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公开(公告)号：US12248321B2
公开(公告)日：2025-03-11
申请号：US18210721
申请日：2023-06-16
Applicant: TUSIMPLE, INC.
Inventor： Xingdong Li , Xing Sun , Wutu Lin , Liu Liu
Abstract: A system and method for generating simulated vehicles with configured behaviors for analyzing autonomous vehicle motion planners are disclosed. A particular embodiment includes: obtaining configuration instructions and data for each of a plurality of simulated vehicles, a specific driving behavior for each of the plurality of simulated vehicles corresponding to perception data obtained from perception data sensors; and generating a plurality of trajectories and acceleration profiles to transition each of the plurality of simulated vehicles from a current position and speed to a corresponding target position and target speed, the target position and the target speed corresponding to the specific driving behavior for each of the plurality of simulated vehicles.
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公开(公告)号：US12242274B2
公开(公告)日：2025-03-04
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
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：US12228472B2
公开(公告)日：2025-02-18
申请号：US18424318
申请日：2024-01-26
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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公开(公告)号：US12174634B2
公开(公告)日：2024-12-24
申请号：US18186137
申请日：2023-03-17
Applicant: TUSIMPLE, INC.
Inventor： Junbo Jing , Arda Kurt , Tianqu Shao , Chasen Sherman , Xing Sun
Abstract: Methods, systems and apparatus for autonomous vehicle path planning and path navigation are described. One example system includes an offline server configured to generate a library of optimal paths for navigating a geographic area, wherein the geographic area is represented as a grid node map and an orientation grid bin map and wherein the optimal paths correspond to paths between pairs of grid node pairs in the grid node map based on optimization criteria, a storage device on the autonomous vehicle for storing the library of optimal paths, and an online server located on the autonomous vehicle configured to access information from the library of optimal paths from the storage device based on a current position and a current heading of the autonomous vehicle, and navigating the autonomous vehicle through the geographic area based on the information.
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公开(公告)号：US12122419B2
公开(公告)日：2024-10-22
申请号：US16912444
申请日：2020-06-25
Applicant: TUSIMPLE, INC.
Inventor： Junbo Jing , Arda Kurt , Yujia Wu , Tianqu Shao , Xing Sun , Zijie Xuan , Haoming Sun , Chasen Sherman
CPC classification number: B60W60/0011 , G05D1/0022 , G05D1/0274 , H04W4/40
Abstract: Described is a two-level optimal path planning process for autonomous tractor-trailer trucks which incorporates offline planning, online planning, and utilizing online estimation and perception results for adapting a planned path to real-world changes in the driving environment. In one aspect, a method of navigating an autonomous vehicle includes determining, by an online server, a current vehicle state of the autonomous vehicle in a mapped driving area. The method includes receiving, by the online server from an offline path library, a path for the autonomous driving vehicle through the mapped driving area from the current vehicle state to a destination vehicle state, and receiving fixed and moving obstacle information. The method includes adjusting the path to generate an optimized path that avoids the fixed and moving obstacles and ends at a targeted final vehicle state, and navigating the autonomous vehicle based on the optimized path.
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Patent Agency Ranking
公开(公告)号：US20230244237A1
公开(公告)日：2023-08-03
申请号：US18186137
申请日：2023-03-17
Applicant: TUSIMPLE, INC.
Inventor： Junbo JING , Arda Kurt , Tianqu Shao , Chasen Sherman , Xing Sun
CPC classification number: G05D1/0212 , B62D15/021 , B62D15/025 , G05D1/0274 , G05D1/0276 , G05D2201/0213 , G05D2201/0212
Abstract: Methods, systems and apparatus for autonomous vehicle path planning and path navigation are described. One example system includes an offline server configured to generate a library of optimal paths for navigating a geographic area, wherein the geographic area is represented as a grid node map and an orientation grid bin map and wherein the optimal paths correspond to paths between pairs of grid node pairs in the grid node map based on optimization criteria, a storage device on the autonomous vehicle for storing the library of optimal paths, and an online server located on the autonomous vehicle configured to access information from the library of optimal paths from the storage device based on a current position and a current heading of the autonomous vehicle, and navigating the autonomous vehicle through the geographic area based on the information.
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公开(公告)号：US11366467B2
公开(公告)日：2022-06-21
申请号：US16862132
申请日：2020-04-29
Applicant: TUSIMPLE, INC.
Inventor： Xing Sun , Wutu Lin , Liu Liu , Kai-Chieh Ma , Zijie Xuan , Yufei Zhao
IPC: G05D1/00 , G01C21/26 , B60W20/00 , 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：US20200348678A1
公开(公告)日：2020-11-05
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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公开(公告)号：US10782693B2
公开(公告)日：2020-09-22
申请号：US15805983
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, using the trained trajectory prediction module to generate predicted trajectories for each of one or more proximate vehicles near the host vehicle, generating a proposed trajectory for the host vehicle, 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：US10768626B2
公开(公告)日：2020-09-08
申请号：US15721781
申请日：2017-09-30
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 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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