|Table of Contents|

Dynamic obstacle avoidance control of intelligent vehicle on large curvature roads considering trajectory prediction(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年4期
Page:
161-174
Research Field:
机械与汽车工程
Publishing date:

Info

Title:
Dynamic obstacle avoidance control of intelligent vehicle on large curvature roads considering trajectory prediction
Author(s):
SHI Pei-long1;  WANG Cai-rui1; 2;  MA Qiang1;  LIU Rui1;  ZHAO Xuan1
(1. School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China; 2. BYD Auto Shanghai, Shanghai 201611, China)
Keywords:
automotive engineering;  trajectory prediction;  model predictive control;  tracking control;  obstacle avoidance trajectory planning;  large curvature road condition
PACS:
U461.5
DOI:
10.19721/j.cnki.1671-8879.2024.04.015
Abstract:
In order to improve the dynamic obstacle avoidance safety of intelligent vehicles on high curvature roads, a trajectory prediction method based on real-time trajectory update long short term memory(U-LSTM)neural network and a dynamic obstacle avoidance control method for intelligent vehicles based on fuzzy control replanning logic strategy was proposed. A trajectory prediction model for U-LSTM neural network was established by extracting large curvature road parameters from the MATLABtoolbox, utilizing the current time-domain real trajectory point information, and training an updated iterative method. Considering the nonlinear relationship between intelligent vehicle tracking, avoidance, and surrounding information in high curvature road scenes, a replanning obstacle avoidance logic strategy based on fuzzy control is proposed. Dynamic programming and quadratic programming algorithms, as well as S-T graph method, was used to optimize the trajectory tracking and speed control of local paths. By establishing tracking error models and speed tracking models, vehicle lateral and longitudinal control was achieved. An MPC path tracking and speed tracking controller for vehicle lateral and longitudinal control was designed, and a joint simulation model was constructed to verify the rationality and effectiveness of trajectory prediction and control methods. The results show that the intelligent vehicle dynamic obstacle avoidance control method proposed in the article can accurately predict the vehicle's trajectory on high curvature roads, and the real-time trajectory U-LSTM neural network can effectively improve prediction accuracy. The replanning obstacle avoidance logic strategy can effectively avoid dynamic obstacles and meet the accuracy of vehicle longitudinal and lateral tracking control, ensuring the driving stability of the vehicle.7 tabs, 20 figs, 25 refs.

References:

[1] GONZALEZ D,PEREZ J,MILANES V,et al.A review of motion planning techniques for automated vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2015,17(4):1135-1145.
[2]陈政宏,李爱娟,邱绪云,等.智能车环境视觉感知及其关键技术研究现状[J].河北科技大学学报,2019,40(1):15-23.
CHEN Zheng-hong,LI Ai-juan,QIU Xu-yun,et al.Survey of environment visual perception for intelligent vehicle and its supporting key technologies[J].Journal of Hebei University of Science and Technology,2019,40(1):15-23
[3]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.
[4]李 柏,张友民,邵之江.自动驾驶车辆运动规划方法综述[J].控制与信息技术,2018(6):1-6.
LI Bai,ZHANG You-min,SHAO Zhi-jiang.Motion planning methodologies for automated vehicles:A critical review[J].Control and Information Technology,2018(6):1-6.
[5]JIN L.Serialized recommendation prediction for steering point behavior of intelligent transportation vehicles based on deep learning[J].IEEE Transactions on Intelligent Transportation Systems,2023,24(11):13350-13358.
[6]LIU P F,FAN W.Extreme gradient boosting(XGBoost)model for vehicle trajectory prediction in connected and autonomous vehicle environment[J].Faculty of Transport and Traffic Sciences,2021,33(5):767-74.
[7]GENG M S,LI J Y,XIA Y J,et al.A physics-informed transformer model for vehicle trajectory prediction on highways[J].Transportation Research Part C,2023,154:1-28.
[8]DJURIC N,RADOSAVLJEVIC V,CUI H,et al.Motion prediction of traffic actors for autonomous driving using deep convolutional networks[EB/OL].(2023-06-28)][2024-06-11].https://doi.org/10.48550/arXiv.1808.05819.
[9]WANG M,LU J.Vehicle trajectory prediction based on LSTM and graph convolutional networks[C]//IEEE.Proceedings of 7th International Conference on Electrical,Mechanical and Computer Engineering.New York:IEEE,2023:884-888.
[10]KIM B,KANG C,KIM J,et al.Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network[C]//IEEE.Proceedings of 2017 20th IEEE International Conference on Intelligent Transportation Systems(ITSC).New York:IEEE,2017:399-404.
[11]DEO N,TRIVEDI M.Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs[C]//IEEE.Proceedings of 2018 IEEE Intelligent Vehicles Symposium(Ⅳ).New York:IEEE,2018:1179-1184.
[12]ALAHI A,GOEL K,RAMANATHAN V,et al.Social LSTM:Human trajectory prediction in crowded spaces[C]//IEEE.Proceedings of 29th IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE,2016:961-971.
[13]WERLING M,ZIEGLER J,KAMMEL S,et al.Optimal trajectory generation for dynamic street scenarios in a Frenet frame[C]//IEEE.Proceedings of 2010 IEEE International Conference on Robotics and Automation.New York:IEEE,2010:987-993.
[14]LIN P,CHOI W,CHUNG C.Local path planning using artificial potential field for waypoint tracking with collision avoidance[C]//IEEE.Proceedings of 2020 23rd IEEE International Conference on Intelligent Transportation Systems(ITSC).New York:IEEE,2020:1-7.
[15]修彩靖,陈 慧.基于改进人工势场法的无人驾驶车辆局部路径规划的研究[J].汽车工程,2013,35(9):808-811.
XIU Cai-jing,CHEN Hui.A research on local path planning for autonomous vehicles based on improved APF method[J].Automotive Engineering,2013,35(9):808-811.
[16]ZHAO B,WANG H,LI Q,et al.PID trajectory tracking control of autonomous ground vehicle based on genetic algorithm[C]//IEEE.Proceedings of 2019 Chinese Control and Decision Conference(CCDC).New York:IEEE,2019:3677-3682.
[17]赵 凯,朱 愿,冯明月,等.基于多点序列预瞄的自动驾驶汽车路径跟踪算法研究[J].汽车技术,2018(11):1-5.
ZHAO Kai,ZHU Yuan,FENG Ming-yue,et al.Research on path tracking algorithm of autonomous vehicles based on multi-point sequence preview[J].Automobile Technology,2018(11):1-5.
[18]SUN Q,LI M,CHENG J,et al.Path tracking control of wheeled mobile robot based on improved pure pursuit algorithm[C]//IEEE.Proceedings of 2019 Chinese Automation Congress(CAC).New York:IEEE,2019:4239-4244.
[19]陈 亮,秦兆博,孔伟伟,等.基于最优前轮侧偏力的智能汽车 LQR 横向控制[J].清华大学学报(自然科学版),2021,61(9):906-912.
CHEN Liang,QIN Zhao-bo,KONG Wei-wei,et al.Lateral control using LQR for intelligent vehicles based on the optimal front-tire lateral force[J].Journal of Tsinghua University(Natural Science Edition),2021,61(9):906-912.
[20]丁幼春,廖庆喜,黄海东,等.联合收获机大曲率路径视觉导航方法[J].农业机械学报,2011,42(增1):122-127.
DING You-chun,LIAO Qing-xi,HUANG Dong-hai,et al.Large curvature path detection for combine harvester based on vision navigation[J].Transactions of the Chinese Society for Agricultural Machinery,2011,42(S1):122-127.
[21]沈 峘,凌 锐,李舜酩.基于预瞄最优曲率模型的大曲率转向控制方法[J].中国机械工程,2012,23(17):2111-2116.
SHEN Heng,LING Rui,LI Shun-ming.Steering control on large curvature road based on preview optimal curvature model[J].China Mechanical Engineering,2012,23(17):2111-2116.
[22]杨 浩,黄 江,李正网,等.基于曲率与车速的两点智能控制驾驶员模型[J].汽车技术,2017,48(8):38-42.
YANG Hao,HUANG Jiang,LI Zheng-wang,et al.Two point intelligent control driver model based on curvature and speed[J].Automobile Technology,2017,48(8):38-42.
[23]张枫毅,王莉华,叶文静.基于多层级LSTM的铝板缺陷检测[J].力学学报,2023,55(11):2566-2576.
ZHANG Feng-yi,WANG Li-hua,YE Wen-jing.Aluminum plate defect detection based on multilevel LSTM[J].Chinese Journal of Theoretical and Applied Mechanics,2023,55(11):2566-2576.
[24]武小花,余忠伟,朱张玲,等.燃料电池公交车模糊能量管理策略[J].吉林大学学报(工学版),2022,52(9):2077-2084.
WU Xiao-hua,YU Zhong-wei,ZHU Zhang-ling,et al.Fuzzy energy management strategy of fuel cell buses[J].Journal of Jilin University(Engineering and Technology Edition),2022,52(9):2077-2084.
[25]龚建伟,姜 岩,徐 威.无人驾驶车辆模型预测控制[M].北京:北京理工大学出版社,2014.
GONG Jian-wei,JIANG Yan,XU Wei.Model predictive control for self-driving vehicle[M].Beijing:Beijing Institute of Technology Press,2014.

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Last Update: 2024-07-10