Lane Change Decision Making and Planning of Intelligent Vehicles Based on GCN and QP

被引：0
作者：
Feng, Fuyong [1 ,2 ,3 ]
Wei, Chao [1 ,4 ]
Lü, Yanzhi [1 ]
He, Yuanhao [1 ]
机构：
[1] School of Mechanical Engineering, Beijing Institute of Technology, Beijing
[2] China North Artificial Intelligence & Innovation Research Institute, Beijing
[3] Collective Intelligence & Collaboration Laboratory, Beijing
[4] National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2024年 / 44卷 / 08期
关键词：
decision making; graph convolution network(GCN); intelligent vehicle; lane change; motion planning; quadratic programming(QP);
D O I：
10.15918/j.tbit1001-0645.2024.015
中图分类号：
学科分类号：
摘要：
To solve the problem of the interaction effects between vehicles in dynamic driving scenarios, an autonomous lane change decision making and motion planning method were proposed for intelligent vehicles based on graph convolution network (GCN) and quadratic programming (QP). Firstly, some interested regions were hierarchically modeled, and the global and local dynamic interaction information of the driving scene were aggregated in a form of graph-structured data, and the driving behavior instructions should take in the ego vehicle were output with the GCN. Then, combined with motion planning module, the free spaces were divided based on the local sub-graph, a quadratic programming model was constructed and solved to obtain collision-free motion trajectory satisfied with kinematics constraints, completing the autonomous lane change without collision finally. The results of simulation experiments and real vehicle verification show that the proposed method can provide better performance than the conventional decision making and motion planning method, showing better experimental success rate and scene generalization performance. © 2024 Beijing Institute of Technology. All rights reserved.
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页码：820 / 827
页数：7
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