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公开(公告)号：US20240104382A1
公开(公告)日：2024-03-28
申请号：US18536677
申请日：2023-12-12
Applicant: TuSimple, Inc.
Inventor： Tian LI , Panqu WANG , Pengfei CHEN
IPC: G06N3/08 , G06F18/21 , G06F18/214 , G06F18/2413 , G06N3/045 , G06N20/00 , G06V10/44 , G06V10/764 , G06V20/56
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 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 of the autonomous vehicle to control operation of the autonomous vehicle based on the instance-level roadway feature detection results.
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公开(公告)号：US20240046489A1
公开(公告)日：2024-02-08
申请号：US18489250
申请日：2023-10-18
Applicant: TUSIMPLE, INC.
Inventor： Lingting GE , Pengfei CHEN , Panqu WANG
CPC classification number: G06T7/277 , G06N3/08 , G06N3/04 , G06T7/248 , G06T7/74 , G06V20/58 , G06V10/764 , G06V10/82 , B60Y2400/3015 , G06T2207/30248 , G06V10/62
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：US20230406297A1
公开(公告)日：2023-12-21
申请号：US18241576
申请日：2023-09-01
Applicant: TuSimple, Inc.
Inventor： Hongkai YU , Zhipeng YAN , Panqu WANG , Pengfei CHEN
IPC: B60W30/095 , G06V20/56 , G06V10/44 , G06V20/58 , G06F18/24 , G06F18/214 , G06F18/2413 , G06V10/25 , G06V10/774 , G06V10/70
CPC classification number: B60W30/0956 , G06V20/56 , G06V10/44 , G06V20/58 , G06F18/24 , G06F18/2155 , G06F18/2413 , G06V10/25 , G06V10/7753 , G06V10/87 , G05D1/0221
Abstract: A system and method for vehicle occlusion detection is 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 classifiers, a first classifier being trained for processing static images of the training image data, a second classifier being trained for processing image sequences of the training image data; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including performing feature extraction on the image data, determining a presence of an extracted feature instance in multiple image frames of the image data by tracing the extracted feature instance back to a previous plurality of N frames relative to a current frame, applying the first trained classifier to the extracted feature instance if the extracted feature instance cannot be determined to be present in multiple image frames of the image data, and applying the second trained classifier to the extracted feature instance if the extracted feature instance can be determined to be present in multiple image frames of the image data.
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公开(公告)号：US20230222916A1
公开(公告)日：2023-07-13
申请号：US18121117
申请日：2023-03-14
Applicant: TUSIMPLE, INC.
Inventor： Zhipeng YAN , Lingting GE , Pengfei CHEN , Panqu WANG
CPC classification number: G06T7/73 , G06V20/56 , G06V10/443 , G06T2207/30248
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：US20230064192A1
公开(公告)日：2023-03-02
申请号：US17983129
申请日：2022-11-08
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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Patent Agency Ranking
公开(公告)号：US20220215672A1
公开(公告)日：2022-07-07
申请号：US17656415
申请日：2022-03-24
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：US20200250456A1
公开(公告)日：2020-08-06
申请号：US16855951
申请日：2020-04-22
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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38.发明申请公开(公告)号：US20190164018A1
公开(公告)日：2019-05-30
申请号：US15822467
申请日：2017-11-27
Applicant: TuSimple
Inventor： Ligeng ZHU , Panqu WANG , Pengfei CHEN
Abstract: A system and method for drivable road surface representation generation using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle and receiving three dimensional (3D) point cloud data from a distance measuring device mounted on the vehicle; projecting the 3D point cloud data onto the 2D image data to produce mapped image and point cloud data; performing post-processing operations on the mapped image and point cloud data; and performing a smoothing operation on the processed mapped image and point cloud data to produce a drivable road surface map or representation.
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公开(公告)号：US20190108641A1
公开(公告)日：2019-04-11
申请号：US16209262
申请日：2018-12-04
Applicant: TuSimple
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：US20190065864A1
公开(公告)日：2019-02-28
申请号：US15796769
申请日：2017-10-28
Applicant: TuSimple
Inventor： Hongkai YU , Zhipeng YAN , Panqu WANG , Pengfei CHEN
Abstract: A system and method for vehicle occlusion detection is 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 classifiers, a first classifier being trained for processing static images of the training image data, a second classifier being trained for processing image sequences of the training image data; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including performing feature extraction on the image data, determining a presence of an extracted feature instance in multiple image frames of the image data by tracing the extracted feature instance back to a previous plurality of N frames relative to a current frame, applying the first trained classifier to the extracted feature instance if the extracted feature instance cannot be determined to be present in multiple image frames of the image data, and applying the second trained classifier to the extracted feature instance if the extracted feature instance can be determined to be present in multiple image frames of the image data.
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