Modern transportation systems face significant challenges in ensuring road safety, given serious injuries caused by road accidents. The rapid growth of autonomous vehicles (AVs) has prompted new traffic designs that aim to optimize interactions among AVs. However, effective interactions between AVs remains challenging due to the absence of centralized control. Besides, there is a need for balancing multiple factors, including passenger demands and overall traffic efficiency. Traditional rule-based, optimization-based, and game-theoretic approaches each have limitations in addressing these challenges. Rule-based methods struggle with adaptability and generalization in complex scenarios, while optimization-based methods often require high computational resources. Game-theoretic approaches, such as Stackelberg and Nash games, suffer from limited adaptability and potential inefficiencies in cooperative settings. This paper proposes an Evolutionary Game Theory (EGT)-based framework for AV interactions that overcomes these limitations by utilizing a decentralized and adaptive strategy evolution mechanism. A causal evaluation module (CEGT) is introduced to optimize the evolutionary rate, balancing mutation and evolution by learning from historical interactions. Simulation results demonstrate the proposed CEGT outperforms EGT and popular benchmark games in terms of lower collision rates, improved safety distances, higher speeds, and overall better performance compared to Nash and Stackelberg games across diverse scenarios and parameter settings.

翻译：鉴于交通事故造成的严重伤害，现代交通系统在保障道路安全方面面临重大挑战。自动驾驶车辆的快速增长催生了旨在优化自动驾驶车辆间交互的新型交通设计。然而，由于缺乏集中控制，自动驾驶车辆之间的有效交互仍然具有挑战性。此外，还需要平衡包括乘客需求和整体交通效率在内的多重因素。传统的基于规则、基于优化以及博弈论的方法在应对这些挑战时各有局限：基于规则的方法在复杂场景中难以适应和泛化；基于优化的方法通常需要较高的计算资源；而博弈论方法（如Stackelberg博弈和Nash博弈）则在协作环境中存在适应性有限和潜在效率低下的问题。本文提出一种基于进化博弈论的自动驾驶车辆交互框架，该框架通过利用去中心化的自适应策略进化机制克服了上述局限。我们引入了一个因果评估模块，通过从历史交互中学习来优化进化速率，从而平衡突变与进化。仿真结果表明，在不同场景和参数设置下，所提出的因果评估模块在碰撞率、安全距离、行驶速度及整体性能方面均优于基础进化博弈论以及流行的基准博弈方法（如Nash博弈和Stackelberg博弈）。