用于建模竞争性人类驾驶的博弈论逆强化学习：切入预测研究

中文摘要 AI 辅助

捕捉竞争性人类驾驶中固有的策略决策对自动驾驶汽车安全和交通模拟至关重要。本研究表明，博弈论逆强化学习（IRL）为应对这一挑战提供了稳健框架。我们开展全面分析，对比数据驱动的IRL模型与已确立的基于物理的博弈论方法，用于预测具有安全关键性质的激进切入车道变更行为。采用高保真highD数据集，我们系统开发并评估一系列特征复杂度递增的IRL模型。结果显示显著优势：表现最佳的IRL模型整体预测准确率超75%，切入预测的精确率最高达51%、召回率最高达49%，相比基准的基于物理的博弈论方法（在高风险场景中精确率仅为4.4%）有显著提升。分析还揭示明确权衡：纳入粒度化的瞬时特征可提高精确率，而添加时间一致性特征可最大化召回率。这些发现表明，基于IRL的模型可有效弥合微观驾驶员意图与宏观安全结果之间的差距，为混合自主环境下的交互建模提供更可靠基础。

英文摘要

Capturing the strategic decision-making inherent in competitive human driving is critical for autonomous vehicle safety and traffic simulation. This study demonstrates that game-theoretic Inverse Reinforcement Learning (IRL) provides a robust framework for this challenge. We present a comprehensive analysis comparing data-driven IRL models against an established physics-based game-theoretic approach for predicting aggressive, safety-critical cut-in lane changes. Using the high-fidelity highD dataset, we systematically develop and evaluate a series of IRL models with increasing feature complexity. Our results reveal significant advantages: the best-performing IRL models achieve an overall prediction accuracy exceeding 75 percent while maintaining a Cut-In precision up to 51 percent and recall up to 49 percent. This represents a significant improvement over the established physics-based benchmark, which achieved only 4.4 percent precision in these high-stakes scenarios. The analysis reveals a clear trade-off: incorporating granular, instantaneous features yields higher precision, while adding temporal consistency features maximizes recall. These findings suggest that IRL-based models can effectively bridge the gap between microscopic driver intent and macroscopic safety outcomes, providing a more reliable foundation for modeling interactions in mixed-autonomy environments.

发表机构

• University of Wyoming（怀俄明大学）

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