , G06V10/44 , G06V10/764 , G06V10/82 , G06V20/56
CPC classification number: G06N3/08 , G05D1/0088 , G05D1/0246 , G06F18/214 , G06N3/04 , G06V10/454 , G06V10/764 , G06V10/82 , G06V20/56 , G06V20/584
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.
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公开(公告)号：US20230391250A1
公开(公告)日：2023-12-07
申请号：US18365055
申请日：2023-08-03
Applicant: TuSimple, Inc.
Inventor： Yu-Ju Hsu , Xiaoling Han , Yijing Li , Zehua Huang , Lingting Ge , Panqu Wang , Shuhan Yang
IPC: B60Q1/14
Abstract: A system comprises a headlight mounted on an autonomous vehicle. The headlight is configured to illuminate at least a portion of a road the autonomous vehicle is on. The system further comprises a control device associated with the autonomous vehicle. The processor obtains information about an environment around the autonomous vehicle. The processor determines that at least a portion of the road should be illuminated if the information indicates that an illumination level of the portion of the road is less than a threshold illumination level. The processor adjusts the headlight to illuminate at least the portion of the road in response to determining that at least the portion of the road should be illuminated.
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公开(公告)号：US11727691B2
公开(公告)日：2023-08-15
申请号：US17090713
申请日：2020-11-05
Applicant: TUSIMPLE, INC.
Inventor： Panqu Wang
Abstract: A system and method for three-dimensional (3D) object detection is disclosed. A particular embodiment can be configured to: receive image data from a camera associated with a vehicle, the image data representing an image frame; use a machine learning module to determine at least one pixel coordinate of a two-dimensional (2D) bounding box around an object in the image frame; use the machine learning module to determine at least one vertex of a three-dimensional (3D) bounding box around the object; obtain camera calibration information associated with the camera; and determine 3D attributes of the object using the 3D bounding box and the camera calibration information.
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公开(公告)号：US11328164B2
公开(公告)日：2022-05-10
申请号：US16916488
申请日：2020-06-30
Applicant: TUSIMPLE, INC.
Inventor： Panqu Wang , Tian Li
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.
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Patent Agency Ranking
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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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
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.
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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
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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