用图强化学习让自动驾驶车在不同驾驶风格的交通中更安全高效
Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
- 构建异构图捕捉车辆间复杂交互，融合专家知识生成驾驶指令
- 在四岔路口测试中，安全性、效率、收敛速度均优于基线方法
- 适合研究自动驾驶协同决策与复杂交通场景建模的工程师
在包含多种驾驶风格的异构交通环境中，自动驾驶车辆（AV）面临复杂动态交互带来的重大挑战。本文提出一种增强专家系统的异构图强化学习（GRL）框架，以提升决策性能。首先引入异构图表示来刻画车辆间的复杂交互；随后设计融合专家模型的异构图神经网络（HGNN-EM），有效编码多样车辆特征并生成基于领域知识的驾驶指令；同时采用双深度Q网络（DDQN）训练决策模型。在典型四岔路口的案例研究中，针对人类车辆（HVs）的不同驾驶风格，所提方法在安全性、效率、稳定性及收敛速度方面均优于多个基线方法，且保持良好的实时性能。
原文摘要 · Abstract (English)
Navigating heterogeneous traffic environments with diverse driving styles poses a significant challenge for autonomous vehicles (AVs) due to their inherent complexity and dynamic interactions. This paper addresses this challenge by proposing a heterogeneous graph reinforcement learning (GRL) framework enhanced with an expert system to improve AV decision-making performance. Initially, a heterogeneous graph representation is introduced to capture the intricate interactions among vehicles. Then, a heterogeneous graph neural network with an expert model (HGNN-EM) is proposed to effectively encode diverse vehicle features and produce driving instructions informed by domain-specific knowledge. Moreover, the double deep Q-learning (DDQN) algorithm is utilized to train the decision-making model. A case study on a typical four-way intersection, involving various driving styles of human vehicles (HVs), demonstrates that the proposed method has superior performance over several baselines regarding safety, efficiency, stability, and convergence rate, all while maintaining favorable real-time performance.
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