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31.发明授权公开(公告)号：US10649458B2
公开(公告)日：2020-05-12
申请号：US15698607
申请日：2017-09-07
Applicant: TuSimple, Inc.
Inventor： Xing Sun , Wutu Lin , Liu Liu , Kai-Chieh Ma , Zijie Xuan , Yufei Zhao
Abstract: A data-driven prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment includes: generating a first suggested trajectory for an autonomous vehicle; generating predicted resulting trajectories of proximate agents using a prediction module; scoring the first suggested trajectory based on the predicted resulting trajectories of the proximate agents; generating a second suggested trajectory for the autonomous vehicle and generating corresponding predicted resulting trajectories of proximate agents, if the score of the first suggested trajectory is below a minimum acceptable threshold; and outputting a suggested trajectory for the autonomous vehicle wherein the score corresponding to the suggested trajectory is at or above the minimum acceptable threshold.
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公开(公告)号：US12242271B2
公开(公告)日：2025-03-04
申请号：US17983974
申请日：2022-11-09
Applicant: TuSimple, Inc.
Inventor： Xing Sun , Yufei Zhao , Wutu Lin , Zijie Xuan , Liu Liu , Kai-Chieh Ma
Abstract: A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; determining if the task request is appropriate for the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on content of the task request or a context of the autonomous vehicle; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the determination; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.
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公开(公告)号：US12164296B2
公开(公告)日：2024-12-10
申请号：US18364587
申请日：2023-08-03
Applicant: TUSIMPLE, INC.
Inventor： Liu Liu , Yuwei Wang , Xing Sun , Yufei Zhao , Wutu Lin
IPC: G05D1/00 , G06F30/15 , G06F30/20 , G06F111/10
Abstract: An autonomous vehicle simulation system for analyzing motion planners is disclosed. A particular embodiment includes: receiving map data corresponding to a real world driving environment; obtaining perception data and configuration data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating simulated perception data for each of the plurality of simulated dynamic vehicles based on the map data, the perception data, and the configuration data; receiving vehicle control messages from an autonomous vehicle control system; and simulating the operation and behavior of a real world autonomous vehicle based on the vehicle control messages received from the autonomous vehicle control system.
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公开(公告)号：US11958473B2
公开(公告)日：2024-04-16
申请号：US17350297
申请日：2021-06-17
Applicant: TuSimple, Inc.
CPC classification number: B60W30/02 , G05D1/0088 , G06N20/00 , B60W2540/30 , B60W2710/18 , B60W2710/20 , G06N3/08
Abstract: A system and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to a normal driving behavior safe zone; receiving a proposed vehicle control command; comparing the proposed vehicle control command with the normal driving behavior safe zone; and issuing a warning alert if the proposed vehicle control command is outside of the normal driving behavior safe zone. Another embodiment includes modifying the proposed vehicle control command to produce a modified and validated vehicle control command if the proposed vehicle control command is outside of the normal driving behavior safe zone.
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公开(公告)号：US11892846B2
公开(公告)日：2024-02-06
申请号：US17006345
申请日：2020-08-28
Applicant: TuSimple, Inc.
Inventor： Xiaomin Zhang , Yilun Chen , Guangyu Li , Xing Sun , Wutu Lin , Liu Liu , Kai-Chieh Ma , Zijie Xuan , Yufei Zhao
CPC classification number: G05D1/0212 , G05D1/0088 , G08G1/161 , G08G1/166 , G08G1/167 , G05D2201/0213
Abstract: A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment is configured to: receive training data and ground truth data from a training data collection system, the training data including perception data and context data corresponding to human driving behaviors; perform a training phase for training a trajectory prediction module using the training data; receive perception data associated with a host vehicle; and perform an operational phase for extracting host vehicle feature data and proximate vehicle context data from the perception data, generating a proposed trajectory for the host vehicle, using the trained trajectory prediction module to generate predicted trajectories for each of one or more proximate vehicles near the host vehicle based on the proposed host vehicle trajectory, determining if the proposed trajectory for the host vehicle will conflict with any of the predicted trajectories of the proximate vehicles, and modifying the proposed trajectory for the host vehicle until conflicts are eliminated.
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Patent Agency Ranking
公开(公告)号：US11673557B2
公开(公告)日：2023-06-13
申请号：US16900611
申请日：2020-06-12
Applicant: TUSIMPLE, INC.
Inventor： Wutu Lin , Xiaodi Hou
IPC: B60W40/04 , G01S13/931 , G01C21/20 , G05D1/02 , G05D1/00 , G01S17/931 , G01S13/86
CPC classification number: B60W40/04 , G01C21/20 , G01S13/931 , G01S17/931 , G05D1/0088 , G05D1/0214 , G01S13/865 , G01S13/867 , G05D2201/0213
Abstract: A system and method for path planning of autonomous vehicles based on gradient are disclosed. A particular embodiment includes: generating and scoring a first suggested trajectory for an autonomous vehicle; generating a trajectory gradient based on the first suggested trajectory; generating and scoring a second suggested trajectory for the autonomous vehicle, the second suggested trajectory being based on the first suggested trajectory and a human driving model; and outputting the second suggested trajectory if the score corresponding to the second suggested trajectory is within a score differential threshold relative to the score corresponding to the first suggested trajectory.
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公开(公告)号：US11500387B2
公开(公告)日：2022-11-15
申请号：US16991599
申请日：2020-08-12
Applicant: TUSIMPLE, INC.
Inventor： Xing Sun , Yufei Zhao , Wutu Lin , Zijie Xuan , Liu Liu , Kai-Chieh Ma
Abstract: A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the content of the task request or a context of the autonomous vehicle; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.
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38.发明授权公开(公告)号：US11294375B2
公开(公告)日：2022-04-05
申请号：US16849916
申请日：2020-04-15
Applicant: TUSIMPLE, INC.
Inventor： Wutu Lin , Liu Liu , Xing Sun , Kai-Chieh Ma , Zijie Xuan , Yufei Zhao
Abstract: A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.
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公开(公告)号：US11029693B2
公开(公告)日：2021-06-08
申请号：US15672207
申请日：2017-08-08
Applicant: TuSimple, Inc.
Abstract: A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.
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公开(公告)号：US10739775B2
公开(公告)日：2020-08-11
申请号：US15796765
申请日：2017-10-28
Applicant: TuSimple, Inc.
Inventor： Xing Sun , Wutu Lin , Liu Liu , Kai-Chieh Ma , Zijie Xuan , Yufei Zhao
Abstract: A system and method for real world autonomous vehicle trajectory simulation are disclosed. A particular embodiment includes: receiving training data from a real world data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation, the vicinal scenarios corresponding to different locations, traffic patterns, or environmental conditions being simulated, provide vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions, generate a trajectory corresponding to perception data and the vehicle intention data, execute at least one of the plurality of trained trajectory prediction models to generate a distribution of predicted vehicle trajectories for each of a plurality of simulated vehicles of the simulation based on the vicinal scenario and the vehicle intention data, select at least one vehicle trajectory from the distribution based on pre-defined criteria, and update a state and trajectory of each of the plurality of simulated vehicles based on the selected vehicle trajectory from the distribution.
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