Looking for an out: Affordances, uncertainty and collision avoidance behavior of human drivers

2025-05-20

AI Summary

This study investigates human driver collision-avoidance decision-making and behavioral mechanisms under sudden, opposing lateral intrusion scenarios, focusing on how uncertainty in actionable paths (affordances) influences evasive maneuver selection and collision outcomes. Method: Integrating ecological psychology’s affordance concept into driving safety modeling for the first time, we propose a quantifiable framework based on reachable sets. Using driving simulator experiments, scenario dynamic modeling, reachable-set computation, and behavioral clustering analysis, we systematically characterize driver responses under varying intrusion uncertainties. Contribution/Results: We identify intrusion trajectory uncertainty as the dominant factor governing avoidance strategy selection and collision outcomes. We develop the first computationally tractable, affordance-driven explanatory model of collision-avoidance behavior. The model uncovers inherent post-hoc attribution biases in human judgment, establishing a novel, human-centered paradigm for the design, verification, and evaluation of ADAS and autonomous driving systems.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Reasoning under Uncertainty: Uncertainty Representations

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
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Understanding collision avoidance behavior is of key importance in traffic safety research and for designing and evaluating advanced driver assistance systems and autonomous vehicles. While existing experimental work has primarily focused on response timing in traffic conflicts, the goal of the present study was to gain a better understanding of human evasive maneuver decisions and execution in collision avoidance scenarios. To this end, we designed a driving simulator study where participants were exposed to one of three surprising opposite direction lateral incursion (ODLI) scenario variants. The results demonstrated that both the participants' collision avoidance behavior patterns and the collision outcome was strongly determined by the scenario kinematics and, more specifically, by the uncertainty associated with the oncoming vehicle's future trajectory. We discuss pitfalls related to hindsight bias when judging the quality of evasive maneuvers in uncertain situations and suggest that the availability of escape paths in collision avoidance scenarios can be usefully understood based on the notion of affordances, and further demonstrate how such affordances can be operationalized in terms of reachable sets. We conclude by discussing how these results can be used to inform computational models of collision avoidance behavior.

Problem

• Understanding human evasive maneuver decisions in collision scenarios
• Analyzing impact of scenario kinematics on collision avoidance behavior
• Exploring affordances for escape paths in uncertain driving situations

Innovation

• Used driving simulator for human evasive maneuver study
• Analyzed scenario kinematics and trajectory uncertainty impact
• Operationalized escape paths using affordances and reachable sets