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公开(公告)号：US11885712B2
公开(公告)日：2024-01-30
申请号：US17093172
申请日：2020-11-09
Applicant: TuSimple, Inc.
Inventor： Xing Sun , Wutu Lin , Yufei Zhao , Liu Liu
Abstract: A system and method for real world autonomous vehicle perception simulation are disclosed. A particular embodiment includes: configuring a sensor noise modeling module to produce simulated sensor errors or noise data with a configured degree, extent, and timing of simulated sensor errors or noise based on a set of modifiable parameters; using the simulated sensor errors or noise data to generate simulated perception data by simulating errors related to constraints of one or more of a plurality of sensors, and by simulating noise in data provided by a sensor processing module corresponding to one or more of the plurality of sensors; and providing the simulated perception data to a motion planning system for the autonomous vehicle.
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22.发明授权公开(公告)号：US11853071B2
公开(公告)日：2023-12-26
申请号：US16848809
申请日：2020-04-14
Applicant: TUSIMPLE, INC.
Inventor： Xing Sun , Wutu Lin , Liu Liu , Kai-Chieh Ma , Zijie Xuan , Yufei Zhao
CPC classification number: G05D1/0221 , B62D15/026 , B62D15/0255 , B62D15/0265 , G05D1/0088
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：US11292480B2
公开(公告)日：2022-04-05
申请号：US16569640
申请日：2019-09-12
Applicant: TuSimple, Inc.
Inventor： Yi Wang , Lindong Sun , Liu Liu , Xiaoling Han , Ruiliang Zhang
Abstract: Disclosed are devices, systems and methods for remote safe driving. One exemplary method includes detecting an emergency situation, and in response to the detecting the emergency situation, switching operation of the vehicle to a low-power operation mode that comprises shutting down a subset of vehicular components, and periodically transmitting a location of the vehicle to a remote monitoring center. Another exemplary method includes selecting at least one of a set of vehicular driving actions, and transmitting, over a secure connection, the at least one of the set of vehicular driving actions to the vehicle, wherein the set of vehicular driving actions is generated based on a classification of driver behavior.
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公开(公告)号：US10782694B2
公开(公告)日：2020-09-22
申请号：US15806013
申请日：2017-11-07
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 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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25.发明授权公开(公告)号：US10656644B2
公开(公告)日：2020-05-19
申请号：US15698375
申请日：2017-09-07
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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Patent Agency Ranking
公开(公告)号：US11853072B2
公开(公告)日：2023-12-26
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
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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公开(公告)号：US11782440B2
公开(公告)日：2023-10-10
申请号：US17126740
申请日：2020-12-18
Applicant: TUSIMPLE, INC.
Inventor： Liu Liu , Yuwei Wang , Xing Sun , Yufei Zhao , Wutu Lin
IPC: G05D1/00 , G05D1/02 , G06F30/15 , G06F30/20 , G06F111/10
CPC classification number: G05D1/0088 , G05D1/0212 , G05D1/0287 , G06F30/15 , G06F30/20 , G05D2201/0213 , G06F2111/10
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 areal 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：US11681292B2
公开(公告)日：2023-06-20
申请号：US17111984
申请日：2020-12-04
Applicant: TuSimple, Inc.
Inventor： Xingdong Li , Xing Sun , Wutu Lin , Liu Liu
IPC: G05D1/00 , G05D1/02 , G09B9/00 , G09B9/04 , G09B9/048 , G09B19/16 , G06F30/15 , G06F30/20 , G06F111/10
CPC classification number: G05D1/0088 , G05D1/0027 , G05D1/0223 , G06F30/15 , G06F30/20 , G09B9/00 , G09B9/04 , G09B9/048 , G09B19/167 , G05D2201/0213 , G06F2111/10
Abstract: A system and method for generating simulated vehicles with configured behaviors for analyzing autonomous vehicle motion planners are disclosed. A particular embodiment includes: receiving perception data from a plurality of perception data sensors; obtaining configuration instructions and data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating a target position and target speed for each of the plurality of simulated dynamic vehicles, the generated target positions and target speeds being based on the perception data and the configuration instructions and data; and generating a plurality of trajectories and acceleration profiles to transition each of the plurality of simulated dynamic vehicles from a current position and speed to the corresponding target position and target speed.
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公开(公告)号：US11550329B2
公开(公告)日：2023-01-10
申请号：US17147836
申请日：2021-01-13
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：US11435748B2
公开(公告)日：2022-09-06
申请号：US16929954
申请日：2020-07-15
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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