Autonomous cars are indispensable when humans go further down the hands-free route. Although existing literature highlights that the acceptance of the autonomous car will increase if it drives in a human-like manner, sparse research offers the naturalistic experience from a passenger's seat perspective to examine the human likeness of current autonomous cars. The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars (driven by either human or AI drivers) as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. In contrast, when the human driver drove the car, the passengers' judgement was around chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline emotions and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition. Our study suggested an important role of affective transition in passengers' ascription of humanness, which might become a future direction for autonomous driving.
翻译:当人类进一步进入无手通道时,自主汽车是不可或缺的。虽然现有文献强调,如果自主汽车以人样的方式驱动,对自主汽车的接受度将会增加,但研究稀少,从乘客座位的角度提供了自然经验,以检查当前自主汽车的人类相似性。本研究报告根据69名参与者在现实道路情况下的反馈,检验了AI驾驶员能否为乘客创造人样的驾驶经验。我们设计了一个非口头驾驶自动驾驶测试的驾驶经验版本。参与者乘坐自主汽车(由人或AI驾驶员驱动)作为乘客,并判断驾驶员是人还是AI。AI驾驶员未能通过我们的考试,因为乘客检测到AI驾驶员高于机会。相比之下,当人类驾驶员驾驶汽车时,乘客的判断是偶然的。我们进一步调查了人类乘客如何将人性纳入我们的测试。根据Lewin的实地理论,我们推进了一个计算模型,将信号检测理论与预先培训的语言模型相结合,以预测乘客的人格评级行为。我们采用了影响驾驶员前基本情感和相应的后期情感转变模式之间的过渡方法,从而显示我们未来的人类感官能更大程度。