Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model

2026-04-21

AI Summary

This study addresses spatial sharing conflicts among road users to enhance traffic safety and support autonomous driving deployment by introducing, for the first time, an active inference framework for multi-agent interaction modeling. By integrating three mechanisms—behavioral coupling, normative expectations, and explicit communication—the approach quantifies and reduces interaction uncertainty. Evaluated in a simplified intersection scenario, the results demonstrate that adherence to social norms and effective communication significantly improve conflict resolution success rates. However, when agents exhibit unexpected behaviors or convey misleading information, these same mechanisms can paradoxically increase collision risk. The work elucidates the functional roles and inherent vulnerabilities of implicit and explicit communication, as well as reliance on shared norms, in cooperative decision-making among interacting agents.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• space-sharing conflicts
• road user interactions
• uncertainty reduction
• autonomous vehicles
• traffic safety

Innovation

• active inference
• uncertainty reduction
• road user interaction
• implicit communication
• normative expectations