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    Decision-making and planning integrated method for nonconservative intelligent vehicle
    1.
    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 predicting trajectory of traffic participant in complex heterogeneous environment
    2.
    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.

    Vehicle state estimation method based on adaptive total variation denoising filtering
    4.
    发明授权
    Vehicle state estimation method based on adaptive total variation denoising filtering 有权

    公开(公告)号：US12087105B1

    公开(公告)日：2024-09-10

    申请号：US18372719

    申请日：2023-09-26

    申请人： Tongji University

    发明人： Hong Chen ,  Lin Zhang ,  Rongjie Yu ,  Chunlai Zhao ,  Jiaxing Lu ,  Bin Li

    IPC分类号： G07C5/08

    CPC分类号： G07C5/0808

    摘要： A vehicle state estimation method based on adaptive total variation denoising (TVD) filtering includes the following steps: step 1: collection and preprocessing of an original signal of a vehicle; step 2: noise level evaluation, step 3: Teager-Kaiser energy evaluation; step 4: optimization problem construction; and step 5: application of a filtered signal in the step 4 in the estimation of a vehicle state. The vehicle state estimation method is mainly based on the global noise level characteristic and the local intensity change characteristic of the vehicle system state data, and adaptive filtering of parameters is achieved by means of a TVD filtering method. The signal is denoised to the maximum extent, peak information of the signal is retained while the data smoothness is maintained, and then the signal is used for vehicle state estimation, working condition identification and the like.

    Longitudinal and lateral vehicle motion cooperative control method based on fast solving algorithm
    6.
    发明授权
    Longitudinal and lateral vehicle motion cooperative control method based on fast solving algorithm 有权

    公开(公告)号：US11938923B1

    公开(公告)日：2024-03-26

    申请号：US18372708

    申请日：2023-09-26

    申请人： Tongji University

    发明人： Hong Chen ,  Lin Zhang ,  Rongjie Yu ,  Hanghang Liu

    IPC分类号： B60W30/045 ,  B60W30/18

    CPC分类号： B60W30/045 ,  B60W30/18172 ,  B60W2520/26 ,  B60W2720/26 ,  B60W2720/30

    摘要： A longitudinal and lateral vehicle motion cooperative control method based on a fast solving algorithm comprises the following steps: calculating a desired yaw rate according to a steering wheel rotation angle and a current vehicle traveling speed; constructing a nonlinear optimization problem according to the desired yaw rate and a current actual motion state of a vehicle, wherein an objective function of the nonlinear optimization problem is used for tracking the desired yaw rate, and simultaneously for restraining a lateral speed and a tire slip ratio of the vehicle; solving the nonlinear optimization problem to calculate desired slip ratios of four tires; calculating an additional torque of each tire according to an actual slip ratio and the desired slip ratio of the tire; and sending the additional torque of each tire to an actuator of the vehicle for cooperative control.