Title: Socially Adaptive Autonomous Vehicles: Effects of Contingent Driving Behavior on Drivers' Experiences Show Chinese title

Title: 社会适应性自动驾驶车辆：条件驾驶行为对驾驶员体验的影响

Abstract: Social scientists have argued that autonomous vehicles (AVs) need to act as effective social agents; they have to respond implicitly to other drivers' behaviors as human drivers would. In this paper, we investigate how contingent driving behavior in AVs influences human drivers' experiences. We compared three algorithmic driving models: one trained on human driving data that responds to interactions (a familiar contingent behavior) and two artificial models that intend to either always-yield or never-yield regardless of how the interaction unfolds (non-contingent behaviors). Results show a statistically significant relationship between familiar contingent behavior and positive driver experiences, reducing stress while promoting the decisive interactions that mitigate driver hesitance. The direct relationship between familiar contingency and positive experience indicates that AVs should incorporate socially familiar driving patterns through contextually-adaptive algorithms to improve the chances of successful deployment and acceptance in mixed human-AV traffic environments. Show Chinese abstract

Abstract: 社会科学家认为，自动驾驶汽车（AVs）需要作为有效的社会代理发挥作用；它们必须像人类驾驶员一样，对其他驾驶员的行为做出隐含的反应。 在本文中，我们研究了自动驾驶汽车中的偶然驾驶行为如何影响人类驾驶员的体验。 我们比较了三种算法驾驶模型：一种是基于人类驾驶数据训练的模型，该模型会对互动做出反应（一种熟悉的偶然行为），以及两种人工模型，这两种模型旨在无论互动如何展开都总是让行或从不让行（非偶然行为）。 结果表明，熟悉的偶然行为与积极的驾驶员体验之间存在统计学上的显著关系，在减少压力的同时促进了能够缓解驾驶员犹豫的果断互动。 熟悉偶然性与积极体验之间的直接关系表明，自动驾驶汽车应通过上下文自适应算法融入社会上熟悉的驾驶模式，以提高在混合人车交通环境中的成功部署和接受几率。