公开(公告)号：US20240336302A1

公开(公告)日：2024-10-10

申请号：US18380161

申请日：2023-10-14

申请人： Tongji University

发明人： Hong CHEN ,  Lin ZHANG ,  Rongjie YU ,  Qiang MENG ,  Jinlong HONG

IPC分类号： B62D15/02 ,  B60W40/105

CPC分类号： B62D15/029 ,  B60W40/105 ,  B60W2510/20 ,  B60W2554/4041

摘要： The present invention provides a real-time path planning method taking into account dynamic properties of vehicles; the method includes calculation without connecting to internet of a reachable set and an online path planning; in the calculation without connecting to internet of the reachable set, all vehicle safety states are traversed by means of the vehicle model and the wheel model, thereby predicting the position set capable of being reached by the vehicle at a next moment; in the online path planning, by means of calculating the position set capable of being reached by the vehicle at the next moment with connecting to internet, non-linear dynamic constraints are provided for the exploration of the artificial potential field method, achieving the purpose of real-time planning while taking into account dynamic properties of the vehicles.

公开(公告)号：US20240270235A1

公开(公告)日：2024-08-15

申请号：US18381352

申请日：2023-10-18

申请人： Tongji University

发明人： Hong CHEN ,  Lin ZHANG ,  Rongjie YU ,  Hanghang LIU

IPC分类号： B60W30/02 ,  B60L15/20 ,  B60W10/08 ,  B60W10/119 ,  B60W10/20 ,  B60W40/114

CPC分类号： B60W30/02 ,  B60L15/20 ,  B60W10/08 ,  B60W10/119 ,  B60W10/20 ,  B60W40/114 ,  B60L2240/463 ,  B60W2510/222 ,  B60W2520/10 ,  B60W2520/14 ,  B60W2520/20 ,  B60W2520/30 ,  B60W2710/207 ,  B60W2720/30

摘要： A coordinated control method for electric vehicles having independent four-wheel driving and steering, comprising the steps of: calculating to obtain a desired value of yaw velocity according to the steering angle and the current vehicle driving speed, and limiting the desired value of yaw velocity according to the current road adhesion condition; constructing an optimization problem according to the current vehicle motion state and the desired value of yaw velocity, and solving the optimization problem to obtain a desired active rear wheel steering angle control variable and a desired additional yaw moment control variable; calculating to obtain an additional torque of each wheel according to a desired additional yaw moment control variable, obtaining a desired active rear wheel steering angle, and sending the additional torque of each wheel and the desired active rear wheel steering angle to an executor of the vehicle for performing a coordinated control.

公开(公告)号：US20240174211A1

公开(公告)日：2024-05-30

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.

公开(公告)号：US20240339029A1

公开(公告)日：2024-10-10

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.

公开(公告)号：US20240336286A1

公开(公告)日：2024-10-10

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.

公开(公告)号：US20240321022A1

公开(公告)日：2024-09-26

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.