Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization

Author:

Feng Fuyong12,Wei Chao13,Zhao Botong1,Lv Yanzhi1,He Yuanhao1

Affiliation:

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

2. China North Artificial Intelligence & Innovation Research Institute, Beijing 100072, China

3. National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology, Beijing 100081, China

Abstract

This paper considers the interactive effects between the ego vehicle and other vehicles in a dynamic driving environment and proposes an autonomous vehicle lane-changing behavior decision-making and trajectory planning method based on graph convolutional networks (GCNs) and multi-segment polynomial curve optimization. Firstly, hierarchical modeling is applied to the dynamic driving environment, aggregating the dynamic interaction information of driving scenes in the form of graph-structured data. Graph convolutional neural networks are employed to process interaction information and generate ego vehicle’s driving behavior decision commands. Subsequently, collision-free drivable areas are constructed based on the dynamic driving scene information. An optimization-based multi-segment polynomial curve trajectory planning method is employed to solve the optimization model, obtaining collision-free motion trajectories satisfying dynamic constraints and efficiently completing the lane-changing behavior of the vehicle. Finally, simulation and on-road vehicle experiments are conducted for the proposed method. The experimental results demonstrate that the proposed method outperforms traditional decision-making and planning methods, exhibiting good robustness, real-time performance, and strong scenario generalization capabilities.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Reference34 articles.

1. A Survey of Autonomous Driving: Common Practices and Emerging Technologies;Yurtsever;IEEE Access,2020

2. Decision-making analysis of vehicle autonomous driving behaviors for autonomous vehi-cles based on finite state machine;Ji;Automot. Technol.,2018

3. Xia, X.T., Pei, X.F., and Yu, J. (2021, January 19–21). Behavior planning and control of intelligence commercial vehicle based on finite state machine. Proceedings of the Annual Meeting of the Chinese Society of Automotive Engineering, Shanghai, China.

4. A human-like game theory-based controller for automatic lane changing;Yu;Transp. Res. Part C Emerg. Technol.,2018

5. Yoo, J.H., and Langari, R. (2012, January 17–19). Stackelberg Game Based Model of Highway Driving. Proceedings of the Asme Dynamic Systems & Control Conference Joint with the Jsme Motion & Vibration Conference, Fort Lauderdale, FL, USA.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3