Feng Fuyong, Wei Chao, Zhao Botong, Lv Yanzhi, He Yuanhao
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
China North Artificial Intelligence & Innovation Research Institute, Beijing 100072, China.
Sensors (Basel). 2024 Feb 23;24(5):1439. doi: 10.3390/s24051439.
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.
本文考虑了动态驾驶环境中自车与其他车辆之间的交互作用，提出了一种基于图卷积网络（GCN）和多段多项式曲线优化的自动驾驶车辆变道行为决策与轨迹规划方法。首先，对动态驾驶环境进行分层建模，以图结构数据的形式聚合驾驶场景的动态交互信息。采用图卷积神经网络处理交互信息并生成自车的驾驶行为决策指令。随后，基于动态驾驶场景信息构建无碰撞可行驶区域。采用基于优化的多段多项式曲线轨迹规划方法求解优化模型，得到满足动态约束的无碰撞运动轨迹，高效完成车辆的变道行为。最后，对所提方法进行了仿真和实车实验。实验结果表明，所提方法优于传统的决策和规划方法，具有良好的鲁棒性、实时性和较强的场景泛化能力。
