[1]唐友名,孙冠豫,孙贵斌,等.基于城市超车工况的智能车辆避障规划方法研究[J].智能系统学报,2024,19(3):619-626.[doi:10.11992/tis.202209060]
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]
点击复制
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]
基于城市超车工况的智能车辆避障规划方法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第3期
页码:
619-626
栏目:
学术论文—机器人
出版日期:
2024-05-05
- Title:
- Autonomous vehicle trajectory planning based on urban overtaking conditions
- Keywords:
- automatic drive; artificial potential field method; model prediction control; overtaking conditions; trajectory planning; safe distance; vehicle dynamics; obstacle avoidance strategy
- 分类号:
- TP181；U46
- 文献标志码:
- 2023-07-31
- 摘要:
- 在城市道路行驶中，超车驾驶既是常见的驾驶行为，也是常见的交通事故工况之一。基于改进人工势场法（artificial potential field method）与模型预测控制(model predicition control)提出了一种避障规划控制方法。首先，构建目标点引力势场函数、道路及车道线势场函数以及障碍物车辆势场函数；其次，基于势场函数建立统一化模型函数；最后，对系统进行模拟实验评估。结果表明，提出的避障规划控制方法能够生成安全、平滑的路径，其前轮转角变化范围在[-0.67°, 1.02°]，质心侧偏角变化范围在[-0.22°, 0.32°]，具有稳定的控制能力。
- 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.
- 参考文献/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.
- 相似文献/References:
-
[1]葛园园,许有疆,赵帅,等.自动驾驶场景下小且密集的交通标志检测[J].智能系统学报,2018,13(3):366.[doi:10.11992/tis.201706040]
GE Yuanyuan,XU Youjiang,ZHAO Shuai,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13():366.[doi:10.11992/tis.201706040]
[2]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.[doi:10.11992/tis.201805004]
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():1040.[doi:10.11992/tis.201805004]
[3]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.[doi:10.11992/tis.202002010]
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():758.[doi:10.11992/tis.202002010]
[4]陆军,李杨,鲁林超.远距离和遮挡下三维目标检测算法研究[J].智能系统学报,2024,19(2):259.[doi:10.11992/tis.202301001]
LU Jun,LI Yang,LU Linchao.Long-distance and occluded 3D target detection algorithm[J].CAAI Transactions on Intelligent Systems,2024,19():259.[doi:10.11992/tis.202301001]
[5]胡丹丹,张忠婷.基于改进YOLOv5s的面向自动驾驶场景的道路目标检测算法[J].智能系统学报,2024,19(3):653.[doi:10.11992/tis.202206034]
HU Dandan,ZHANG Zhongting.Road target detection algorithm for autonomous driving scenarios based on improved YOLOv5s[J].CAAI Transactions on Intelligent Systems,2024,19():653.[doi:10.11992/tis.202206034]
备注/Memo
收稿日期:2022-09-28。
基金项目:国家自然科学基金项目（51975172）；国家高端外专项目（G20200221011）；福建省自然科学基金项目（2021J011197）；浙江省自然科学基金项目（LY19E050012）；厦门市重大科技项目（3502Z20201015）；厦门理工学院研究生科技创新项目（YKJCX2021024）.
作者简介:唐友名，教授，博士，《中国安全科学学报》首届青年编委，主要研究方向为智能驾驶与汽车安全、动力电池安全技术。主持省部级以上科研项目9项，获福建省青年科技奖、福建省科技进步二等奖和三等奖各1项（排名第1），获国家专利授权87件，发表学术论文40余篇，出版学术专著2部。E-mail：tangym1981@163.com;孙冠豫，硕士研究生，主要研究方向为智能驾驶与路径规划。作为参赛队队长荣获2022年第二十四届中国机器人及人工智能大赛百度Apollo 城市道路自动驾驶虚拟仿真赛福建赛区一等奖和全国总决赛三等奖（排名第一）。E-mail：sgy2020year@163.com;孙贵斌，副教授，主要研究方向为车辆安全技术、新能源汽车设计。主持省级以上科研项目4项，获福建省科技进步奖1项，获国家专利授权16件，发表学术论文10余篇，出版国家规划教材1部。 E-mail：sgbzxx@xmut.edu.cn
通讯作者:唐友名. E-mail：tangym1981@163.com
更新日期/Last Update:
1900-01-01