Passenger well-being in highly automated vehicles
- Wohlbefinden von Insassen hochautomatisierter Fahrzeuge
Sauer, Vanessa Christine; Nitsch, Verena (Thesis advisor); Klarmann, Martin (Thesis advisor)
Dissertation / PhD Thesis
Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020
Technical advances in vehicle automation make highly automated driving a realistic possibility within the next decade. With higher levels of vehicle automation, the driver will be able to transfer the driving task and supervision to the vehicle for extended periods and can engage in non-driving related tasks. The different driver/passenger behavior will change user requirements for automated vehicles and may increase the importance of a holistic view of the passengers’ experience in an automated vehicle. One way to view the automated driving experience more holistically from a passenger perspective is to focus on passenger well-being. This work investigates the concept of passenger well-being in automated driving and explores reliable and valid subjective and objective measures to gauge passenger well-being in study settings and during real-world automated driving experiences. Based on these findings and prior findings in transportation research, a model of determinants of passenger well-being is developed. Empirical user studies are utilized to investigate the role of each element. The resulting model considers physical and technical features that affect passenger well-being through a dual processing mechanism. The features may have a direct effect (peripheral pro-cessing) or a mediated effect (central processing) on passenger well-being. Nine passenger needs categorized as utilitarian or hedonic needs act as mediators depending on the features. The impact of features on passenger well-being is further moderated by non-driving related tasks engaged in during the drive and by passenger characteristics. The resulting model is partially validated with two empirical studies. The model of determinants of passenger well-being in automated driving provides a framework for a user-centered development of automated vehicles. Further, this work yields implications and recommendations for the design of such vehicles.