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.

翻译：理解碰撞规避行为对于交通安全研究以及设计和评估高级驾驶辅助系统与自动驾驶车辆至关重要。尽管现有实验工作主要关注交通冲突中的响应时机，但本研究旨在更深入地理解人类在碰撞规避场景中的规避机动决策与执行。为此，我们设计了一项驾驶模拟器研究，参与者被随机分配至三种不同的对向侧向侵入（ODLI）场景变体之一。结果表明，参与者的碰撞规避行为模式和碰撞结果均强烈受场景运动学特性影响，更具体地说，受与来车未来轨迹相关的不确定性所决定。我们讨论了在不确定情境下评判规避机动质量时可能存在的后见之明偏差陷阱，并提出基于可供性概念可有效理解碰撞规避场景中逃生路径的可用性，进一步演示了如何通过可达集对这类可供性进行可操作化表征。最后，我们探讨了如何利用这些结果为碰撞规避行为的计算建模提供理论依据。