Adaptive Evolutionary Framework for Safe, Efficient, and Cooperative Autonomous Vehicle Interactions

2025-09-09

AI Summary

To address the challenges of inefficient and unsafe interactions among autonomous vehicles (AVs) under decentralized control—where balancing safety and efficiency remains difficult—this paper proposes a distributed adaptive evolutionary framework integrating evolutionary game theory with causal inference. The framework employs a causal evaluation module to dynamically adjust strategy mutation rates and evolutionary speeds, enabling data-driven, decentralized coordination and optimization of driving policies. Unlike conventional rule-based, optimization-driven, or static game-theoretic approaches, our method significantly reduces computational overhead while enhancing policy adaptability and cooperative efficiency. Simulation results demonstrate that, in multi-vehicle interactive scenarios, the framework effectively reduces collision rates, increases minimum safe inter-vehicle distances, and improves average cruising speed. Overall performance surpasses both Nash equilibrium and Stackelberg game benchmarks, confirming its superiority in real-time, safety-critical AV coordination.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Ensuring safe autonomous vehicle interactions without centralized control
• Balancing passenger demands with overall traffic efficiency optimization
• Overcoming limitations of traditional rule-based and game-theoretic approaches

Innovation

• Evolutionary Game Theory-based decentralized framework
• Causal evaluation module optimizes evolutionary rate
• Adaptive strategy evolution from historical interactions