US 12,134,386 B2
Smooth cooperative lane change control method for multi-connected and autonomous vehicle (CAV)
Siyuan Gong, Xi'an (CN); Kang Sun, Xi'an (CN); Xiangmo Zhao, Xi'an (CN); Xia Wu, Xi'an (CN); Licheng Zhang, Xi'an (CN); Wenjing Wang, Xi'an (CN); Yukun Ding, Xi'an (CN); Congli Zhang, Xi'an (CN); Yihan Sun, Xi'an (CN); Hongda Chang, Xi'an (CN); Ze Li, Xi'an (CN); Lu Zeng, Xi'an (CN); Xinxin Wang, Xi'an (CN); Jiakai Yin, Xi'an (CN); Yuqian Ma, Xi'an (CN); and Guohui Zheng, Xi'an (CN)
Assigned to CHANG'AN UNIVERSITY, Xi'an (CN)
Filed by CHANG'AN UNIVERSITY, Xi'an (CN)
Filed on Nov. 14, 2022, as Appl. No. 18/055,160.
Prior Publication US 2023/0182741 A1, Jun. 15, 2023
Int. Cl. B60W 30/18 (2012.01); B60W 50/00 (2006.01); B60W 60/00 (2020.01); G08G 1/16 (2006.01)
CPC B60W 30/18163 (2013.01) [B60W 50/0097 (2013.01); B60W 60/0013 (2020.02); B60W 60/0027 (2020.02); B60W 2050/0031 (2013.01); B60W 2520/06 (2013.01); B60W 2520/10 (2013.01); B60W 2520/105 (2013.01); B60W 2520/14 (2013.01); B60W 2530/201 (2020.02); B60W 2552/10 (2020.02); B60W 2554/4041 (2020.02)] 9 Claims
OG exemplary drawing
 
1. A smooth cooperative lane change control method for a multi-connected and autonomous vehicle (CAV), comprising:
(a) acquiring vehicle information of a lane-changing vehicle M and four surrounding vehicles thereof, wherein the four surrounding vehicles are vehicle A, vehicle B, vehicle C and vehicle D, respectively; a lane where the lane-changing vehicle M is currently located is defined as a main lane, and the vehicle C and the vehicle D are located in the main lane; the vehicle C is located in front of the lane-changing vehicle M, and the vehicle D is located behind the lane-changing vehicle M; the vehicle A and the vehicle B are located in a target lane; the vehicle B is located in front of the lane-changing vehicle M, and the vehicle A is located behind the lane-changing vehicle M; the vehicle D and the vehicle A are configured as cooperative lane-changing vehicles of the lane-changing vehicle M; the vehicle D and the vehicle A are controlled to behave cooperatively with the lane-changing vehicle M during a lane changing process, and the vehicle B and the vehicle C are uncontrolled; the lane-changing vehicle M, the vehicle D and the vehicle A are each a connected and autonomous vehicle; and the vehicle B and the vehicle C are each a human-driven vehicle (HDV);
(b) according to vehicle information and parameters of the vehicle B and the vehicle C, constructing an uncontrolled-vehicle motion state prediction model; and according to vehicle information and parameters of the lane-changing vehicle M, the vehicle A and the vehicle D, constructing a controlled-vehicle motion state prediction model; and
according to the uncontrolled-vehicle motion state prediction model, predicting a motion state of the vehicle B and a motion state of the vehicle C; and according to the controlled-vehicle motion state prediction model, predicting a motion state of the lane-changing vehicle M, a motion state of the vehicle A, and a motion state of the vehicle D;
wherein the uncontrolled-vehicle motion state prediction model comprises a long short-term memory (LSTM)-based neural network model; and the controlled-vehicle motion state prediction model comprises a vehicle kinematic model, and the vehicle kinematic model comprises a steering kinematic model and a longitudinal kinematic model;
the LSTM-based neural network model is configured to predict the motion state of the vehicle B and the motion state of the vehicle C, so as to obtain a predicted motion state of the vehicle B and a predicted motion state of the vehicle C, wherein the predicted motion state of the vehicle B and the predicted motion state of the vehicle C each comprise longitudinal position, longitudinal acceleration and longitudinal speed;
the steering kinematic model is configured to predict the motion state of the lane-changing vehicle M, wherein a predicted motion state of the lane-changing vehicle M comprises longitudinal position increment yM, transverse position increment xM, yaw angle increment φM and axial speed increment vM; and
the longitudinal kinematic model is configured to predict the motion state of the vehicle A and the motion state of the vehicle D, wherein the motion state of the vehicle A and the motion state of the vehicle D each comprise longitudinal position increment xi and longitudinal speed increment vi, wherein xi is a longitudinal position of the vehicle A and the vehicle D, and vi is a longitudinal speed of the vehicle A and the vehicle D;
(c) based on vehicle information and predicted motion states of the lane-changing vehicle M, the vehicle A, the vehicle B and the vehicle C, constructing an upper-layer optimization model; and
calculating an optimal control value and an optimized motion state of the lane-changing vehicle M and an optimal control value of the vehicle A according to the upper-layer optimization model; and
(d) based on vehicle information of the lane-changing vehicle M, the vehicle C and the vehicle D, the optimized motion state of the lane-changing vehicle M and predicted motion states of the vehicle C and the vehicle D, constructing a lower-layer optimization model;
calculating an optimal control value of the vehicle D according to the lower-layer optimization model; and
controlling the lane-changing vehicle M, the vehicle A and vehicle D to run according to a corresponding optimal control value.