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

TL;DR

This study investigates how human drivers make evasive decisions in collision scenarios, emphasizing the role of scenario kinematics and uncertainty, with implications for designing autonomous vehicle systems.

Contribution

It introduces a driving simulator experiment analyzing human evasive behavior under uncertainty, linking affordances to collision avoidance decisions.

Findings

Behavior strongly influenced by scenario kinematics and uncertainty.

Hindsight bias affects judgment of evasive maneuvers.

Operationalization of affordances via reachable sets.

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.