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    Intelligent driving test method with corner cases dynamically completed based on human feedback
    2.
    发明授权
    Intelligent driving test method with corner cases dynamically completed based on human feedback 有权

    公开(公告)号：US12013671B1

    公开(公告)日：2024-06-18

    申请号：US18537769

    申请日：2023-12-12

    申请人： TONGJI UNIVERSITY

    发明人： Hong Chen ,  Lin Zhang ,  Qiang Meng

    IPC分类号： G05B13/04 ,  G06F11/36 ,  G06F30/27

    CPC分类号： G05B13/04 ,  G06F11/3664 ,  G06F30/27

    摘要： Disclosed is an intelligent driving test method with corner cases dynamically completed based on human feedback, including the following steps: obtaining an initial state of a real environment; building an original scene driver based on reinforcement learning, and correcting behavior selection to obtain an exploratory behavior; testing in a testing environment, performing expert evaluation, and building a dynamic corner case completion library based on the human feedback; building an imitation learning driver based on the human feedback, updating a policy based on test data in the dynamic corner case completion library, training the imitation learning driver, and outputting a corner case reproduction behavior; obtaining the initial state of the real environment and an initial environmental state of the dynamic corner case completion library, and selecting a scene driver; and outputting a corresponding behavior, and testing in the testing environment based on the corresponding behavior to obtain a test result.

    POWER ASSEMBLY FOR ROBOTIC PLATFORM FOR GLOBAL VEHICLE TARGET (GVT) OF AUTONOMOUS DRIVING
    3.
    发明申请
    POWER ASSEMBLY FOR ROBOTIC PLATFORM FOR GLOBAL VEHICLE TARGET (GVT) OF AUTONOMOUS DRIVING 有权

    公开(公告)号：US20220155182A1

    公开(公告)日：2022-05-19

    申请号：US17529281

    申请日：2021-11-18

    申请人： TONGJI UNIVERSITY

    发明人： Hong Chen ,  Zhihua Li ,  Lin Zhang

    IPC分类号： G01M17/06

    摘要： The present invention relates to a power assembly for a robotic platform for a Global Vehicle Target (GVT) of autonomous driving. The power assembly includes an assembly housing, a motor, a transmission mechanism, a standby brake, a suspension and wheels, where when the power assembly is in use, a driving force output by the motor is transmitted to the wheels by means of the transmission mechanism, so as to drive the wheels to rotate, the standby brake is used for braking an output shaft of the motor, a top of the suspension supports the assembly housing, and when a load borne by the assembly housing changes, the suspension contracts or extends to drive the wheels to rotate upwards and downwards with a housing of the transmission mechanism as a swing arm and a rotating shaft of the motor as a swing arm rotation center.

    Self-adaptive guided advanced driver assistance system considering driving skill difference between drivers
    4.
    发明授权
    Self-adaptive guided advanced driver assistance system considering driving skill difference between drivers 有权

    公开(公告)号：US11975705B1

    公开(公告)日：2024-05-07

    申请号：US18518356

    申请日：2023-11-22

    申请人： TONGJI UNIVERSITY

    发明人： Hong Chen ,  Lin Zhang ,  Qiang Meng

    IPC分类号： B60W10/20 ,  B60W40/09 ,  B60W50/00 ,  B60W60/00

    CPC分类号： B60W10/20 ,  B60W40/09 ,  B60W50/0098 ,  B60W60/0015 ,  B60W2050/0031 ,  B60W2510/202

    摘要： The present disclosure relates to a self-adaptive guided advanced driver assistance system (ADAS) considering a driving skill difference between drivers, including a driving skill classification module, configured to calculate a vehicle stability margin based on a vehicle state, and obtain a corresponding driving skill classification result with the vehicle stability margin and a driver state as inputs of a driving skill classification model; a skill learning range classification module, configured to obtain the vehicle stability margin and a distance between a vehicle and a lane line boundary, and use a skill learning range classification model to obtain a skill learning range classification result; and a self-adaptive guided driving right allocation module, configured to realize driving right allocation control based on the driving skill classification result and the skill learning range classification result, and generate an assisted steering torque acting on a vehicle steering system.

    Decision-making and planning integrated method for nonconservative intelligent vehicle
    5.
    发明授权
    Decision-making and planning integrated method for nonconservative intelligent vehicle 有权

    公开(公告)号：US12116016B1

    公开(公告)日：2024-10-15

    申请号：US18539247

    申请日：2023-12-13

    申请人： TONGJI UNIVERSITY

    发明人： Hong Chen ,  Lin Zhang ,  Rongjie Yu ,  Qiang Meng ,  Jinlong Hong

    IPC分类号： B60W60/00 ,  B60W40/04 ,  B60W50/00 ,  G06N3/04

    CPC分类号： B60W60/00274 ,  B60W40/04 ,  B60W50/0097 ,  G06N3/04 ,  B60W2554/4041 ,  B60W2554/4045 ,  B60W2556/10

    摘要： Disclosed is a decision-making and planning integrated method for a nonconservative intelligent vehicle in a complex heterogeneous environment, including the following steps: offline establishing and training a social interaction knowledge learning model; obtaining state data of the traffic participants and state data of an intelligent vehicle online in real time, and splicing the state data to obtain an environmental state; using the environmental state as an input to the trained social interaction knowledge learning model to obtain predicted trajectories of all traffic participants including the nonconservative intelligent vehicle; updating the environmental state based on the predicted trajectories; and inputting the updated environmental state to the social interaction knowledge learning model to complete trajectory decision-making and planning for the nonconservative intelligent vehicle by iteration, where a planned trajectory of the nonconservative intelligent vehicle is a splicing result of a first point of a predicted trajectory obtained by each iteration.

    METHOD FOR REAL TIME OPTIMIZATION AND PARALLEL COMPUTING OF MODEL PREDICTION CONTROL BASED ON COMPUTING CHART
    7.
    发明申请
    METHOD FOR REAL TIME OPTIMIZATION AND PARALLEL COMPUTING OF MODEL PREDICTION CONTROL BASED ON COMPUTING CHART 有权

    公开(公告)号：US20220327388A1

    公开(公告)日：2022-10-13

    申请号：US17562001

    申请日：2021-12-27

    申请人： TONGJI UNIVERSITY

    发明人： Hong Chen ,  Qiang Meng ,  Lin Zhang ,  Haoqi Hu ,  Bin Li

    IPC分类号： G06N3/08 ,  G06N5/02

    摘要： The disclosure relates to a method for real time optimization and parallel computing of model prediction control based on a computing chart, comprising the following steps: building a prediction model of a system state amount and building a target function of a system; building a parallel computing architecture for model prediction control of a prediction model and the target function and employing a triggering parallel computing method by the parallel computing architecture to synchronously compute the prediction model and the target function; and solving and computing a gradient with a manner of back propagation and using a gradient descent method to optimize a control amount of the system and realize real time optimal control of the system. Compared with the prior art, the present disclosure greatly improves a computing efficiency, ensures real time property of a model prediction controller, and extends application fields of model prediction control.

    PARALLEL COMPUTING METHOD FOR MAN-MACHINE COORDINATED STEERING CONTROL OF SMART VEHICLE BASED ON RISK ASSESSMENT
    8.
    发明申请
    PARALLEL COMPUTING METHOD FOR MAN-MACHINE COORDINATED STEERING CONTROL OF SMART VEHICLE BASED ON RISK ASSESSMENT 有权

    公开(公告)号：US20220324443A1

    公开(公告)日：2022-10-13

    申请号：US17562021

    申请日：2021-12-27

    申请人： TONGJI UNIVERSITY

    发明人： Hong Chen ,  Bin Li ,  Qiang Meng ,  Lin Zhang ,  Yanjun Huang

    IPC分类号： B60W30/12 ,  B60W10/04 ,  B60W10/20 ,  B60W50/00

    摘要： A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

    Method for predicting trajectory of traffic participant in complex heterogeneous environment
    10.
    发明授权
    Method for predicting trajectory of traffic participant in complex heterogeneous environment 有权

    公开(公告)号：US12112623B1

    公开(公告)日：2024-10-08

    申请号：US18537771

    申请日：2023-12-12

    申请人： TONGJI UNIVERSITY

    发明人： Lin Zhang ,  Hong Chen ,  Rongjie Yu ,  Qiang Meng ,  Jinlong Hong

    IPC分类号： G08G1/01 ,  B60W60/00 ,  G08G1/015

    CPC分类号： G08G1/0133 ,  B60W60/00276 ,  G08G1/015

    摘要： Disclosed is a method for predicting a trajectory of a traffic participant in a complex heterogeneous environment, including the following steps: obtaining traffic participant information in a complex heterogeneous environment; arranging and numbering traffic participant classes based on the class information, to obtain serial numbers of the traffic participant classes; establishing a position graph, a velocity graph, an acceleration graph, and a class graph, into each of which expert experience is introduced; and capturing topological structure relationships and time dependence relationships to obtain a position hidden state, a velocity hidden state, an acceleration hidden state, and a class hidden state; classifying the position hidden state, the velocity hidden state, the acceleration hidden state, and the class hidden state to obtain a hidden state set of traffic participants; and decoding hidden states of the traffic participants separately using a corresponding decoder to obtain future trajectory predictions of the traffic participants.