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：自动驾驶汽车是人类迈向完全解放双手驾驶道路上的关键环节。尽管现有研究强调，若自动驾驶汽车能以类人方式行驶，其接受度将得到提升，但鲜有研究从乘客视角出发，在自然场景下检验当前自动驾驶汽车的类人程度。本研究基于69名参与者在真实道路场景中的反馈，测试了AI驾驶员能否为乘客提供类人乘坐体验。我们针对自动驾驶设计了一种基于乘坐体验的非语言图灵测试版本。参与者以乘客身份乘坐由人类或AI驾驶员操控的汽车，并判断驾驶员是真人还是AI。结果表明，AI驾驶员未能通过测试，因为乘客能以高于随机水平的准确率识别出AI驾驶员；而人类驾驶员操控汽车时，乘客的判断正确率接近随机水平。我们进一步探究了人类乘客如何在该测试中赋予类人属性。基于Lewin场论，我们提出了一种结合信号检测理论与预训练语言模型的计算模型，用于预测乘客的类人评分行为。我们采用从研究前基线情绪到相应阶段后情绪的情感转换作为模型的信号强度。结果显示，情感转换幅度越大，乘客对类人属性的归因程度越高。本研究揭示了情感转换在乘客类人属性归因中的重要作用，这或将成为自动驾驶领域的未来研究方向。