# Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles

arXiv: https://arxiv.org/abs/2509.00981 · HTML: https://mlsanity.com/paper/2509.00981/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.16196/) (2026)
- [Traffic flow forecasting, STL decomposition, Hybrid model, LSTM, ARIMA, XGBoost, Intelligent transportation systems](https://mlsanity.com/paper/2510.23668/) (2025)

## Abstract (arXiv)

This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving style representation that maps abstract traits to parameters such as speed, safety factors, and reaction time. These parameters are embedded into the MFG through a spatial influence field model. To ensure safe operation in dense traffic, we introduce a safety-critical lane-changing algorithm that leverages dynamic safety margins, time-to-collision analysis, and multi-layered constraints. Real-world NGSIM data is employed for style calibration and empirical validation. Experimental results demonstrate zero collisions across six style combinations, two 15-vehicle scenarios, and NGSIM-based trials, consistently outperforming conventional game-theoretic baselines. Overall, our approach provides a scalable, interpretable, and behavior-aware planning framework for real-world autonomous driving applications.