DataSheet8_Deviant Behavior of Pedestrians: A Risk Gamble or Just Against Automated Vehicles? How About Social Control?.CSV

Recent evidence suggests that the assumed conflict-avoidant programming of autonomous vehicles will incentivize pedestrians to bully them. However, this frequent argument disregards the embedded nature of social interaction. Rule violations are socially sanctioned by different forms of social control, which could moderate the rational incentive to abuse risk-avoidant vehicles. Drawing on a gamified virtual reality (VR) experiment (n = 36) of urban traffic scenarios, we tested how vehicle type, different forms of social control, and monetary benefit of rule violations affect pedestrians’ decision to jaywalk. In a second step, we also tested whether differences in those effects exist when controlling for the risk of crashes in conventional vehicles. We find that individuals do indeed jaywalk more frequently when faced with an automated vehicle (AV), and this effect largely depends on the associated risk and not their automated nature. We further show that social control, especially in the form of formal traffic rules and norm enforcement, can reduce jaywalking behavior for any vehicle. Our study sheds light on the interaction dynamics between humans and AVs and how this is influenced by different forms of social control. It also contributes to the small gamification literature in this human–computer interaction.

近期研究证据表明，自动驾驶车辆（Automated Vehicle, AV）预设的避冲突编程，会诱使行人对其进行不当刁难。然而，这一常见论点忽略了社会互动的嵌入性本质。不同形式的社会管控会对违规行为施加社会制裁，或可削弱行人针对避风险车辆实施不当行为的理性动机。
本研究依托一项针对城市交通场景的游戏化虚拟现实（Virtual Reality, VR）实验（样本量n=36），探究了车辆类型、不同形式的社会管控，以及违规过马路的金钱收益，如何影响行人乱穿马路的决策。在此基础上，本研究进一步检验了，在控制传统机动车碰撞风险的前提下，上述影响是否存在显著差异。
研究结果显示，当行人面对自动驾驶车辆时，乱穿马路的频率确实更高，且这一效应主要取决于相关碰撞风险，而非车辆的自动驾驶属性。此外，本研究还证实，各类社会管控手段——尤其是正式交通规则与规范执行形式——能够降低所有车辆场景下的行人乱穿马路行为。
本研究阐明了人类与自动驾驶车辆之间的互动动态，以及不同形式的社会管控如何影响这一互动过程；同时，本研究也为该人机交互（Human-Computer Interaction）领域中体量较小的游戏化研究文献提供了新的学术贡献。