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

arXiv: https://arxiv.org/abs/2508.07080 · HTML: https://mlsanity.com/paper/2508.07080/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach](https://mlsanity.com/paper/2005.11064/) (2020)
- [AMZ Driverless: The Full Autonomous Racing System](https://mlsanity.com/paper/1905.05150/) (2019)
- [Interactive multi-modal motion planning with Branch Model Predictive Control](https://mlsanity.com/paper/2109.05128/) (2021)
- [Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data](https://mlsanity.com/paper/2010.14712/) (2020)
- [Potential Game-Based Decision-Making for Autonomous Driving](https://mlsanity.com/paper/2201.06157/) (2022)
- [Automated Lane Merging via Game Theory and Branch Model Predictive Control](https://mlsanity.com/paper/2311.14916/) (2023)

## Abstract (arXiv)

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.