Sociality Probe: Game-Theoretic Inverse Reinforcement Learning for Modeling and Quantifying Social Patterns in Driving Interaction

Autonomous vehicles (AV) are consistently criticized for their inadequacies in harmoniously interacting with human-driven vehicles (HV), primarily attributed to the lack of sociality, a key human trait that balances individual and group rewards. Understanding sociality is essential for smooth AV navigation but remains challenging. To address this, we propose a Game-Theoretic Inverse Reinforcement Learning (GT-IRL) approach to quantify individualized sociality in driving interaction. Our approach identifies the sociality-preference parameters of a pre-designed reward function that integrates the ego agent's rewards and the group rewards shared by all the interacting agents. Instead of presuming an agent is propelled by the maximization of its own rewards, the game-theoretical mechanism is utilized within the IRL structure to capture the fact that human drivers take into account others' interests. We validated our method using human driving data from an unprotected left-turn scenario. The results demonstrate that the proposed GT-IRL outperforms state-of-the-art methods in better reproducing the evolution of the left-turn interaction at both semantic and trajectory levels. Additionally, cross-dataset analysis reveals variations in sociality due to geographical differences (China vs. the U.S.) and the nature of the interacting entities (AV vs. HV or HV vs. HV).