Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 693-703.doi: 10.13229/j.cnki.jdxbgxb20220897

Previous Articles    

Integrated moving horizon decision⁃making method for lane and speed of intelligent vehicle in complex scenarios

Hong-yan GUO1,2(),Wen-ya YU1,2,Jun LIU1,2(),Qi-kun DAI1,2   

  1. 1.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
    2.College of Communication Engineering，Jilin University，Changchun 130022，China
  • Received:2022-07-15 Online:2023-03-01 Published:2023-03-29
  • Contact: Jun LIU E-mail:guohy11@jlu.edu.cn;liujun20@jlu.edu.cn

RICH HTML

 7   

PDF (PC)

136

Abstract:

Aiming at the problem of intelligent vehicle's driving environment understanding and behavior decision-making in complex scenarios， an integrated moving horizon decision-making method for lane and speed of intelligent vehicle was proposed. To obtain the vehicle positions based on the centerlines of different lanes， the vehicle-road space reconstruction model in complex scenarios and the integrated model of lane and vehicle speed with non-integer longitudinal vehicle speed and integer lane number as control variables was established. The safety of future trajectories of the vehicle and oncoming vehicles from multiple directions was analyzed. The integrated decision-making of lane and speed of intelligent vehicle in complex scenarios was described as a mixed-integer nonlinear programming problem. In order to verify the effectiveness of the integrated moving horizon decision-making method of lane and speed， the joint simulation of the vehicle dynamics simulation software veDYNA and Simulink is carried out in the unprotected intersection scenario. The results show that the decision-making method can change lanes in the face of slow-moving or abruptly cut-in vehicles； for safety, intelligent vehicle can make decisions to slow down and give way, when it and the surrounding vehicles drive to the intersection area at the same time.

Key words: control theory and control engineering, integrated decision-making of lane and speed, moving horizon, complex scenarios, mixed-integer programming

CLC Number: 

  • TP273

Fig.1

Ddiagram of integrated decision-making for lane and speed of intelligent vehicle"

Fig.2

Curves of fitting the lane change"

Fig.3

Vehicle-road space reconstruction model"

Fig.4

Division of driving area"

Fig.5

Simulation results of the condition where the vehicle in front is moving slowly"

Fig.6

Simulation results of the condition where theobstacle vehicles abruptly cut in"

Fig.7

Simulation results of the condition of intelligent vehicle and obstacle vehicles entering the intersection at the same time"

Fig.8

Comparative experiment"

1 张家旭, 王晨, 赵健, 等. 面向狭小平行泊车位的路径规划与跟踪控制[J]. 吉林大学学报: 工学版, 2021, 51(5): 1879-1886.
Zhang Jia-xu, Wang Chen, Zhao Jian, et al. Path planning and tracking control for narrow parallel parking spaces[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(5): 1879-1886.
2 Milanes V, Shladover S E, Spring J, et al. Cooperative adaptive cruise control in real traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 296-305.
3 Switkes J P, Rossetter E J, Coe I A, et al. Handwheel force feedback for lanekeeping assistance: combined dynamics and stability[J]. Journal of Dynamic Systems, Measurement, and Control, 2005, 128(3): 532-542.
4 国家制造强国建设战略咨询委员会. “绿皮书”助跑机器人—解读“中国制造2025”机器人领域技术路线图[J]. 机器人产业, 2015(5): 36-37.
Manufacturing-Power-Construction-Strategy-Advisory-Committee National. "Green Book" run-up robot - interpreting the "Made in China 2025" robot technology roadmap[J]. Robot Industry, 2015(5): 36-37.
5 Zhang Lin, Meng Qiang, Chen Hong, et al. Kalman filter-based fusion estimation method of steering feedback torque for steer-by-wire systems[J]. Automotive Innovation, 2021, 4(4): 430-439.
6 Liu Jun, Dai Qi-kun, Guo Hong-yan, et al. Human-oriented online driving authority optimization for driver-automation shared steering control[J]. IEEE Transactions on Intelligent Vehicles,2022,7(4):863-872.
7 Karlsson J, Murgovski N, Sjoberg J. Optimal trajectory planning and decision making in lane change maneuvers near a highway exit[C]∥2019 18th European Control Conference (ECC), Naples, Italy, 2019:3254-3260.
8 罗开杰, 何赏璐, 叶茂. 智能网联车换道决策建模研究综述[C]∥世界交通运输工程技术论坛(WTC2021)论文集(上), 西安, 中国, 2021: 1456-1463.
Luo Kai-jie, He Shang-lu, Ye Mao. A review of lane-changing decision modeling for intelligent connected vehicles[C]∥Proceedings of World Transportation Engineering and Technology Forum (WTC2021) (1), Xi'an, China, 2021: 1456-1463.
9 Gipps P G. A model for the structure of lane-changing decisions[J]. Transportation Research, Part B: Methodological, 1986, 20(5): 403-414.
10 杨达, 吕蒙, 戴力源, 等. 车联网环境下自动驾驶车辆车道选择决策模型[J]. 中国公路学报, 2022, 35(4): 243-255.
Yang Da, Lv Meng, Dai Li-yuan, et al. Lane selection decision model for autonomous vehicle in internet of vehicles[J]. China Journal of Highway and Transport, 2022, 35(4): 243-255.
11 Tang Shuang, Shu Hong, Tang Yu. Research on decision-making of lane-changing of automated vehicles in highway confluence area based on deep reinforcement learning[C]∥2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI), Tianjin, China, 2021: 1-8.
12 Noh S, An K. Decision-making framework for automated driving in highway environments[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(1): 58-71.
13 Noh S. Decision-making framework for autonomous driving at road intersections: safeguarding against collision, overly conservative behavior, and violation vehicles[J]. IEEE Transactions on Industrial Electronics, 2019, 66(4): 3275-3286.
14 Hang Peng, Lv Chen, Huang Chao, et al. An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14458-14469.
15 Huang Chao, Lv Chen, Hang Peng, et al. Toward safe and personalized autonomous driving: decision-making and motion control with DPF and CDT techniques[J]. IEEE/ASME Transactions on Mechatronics, 2021, 26(2): 611-620.
16 熊璐, 康宇宸, 张培志, 等. 无人驾驶车辆行为决策系统研究[J]. 汽车技术, 2018(8): 1-9.
Xiong Lu, Kang Yu-chen, Zhang Pei-zhi, et al. Research on behavior decision-making system for unmanned vehicle[J]. Automobile Technology, 2018(8): 1-9.
17 余如, 郭洪艳, 陈虹. 自主驾驶车辆的预测避障控制[J]. 信息与控制, 2015, 44(1): 117-124.
Yu Ru, Guo Hong-yan, Chen Hong. Predictive obstacle avoidance control for autonomous vehicle[J]. Information and Control, 2015, 44(1): 117-124.
18 魏民祥, 滕德成, 吴树凡. 基于Frenet坐标系的自动驾驶轨迹规划与优化算法[J]. 控制与决策, 2021, 36(4): 815-824.
Wei Min-xiang, Teng De-cheng, Wu Shu-fan. Automatic driving trajectory planning and optimization algorithm based on frenet coordinate system[J]. Control and Decision, 2021, 36(4): 815-824.
19 Zhu S, Aksun-Guvenc B. Trajectory planning of autonomous vehicles based on parameterized control optimization in dynamic on-road environments[J]. Journal of Intelligent and Robotic Systems, 2020 100(3): 1055-1067.
20 陈虹. 模型预测控制[M]. 北京: 科学出版社, 2013: 213-223.
21 Xie Guo-tao, Gao Hong-bo, Qian Li-jun, et al. Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5999-6008.
22 金芬. 遗传算法在函数优化中的应用研究[D]. 苏州: 苏州大学电子信息学院, 2008.
Jin Fen. Application of genetic algorithm in function optimization[D]. Suzhou: School of Electronic and Information Engineering, Suzhou University, 2008.
23 Xi Chen-yang, Shi Tian-yu, Wu Yuan-kai, et al. Efficient motion planning for automated lane change based on imitation learning and mixed-integer optimization[C]∥23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Electr Network, 2020: 1-6.
[1] De-jun WANG,Kai-ran ZHANG,Peng XU,Tian-biao GU,Wen-ya YU. Speed planning and control under complex road conditions based on vehicle executive capability [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 643-652.
[2] Jian-xin FENG,Qiang WANG,Ya-lei WANG,Biao XU. Fuzzy PID control of ultrasonic motor based on improved quantum genetic algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 1990-1996.
[3] Hong-zhi WANG,Ting-ting WANG,Huang-shui HU,Xiao-fan LU. PID control based on BP neural network optimized by Q⁃learning for speed control of BLDCM [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2280-2286.
[4] Guang-xin HAN,Ju-le ZHAO,Yun-feng HU. Moving horizon linear quadratic regulator control for ball and plate system with input constraints [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 1982-1989.
[5] Yan MA,Jian-fei HUANG,Hai-yan ZHAO. Method of vehicle formation control based on vehicle to vehicle communication [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 711-718.
[6] DENG Li-fei, SHI Yao-wu, ZHU Lan-xiang, YU Ding-li. Failure detection of closed-loop systems and application to SI engines [J]. 吉林大学学报(工学版), 2017, 47(2): 577-582.
[7] Yu Shu-you，Chen Hong . H∞/generalized H2 control of active suspension based on moving horizon strategy [J]. 吉林大学学报(工学版), 2007, 37(05): 1164-1169.
[8] Zhao Hai-yan，Chen Hong .

Moving horizon estimation for stochastic systems with missing measurements

[J]. 吉林大学学报(工学版), 2007, 37(02): 396-0400.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd