[1]TANG Youming,SUN Guanyu,SUN Guibin,et al.Autonomous vehicle trajectory planning based on urban overtaking conditions[J].CAAI Transactions on Intelligent Systems,2024,19(3):619-626.[doi:10.11992/tis.202209060]
Copy

Autonomous vehicle trajectory planning based on urban overtaking conditions

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 619-626 Column: 学术论文—机器人 Public date: 2024-05-05
Title:
Autonomous vehicle trajectory planning based on urban overtaking conditions
Author(s):
TANG Youming1; 2;  SUN Guanyu1; 3;  SUN Guibin1;  HUANG Xiongdong3;  ZHANG Yi1;  CHEN Huiqin4
1. Mechanical and Automotive Engineering College, Xiamen Technology University, Xiamen 361024, China;
2. School of Mechanical & Energy Engineering, Zhejiang University of Science & Technology, Hangzhou 310023, China;
3. Xiamen King Long United Autom
Keywords:
automatic drive; artificial potential field method; model prediction control; overtaking conditions; trajectory planning; safe distance; vehicle dynamics; obstacle avoidance strategy
CLC:
TP181；U46
DOI:
10.11992/tis.202209060
Abstract:
Overtaking is a common driving behavior on urban road. It is also one of the common behaviors in traffic accidents. Based on the improved artificial potential field method (APF) and model prediction control (MPC), a barrier avoidance planning control method was proposed in this study. The method firstly built the gravitational potential field function of the target point, the potential field function of the road & lane line and the potential field function of the obstacle vehicle, then established the unified model function on the basis of the potential field function, and finally evaluated the simulation experiment of the system. The results show that the proposed obstacle avoidance planning control method can generate a safe and smooth path, with the change range of the turning angle of the front wheel within [-0.67°, 1.02°], the change scope of the side slip angle within [-0.22°, 0.32°], having stable control ability.
References:
[1] 《中国公路学报》编辑部. 中国交通工程学术研究综述·2016[J]. 中国公路学报, 2016, 29(6): 1–161
Editorial board of the Chinese journal of highways. Review on China’s traffic engineering research progress: 2016[J]. China journal of highway and transport, 2016, 29(6): 1–161
[2] DANIEL W, MARTIN H. Automated driving: safer and more efficient future driving[M]. New Yor: Springer, 2016.
[3] ZHAO Can, LI Li, PEI Xin, et al. A comparative study of state-of-the-art driving strategies for autonomous vehicles[J]. Accident analysis & prevention, 2021, 150: 105937.
[4] 张新钰, 邹镇洪, 李志伟, 等. 面向自动驾驶目标检测的深度多模态融合技术[J]. 智能系统学报, 2020, 15(4): 758–771
ZHANG Xinyu, ZOU Zhenhong, LI Zhiwei, et al. Deep multi-modal fusion in object detection for autonomous driving[J]. CAAI transactions on intelligent systems, 2020, 15(4): 758–771
[5] 李克强, 戴一凡, 李升波, 等. 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报, 2017, 8(1): 1–14
LI Keqiang, DAI Yifan, LI Shengbo, et al. State-of-the-art and technical trends of intelligent and connected vehicles[J]. Journal of automotive safety and energy, 2017, 8(1): 1–14
[6] GONZáLEZ D, PéREZ J, MILANéS V, et al. A review of motion planning techniques for automated vehicles[J]. IEEE transactions on intelligent transportation systems, 2016, 17(4): 1135–1145.
[7] KATRAKAZAS C, QUDDUS M, CHEN Wenhua, et al. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions[J]. Transportation research part C:emerging technologies, 2015, 60: 416–42.
[8] ZHOU Yulan, HUANG Nannan. Airport AGV path optimization model based on ant colony algorithm to optimize Dijkstra algorithm in urban systems[J]. Sustainable computing:informatics and systems, 2022, 35: 100716.
[9] WANG Xingdong, ZHANG Haowei, LIU Shuo, et al. Path planning of scenic spots based on improved A* algorithm[J]. Scientific reports, 2022, 12(1): 1320.
[10] LIU Lisang, LIN Jiafeng, YAO Jinxin, et al. Path planning for smart car based on dijkstra algorithm and dynamic window approach[J]. Wireless communications and mobile computing, 2021, 2021: 8881684.
[11] 李娟, 张韵, 陈涛. 改进RRT算法在未知三维环境下AUV目标搜索中的应用[J]. 智能系统学报, 2022, 17(2): 368–375
LI Juan, ZHANG Yun, CHEN Tao. Application of the improved RRT algorithm to AUV target search in an unknown 3D environment[J]. CAAI transactions on intelligent systems, 2022, 17(2): 368–375
[12] BAE I, KIM J H, MOON J, et al. Lane change maneuver based on bezier curve providing comfort experience for autonomous vehicle users[C]//2019 IEEE Intelligent Transportation Systems Conference. Auckland: IEEE, 2019: 2272–2277.
[13] 王星, 赵海良, 王志刚. 基于邻域系统的智能车辆最优轨迹规划方法[J]. 智能系统学报, 2019, 14(5): 1040–1047
WANG Xing, ZHAO Hailiang, WANG Zhigang. Optimal trajectory planning method of intelligent vehicles based on neighborhood system[J]. CAAI transactions on intelligent systems, 2019, 14(5): 1040–1047
[14] ROSTAMI S M H, SANGAIAH A K, WANG Jin, et al. Obstacle avoidance of mobile robots using modified artificial potential field algorithm[J]. EURASIP journal on wireless communications and networking, 2019, 2019(1): 70.
[15] WANG Pengwei, GAO Song, LI Liang, et al. Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm[J]. Energies, 2019, 12(12): 2342.
[16] SANG Hongqiang, YOU Yusong, SUN Xiujun, et al. The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations[J]. Ocean engineering, 2021, 223: 108709.
[17] FALCONE. Nonlinear model predictive control for autonomous vehicles [D]. Benevento : University of Sannio, 2007.
[18] 张志勇, 龙凯, 杜荣华, 等. 自动驾驶汽车高速超车轨迹跟踪协调控制[J]. 汽车工程, 2021, 43(7): 995–1004
ZHANG Zhiyong, LONG Kai, DU Ronghua, et al. Trajectory tracking coordinated control for autonomous vehicle in high-speed overtaking[J]. Automotive engineering, 2021, 43(7): 995–1004
[19] BHARGAV J, BETZ J, ZEHNG H, et al. Deriving spatial policies for overtaking maneuvers with autonomous vehicles[C]//2022 14th International Conference on COMmunication Systems & NETworkS. Bangalore: IEEE, 2022: 859–864.
[20] 张家旭, 王晨, 赵健. 基于改进人工势场法的汽车弯道超车路径规划与跟踪控制[J]. 汽车工程, 2021, 43(4): 546–552
ZHANG Jiaxu, WANG Chen, ZHAO Jian. Path planning and tracking control for vehicle overtaking on curve based on modified artificial potential field method[J]. Automotive engineering, 2021, 43(4): 546–552
[21] 王树凤, 张钧鑫, 刘宗锋. 基于改进人工势场法的智能车辆超车路径规划研究[J]. 汽车技术, 2018(3): 5–9
WANG Shufeng, ZHANG Junxin, LIU Zongfeng. A research on overtaking lane planning for intelligent vehicles based on improved artificial potential field method[J]. Automobile technology, 2018(3): 5–9
[22] WU Qiong, CHENG Shuo, LI Liang, et al. A fuzzy-inference-based reinforcement learning method of overtaking decision making for automated vehicles[J]. Proceedings of the institution of mechanical engineers, part D:journal of automobile engineering, 2022, 236(1): 75–83.
[23] JEON S, LEE K, KUM D. Overtaking decision and trajectory planning in highway via hierarchical architecture of conditional state machine and chance constrained model predictive control[J]. Robotics and autonomous systems, 2022, 151: 104014.
[24] LIU J C, JAYAKUMAR P, OVERHOLT J L, et al. The role of model fidelity in model predictive control based hazard avoidance in unmanned ground vehicles using LIDAR sensors[C]//Proceedings of ASME 2013 Dynamic Systems and Control Conference, Palo Alto: ASMI, 2014.
[25] 石贞洪. 基于模糊MPC和APF的智能汽车转向避撞控制研究[D]. 镇江: 江苏大学, 2020.
SHI Zhenhong. Study on steering avoidance control of intelligent vehicle based on fuzzy MPC and APF[D]. Zhenjiang: Jiangsu University, 2020.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems