Intention-Inspired Recognition and Quantification for Driver Interactions in Traffic Flows

Appropriate interaction with human-driven vehicles is crucial for advancing autonomous vehicles from demonstrations in controlled environments to applications in open-road scenarios. Accurately recognizing and quantifying driver interaction behaviors is essential for understanding their interaction intentions and subsequently formulating suitable responses. Previous studies have primarily focused on the generation of interaction behaviors. However, insufficient attention has been paid to the recognition and quantification of driver interaction behaviors. We establish a higher-order traffic flow network and present mathematical definitions for driver interaction behaviors and their strengths based on this framework. Furthermore, we find that humans identify interaction behaviors during driving by perceiving conflicts between different drivers’ intentions. Therefore, we propose a driver resistance field model (DRFM) to characterize driving intentions and describe these conflicts through operations within the resistance fields, enabling the recognition of interactions. Through computational social experiments and real-world experiments, the DRFM is demonstrated to accurately quantify the strength of driver-driver interactions. The analysis of extensive cases indicates that the quantification from the DRFM effectively capture the dynamics of interaction behaviors, aligning with human cognition of driver interactions, with remarkably higher correlation coefficients compared to baseline methods.