Understanding and Improving User Comfort in Automated Driving

Comfort is an important factor that affects user acceptance and the subsequent uptake of automated vehicles (AVs). In highly and fully automated driving, the transition of control from drivers to the automation system transforms the role of onboard users from active drivers to passive riders. This transition removes the need to control the vehicle and monitor the environment, which allows users to engage in non-driving-related activities. This, in turn, makes it difficult for users to predict the vehicle’s manoeuvres, which potentially challenges user comfort. Evidence suggests that designing AVs’ driving styles in certain ways, such as mimicking users’ manual driving styles, may affect user comfort. However, our knowledge about the influences of AVs’ driving styles on user comfort is limited. There also remains a significant gap in understanding the complexities of the concept of user comfort in automated driving. Addressing these research gaps is crucial for a comprehensive understanding of user comfort in automated driving and improving cross-study comparability.
This thesis aims to investigate user comfort in highly automated driving, and how different driving styles of AVs affect comfort. The research examined a) users’ subjective evaluations of different driving styles, b) the relationship between objective vehicle metrics and subjective evaluations, and c) a conceptual model explaining how driving styles affect user comfort, involving related concepts and factors.
This thesis adopted a mixed-method approach. Based on a driving simulator experiment, quantitative methods were used to understand users’ subjective preferences for human-like versus non-human-like driving styles and the effect of vehicle metrics on such subjective evaluations. Based on a focus group workshop with experts, qualitative methods were used to establish a conceptual model of user comfort.
The quantitative exploration showed that two representative human-like driving styles (defensive and aggressive) were perceived as more comfortable and natural than the non-human-like, robotic, driving style. Particularly, the defensive one was rated as the most comfortable, by both low and high sensation seekers, especially for more challenging roads. Results further showed that several lateral and rotational kinematics of the vehicle were significantly associated with both comfort and naturalness evaluations, while only one longitudinal factor was associated with comfort. Results also suggested that enhancing the human-likeness of automated driving by aligning it with users’ manual driving, in terms of several vehicle metrics like speed, could improve user comfort and naturalness. However, it also noted that such human-like patterns in lateral jerk might adversely affect evaluations.
The qualitative study found a range of aspects related to comfort in automated driving, such as physical comfort, design expectations, and pleasantness. Several aspects of discomfort were also identified, which differ from those associated with comfort. The study further led to the development of a conceptual framework. The framework explains how AVs’ driving styles, as well as other non-driving-related factors, affect user comfort in automated driving. It incorporates a range of concepts, such as trust, naturalness, expectations, and privacy concerns.
This thesis contributes to a better understanding of user comfort in automated driving, empirically and theoretically. It clarifies the effect of driving styles on user comfort from both subjective and objective perspectives. Moreover, it reveals the multifaceted nature of the concept of user comfort in automated driving. The implications drawn from this work provide design guidelines to assist in the development of more comfortable, pleasant, and acceptable automated vehicles for users.