Automated vehicles (AVs) are commonly programmed to yield unconditionally to pedestrians in the interest of safety. However, this design choice can give rise to the Freezing Robot Problem in which pedestrians learn to assert priority at every interaction, causing vehicles to stall and make no progress. The game theoretic Sequential Chicken model has shown that, like human drivers, AVs can resolve this problem by trading credible threats of very small risks of collision or larger risks of less severe invasion of personal space against the value of time due to yielding delays. This paper presents the first demonstration and evaluation of this approach using a real AV with human subjects and shows that pedestrian behavior under experimentally constrained safety conditions can be well fitted by Sequential Chicken, with a low time value of collision, suggestive of their planning to avoid proxemic personal space penalties as well as actual collisions.

翻译：自动驾驶汽车通常被编程为无条件为行人让行以确保安全。然而，这种设计选择可能引发“冻结机器人问题”——行人学会在每次交互中主张优先权，导致车辆停滞不前、无法推进。博弈论中的“序贯斗鸡”模型已表明，如同人类驾驶员一般，自动驾驶汽车通过权衡碰撞极小风险的可靠威胁、侵犯个人空间程度较轻但概率较大的侵入风险，与让行延误导致的时间价值损失，可解决该问题。本文首次基于真实自动驾驶汽车与人类受试者，演示并评估了该方法：在实验约束的安全条件下，行人行为可被“序贯斗鸡”模型良好拟合，其中碰撞的时间价值较低，表明行人的规划不仅规避实际碰撞，还试图避免因接近他人而产生的个人空间惩罚。