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公开(公告)号：US20220317680A1
公开(公告)日：2022-10-06
申请号：US17807709
申请日：2022-06-17
Applicant: TUSIMPLE, INC.
Inventor： Xing SUN , Wutu LIN , Liu LIU , Kai-Chieh MA , Zijie XUAN , Yufei ZHAO
IPC: G05D1/00 , B60R16/023 , G01C21/34
Abstract: A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.
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公开(公告)号：US20240085900A1
公开(公告)日：2024-03-14
申请号：US18510352
申请日：2023-11-15
Applicant: TUSIMPLE, INC.
Inventor： Xing SUN , Wutu LIN , Liu LIU , Kai-Chieh MA , Zijie XUAN , Yufei ZHAO
IPC: G05D1/00 , B60R16/023 , G01C21/34
CPC classification number: G05D1/0005 , B60R16/0236 , G01C21/3469 , G05D1/0088
Abstract: A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.
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公开(公告)号：US20230373483A1
公开(公告)日：2023-11-23
申请号：US18228574
申请日：2023-07-31
Applicant: TUSIMPLE, INC.
Inventor： Wutu LIN , Liu LIU , Zijie Xuan , Xing SUN , Kai-Chieh MA , Yufei ZHAO
CPC classification number: B60W30/16 , B60W30/143 , B60W2420/52 , B60W2720/10 , B60W2520/10 , B60W2554/801 , B60W2554/804 , B60W2754/30
Abstract: A system and method for adaptive cruise control with proximate vehicle detection are disclosed. The example embodiment can be configured for: receiving input object data from a subsystem of a host vehicle, the input object data including distance data and velocity data relative to detected target vehicles; detecting the presence of any target vehicles within a sensitive zone in front of the host vehicle, to the left of the host vehicle, and to the right of the host vehicle; determining a relative speed and a separation distance between each of the detected target vehicles relative to the host vehicle; and generating a velocity command to adjust a speed of the host vehicle based on the relative speeds and separation distances between the host vehicle and the detected target vehicles to maintain a safe separation between the host vehicle and the target vehicles.
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4.发明申请公开(公告)号：US20220197283A1
公开(公告)日：2022-06-23
申请号：US17654224
申请日：2022-03-09
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：US20240103523A1
公开(公告)日：2024-03-28
申请号：US18536635
申请日：2023-12-12
Applicant: TuSimple, Inc.
Inventor： Xing SUN , Wutu LIN , Liu LIU , Kai-Chieh MA , Zijie XUAN , Yufei ZHAO
CPC classification number: G05D1/0221 , G05B13/048 , G05D1/0088 , G06N20/00
Abstract: A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a 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. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.
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Patent Agency Ranking
公开(公告)号：US20230063989A1
公开(公告)日：2023-03-02
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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7.发明公开公开(公告)号：US20240288868A1
公开(公告)日：2024-08-29
申请号：US18507038
申请日：2023-11-11
Applicant: TUSIMPLE, INC.
Inventor： Xing SUN , Wutu LIN , Liu LIU , Kai-Chieh MA , Zijie XUAN , Yufei ZHAO
CPC classification number: G05D1/0221 , B62D15/0255 , B62D15/026 , B62D15/0265
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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8.发明公开公开(公告)号：US20240255948A1
公开(公告)日：2024-08-01
申请号：US18631700
申请日：2024-04-10
Applicant: TuSimple, Inc.
Inventor： Wutu LIN , Liu LIU , Xing SUN , Kai-Chieh MA , Zijie XUAN , Yufei ZHAO
CPC classification number: G05D1/0088 , B60W40/09 , B60W2720/103
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：US20240168478A1
公开(公告)日：2024-05-23
申请号：US18431194
申请日：2024-02-02
Applicant: TuSimple, Inc.
Inventor： Xiaomin ZHANG , Yilun CHEN , Guangyu LI , Xing SUN , Wutu LIN , Liu LIU , Kai-Chieh MA , Zijie XUAN , Yufei ZHAO
Abstract: A prediction-based system and method for trajectory planning of autonomous vehicles is configured to: receive 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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公开(公告)号：US20230004165A1
公开(公告)日：2023-01-05
申请号：US17901736
申请日：2022-09-01
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 may include: receiving training data from a 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. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.
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