用风格化建模让自动驾驶更懂不同司机行为。
Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles
- 基于平均场博弈构建多车交互决策框架，用参数化风格表征驾驶行为。
- 六种风格组合下15辆车场景零碰撞，显著优于传统博弈方法。
- 适合关注真实交通中安全与个性化决策的自动驾驶研究者。
本文提出一种基于平均场博弈（MFG）的异构交通下自动驾驶决策框架。为捕捉多样化的驾驶行为，我们设计了一种量化驾驶风格表示法，将抽象特质映射为速度、安全系数和反应时间等参数，并通过空间影响场模型嵌入MFG。为保障密集交通中的安全运行，引入一种融合动态安全距离、碰撞时间分析和多层约束的安全变道算法。采用真实世界NGSIM数据进行风格校准与实证验证。实验结果表明，在六种风格组合、两组15辆车辆场景及基于NGSIM的测试中均实现零碰撞，持续优于传统博弈基准方法。整体方案为真实自动驾驶应用提供了可扩展、可解释且行为感知的规划框架。
原文摘要 · Abstract (English)
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.
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