Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model
This work addresses traffic safety and autonomous vehicle deployment by providing a computational framework for road user interactions, though it is incremental as it builds on an existing model.
The paper tackled the problem of how road users resolve space-sharing conflicts by extending an active inference-based driver behavior model to simulate interactions between two agents, showing that normative and explicit communication cues can increase successful conflict resolution in a simplified intersection scenario, but may lead to collisions if expectations are violated.
Understanding how road users resolve space-sharing conflicts is important both for traffic safety and the safe deployment of autonomous vehicles. While existing models have captured specific aspects of such interactions (e.g., explicit communication), a theoretically-grounded computational framework has been lacking. In this paper, we extend a previously developed active inference-based driver behavior model to simulate interactive behavior of two agents. Our model captures three complementary mechanisms for uncertainty reduction in interaction: (i) implicit communication via direct behavioral coupling, (ii) reliance on normative expectations (stop signs, priority rules, etc.), and (iii) explicit communication. In a simplified intersection scenario, we show that normative and explicit communication cues can increase the likelihood of a successful conflict resolution. However, this relies on agents acting as expected. In situations where another agent (intentionally or unintentionally) violates normative expectations or communicates misleading information, reliance on these cues may induce collisions. These findings illustrate how active inference can provide a novel framework for modeling road user interactions which is also applicable in other fields.