Quantitative Identification of Strong-Interaction Vehicles in Autonomous Driving

Understanding vehicle-to-vehicle interactions is critical for safe and efficient autonomous driving in complex traffic scenarios. However, most existing behavior prediction and planning models implicitly account for interactions through soft weighting or metrics based on distance and risk, without a clear or quantitative definition of vehicle interaction. This paper proposes a metric called Vehicle Interaction Level (VIL) to quantify the degree to which a vehicle’s behavior is constrained by another vehicle. By applying a VIL threshold, strong-interaction vehicles are distinguished from weak ones. Then, the Feature Difference Level (FDL) is proposed to analyze feature-level behavioral differences across interaction categories, which is quantified using the Random Forest (RF) and the Gini Index (GI). Based on real-world vehicle trajectory pairs, an optimal VIL threshold of 0.2 is determined by maximizing FDL to identify strong-interaction vehicles, providing a more discriminative and broadly applicable interaction characterization than distance-based and risk-based metrics. Furthermore, the application of this strong-interaction vehicle identification method to trajectory prediction achieves consistently better performance than the baseline across multiple evaluation metrics, demonstrating its practical value for downstream autonomous driving tasks. Owing to its lightweight and model-agnostic formulation, the VIL-based method can be integrated into existing autonomous driving systems operating in highly interactive traffic scenarios.