An Evolutionary Game-Theoretic Merging Decision-Making Considering Social Acceptance for Autonomous Driving

TL;DR

This paper introduces an evolutionary game-theoretic framework for autonomous vehicle merging decisions that considers social acceptance, improving safety, efficiency, and comfort through real-time adaptation and human-like interaction modeling.

Contribution

It presents a novel EGT-based merging decision algorithm that incorporates social acceptance and real-time driving style estimation, addressing limitations of existing methods.

Findings

Enhanced merging efficiency and safety compared to traditional methods.

Improved driving comfort for both AVs and MVs.

Effective real-time adaptation to human driving styles.

Abstract

Highway on-ramp merging is of great challenge for autonomous vehicles (AVs), since they have to proactively interact with surrounding vehicles to enter the main road safely within limited time. However, existing decision-making algorithms fail to adequately address dynamic complexities and social acceptance of AVs, leading to suboptimal or unsafe merging decisions. To address this, we propose an evolutionary game-theoretic (EGT) merging decision-making framework, grounded in the bounded rationality of human drivers, which dynamically balances the benefits of both AVs and main-road vehicles (MVs). We formulate the cut-in decision-making process as an EGT problem with a multi-objective payoff function that reflects human-like driving preferences. By solving the replicator dynamic equation for the evolutionarily stable strategy (ESS), the optimal cut-in timing is derived, balancing efficiency, comfort, and safety for both AVs and MVs. A real-time driving style estimation algorithm is proposed to adjust the game payoff function online by observing the immediate reactions of MVs. Empirical results demonstrate that we improve the efficiency, comfort and safety of both AVs and MVs compared with existing game-theoretic and traditional planning approaches across multi-object metrics.