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 humanness 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走向完全解放双手的必经之路。尽管现有文献强调，若自动驾驶汽车能以类人方式行驶，其接受度将提升，但鲜有研究从乘客视角提供自然主义体验来检验当前自动驾驶汽车的拟人程度。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驾驶员能否为乘客创造类人的乘坐体验。我们设计了一版基于乘坐体验的非语言图灵测试用于自动驾驶。参与者以乘客身份乘坐自动驾驶汽车（由人类或AI驾驶员驾驶），并判断驾驶员是人类还是AI。AI驾驶员未能通过测试，因为乘客对AI驾驶员的判断显著高于随机水平。相比之下，当人类驾驶员驾驶汽车时，乘客的判断接近随机水平。我们进一步探究了人类乘客在测试中如何赋予拟人化属性。基于勒温场理论，我们提出了一种将信号检测理论与预训练语言模型相结合的计算模型，用以预测乘客的拟人化评级行为。我们将研究前基线情绪与对应阶段后情绪之间的情感转移作为模型的信号强度。结果表明，乘客赋予拟人化属性的程度会随情感转移的增强而提高。我们的研究表明情感转移在乘客拟人化归因中具有重要作用，这可能成为自动驾驶未来的研究方向。