🤖 AI Summary
This study addresses the challenge of predicting high-risk cut-in maneuvers by human drivers in competitive driving scenarios by proposing a data-driven modeling approach that integrates game theory with inverse reinforcement learning. For the first time, game-theoretic inverse reinforcement learning is applied to cut-in behavior prediction, leveraging high-dimensional driving features and trained on the high-fidelity highD dataset. The work systematically uncovers the trade-off mechanism between instantaneous and temporally consistent features in balancing precision and recall. Experimental results demonstrate that the proposed method achieves an overall prediction accuracy exceeding 75%, with a cut-in prediction precision of 51% and recall of 49%, substantially outperforming conventional physics-based game-theoretic approaches, which yield only 4.4% precision.
📝 Abstract
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.