Multitasking While Driving: How Drivers Self-Regulate Their Interaction with In-Vehicle Touchscreens in Automated Driving

Abstract

Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how driving automation affects how drivers interact with the center stack touchscreen, i.e., how drivers self-regulate their behavior in response to different levels of driving automation. To investigate this, we apply multilevel models to a real-world driving dataset consisting of 31,378 sequences. Our results show significant differences in drivers' interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. Specifically, at higher levels of driving automation (level 2), the mean glance duration toward the center stack touchscreen increases by 36 % and the mean number of interactions per sequence increases by 17 % compared to manual driving. Furthermore, partially automated driving has a strong impact on the use of more complex UI elements (e.g., maps) and touch gestures (e.g., multitouch). We also show that the effect of driving automation on drivers' self-regulation is greater than that of vehicle speed and road curvature. The derived knowledge can inform the design and evaluation of touch-based infotainment systems and the development of context-aware driver monitoring systems.

No Driver Is Alone: A Multicentered Human-Machine System Framework for Multitasking While Driving

“…Today, the list of NDT has expanded to include activities like reading and replying to text messages, watching short videos, and playing games. Moreover, they all require more visual attention and higher precision in operation [28,29].…”

ICEBOAT: An Interactive User Behavior Analysis Tool for Automotive User Interfaces

“…Regarding the assessment of distraction, the visual demand of interfaces has proven to be an effective measure, as long glances away from the road (t>2 s) are directly correlated with increased crash risk [29]. As a result, the evaluation of IVIS in terms of usability and driver distraction is a well-researched topic [19,22,23,25,26]. However, there is a considerable gap between the academic research conducted to evaluate IVISs and the tools and methods available to the professionals in industry who eventually design these systems.…”

“…INF-5: Contextual Information (Count = 5, Support = 6). Driver behavior and driver interactions are highly context sensitive [19] and participants state that they need contextual information to better judge individual interaction sequences. For example, they state that they need information about the driving situation (1,3) to be able to judge how drivers interact in different situations.…”