Understanding older users’ comfort needs can inform the inclusive design of automated vehicles (AVs). Real-world experience with automated driving is crucial to elicit meaningful insights for older adults, who are expected to benefit from AVs in terms of enhanced mobility and autonomy. In this study, semi-structured interviews were conducted with 27 participants (aged over 60) who experienced a so-called automated ride, operated by a Wizard-of-Oz driver, in Delft, Netherlands. Following the ride, participants were interviewed about their comfort during the ride. Using thematic analysis, we identified three overarching factors associated with user comfort: (1) vehicle factors (including driving styles, AV capabilities, effect of AV exposure , and physical aspects ), (2) environment factors (including effect of external driving environment ), and (3) human factors (including affective experience, attitudes to AV/technology, engagement in non-driving related tasks [NDRTs] , and communication with the AV ). Our findings contribute to understanding comfort in automated driving, by offering a comprehensive list of factors associated with comfort, identifying affective reflections of psychological comfort, and discovering the co-existence of psychological comfort and physical discomfort. The study provides implications for designing comfortable AVs, such as the need for smooth, cautious, and anticipatory driving styles, and flexible and early reactions to unexpected events.

The rise of automated vehicles (AVs) has provoked discussions that extend far beyond their technological sophistication, addressing human and social psychology and social dynamics.While AVs promise...

Conditionally automated driving enables drivers to engage in non-driving-related activities, with the responsibility to take over vehicle control upon request. This takeover process increases the risk of collisions, especially when drivers fail to safely complete takeovers within limited time budgets (i.e., the time offered by automation for takeovers). This phenomenon underlines the significance of providing time budgets that sufficiently accommodate drivers’ takeover time (i.e., the time required by drivers to resume conscious control of vehicles). Considering that drivers’ takeover time varies significantly across scenarios, this study centres on understanding the role of driver perception in takeover time using the Task-Capability Interface (TCI) theory. The TCI theory suggests that drivers adjust their behaviours based on their perceived task demands and driver capabilities. Accordingly, in a driving simulator experiment featuring diverse traffic densities and distractions, we investigated drivers’ takeover time while capturing their perceived task demands and capabilities through a takeover-oriented questionnaire based on established instruments. The results show that drivers generally have longer takeover time as their perceived task demand rises, perceived driver capability diminishes, and perceived spare capacity (perceived driver capability minus perceived task demand) decreases. These patterns fluctuate under conditions of low perceived task demand or high perceived driver capability. When both conditions coincide, drivers necessitate a considerably longer time to regain vehicle control. Our findings on takeover time contribute to the development of strategies aimed at predicting drivers’ takeover time, optimizing time budgets, fostering human-centred vehicle design, and enhancing the safety of conditionally automated driving.

The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how meaningful human control relates to subjective experiences of actual users of these systems. To address this gap, our study aimed to investigate the alignment between the degree of meaningful human control and drivers’ perceptions of safety and trust in a real-world partially automated driving system. We utilized previously collected data from interviews with Tesla "Full Self-Driving" (FSD) Beta users, investigating the alignment between the user perception and how well the system was tracking the users’ reasons. We found that tracking of users’ reasons for driving tasks (such as safe maneuvers) correlated with perceived safety and trust, albeit with notable exceptions. Surprisingly, failure to track lane changing and braking reasons was not necessarily associated with negative perceptions of safety. However, the failure of the system to track expected maneuvers in dangerous situations always resulted in low trust and perceived lack of safety. Overall, our analyses highlight alignment points but also possible discrepancies between perceived safety and trust on the one hand, and meaningful human control on the other hand. Our results can help the developers of automated driving technology to design systems under meaningful human control and are perceived as safe and trustworthy.