While the interest of the transport research community and automotive industry is increasingly turning towards developments and improvements in the field of autonomous vehicles, there is a need for a better understanding of the end users’ preferences regarding perceived passenger comfort, in order to improve acceptance and intention to use. The present study is based on a driving simulator experiment conducted at the University of Leeds Driving Simulator and approaches the issue of comfort via observed speed choice behaviour. Participants drove a series of driving simulator scenarios composed of road segments of different road type, road geometry, risk level at the road edge, and oncoming traffic. They also completed a series of self-report questionnaires, including Arnett’s Inventory of Sensation-seeking. A set of models was developed in order to investigate the effects of road environment and sensation-seeking on speed behaviour. The initial model only considered explanatory variables related to the road environment and accounted for individual unobserved heterogeneity. Past behaviour, serial correlation and heterogeneity in road environment were then introduced in the model specification. The autoregressive disturbance term that accounted for serial correlation was also applied in the form of a random variable and significantly improved model fit. Finally, sensation-seeking was incorporated in the model as a latent variable. The results showed a significant impact of most of the road elements as road type, curvature, risk type at the road edge on observed behaviour, implying a future need for the development of autonomous vehicle controllers that adapt their performance based on the road environment. Moreover, sensation-seeking had a significant and positive effect on speed, which indicates a potential future demand for personalised controllers to meet the users’ individual preferences.

尽管交通运输研究界和汽车行业的兴趣日益转向自动驾驶汽车领域的发展和改进，但仍需要更好地了解最终用户在感知到的乘客舒适度方面的偏好，以提高接受度和使用意图。本研究基于在利兹大学驾驶模拟器上进行的驾驶模拟器实验，并通过观察速度选择行为来解决舒适性问题。参与者驾驶了一系列驾驶模拟器场景，这些场景由不同道路类型，道路几何形状，道路边缘的风险级别以及来临的交通组成。他们还完成了一系列自我报告调查表，包括Arnett的“寻求感觉的清单”。为了研究道路环境和寻求感觉对速度行为的影响，开发了一组模型。初始模型仅考虑与道路环境有关的解释变量，并说明了各个未观察到的异质性。然后在模型规范中介绍了道路环境中的过去行为，序列相关性和异质性。考虑序列相关性的自回归干扰项也以随机变量的形式应用，并显着改善了模型拟合。最后，将感觉寻求作为潜在变量纳入模型中。结果表明，大多数道路要素（如道路类型，曲率，路边的风险类型）对观察到的行为具有重大影响，这意味着未来需要开发自动驾驶控制器，以根据道路环境调整其性能。此外，寻求感觉对速度有明显的积极影响，这表明将来可能需要个性化控制器来满足用户的个人喜好。