Effects of an Unexpected and Expected Event on Older Adults' Autonomic Arousal and Eye Fixations During Autonomous Driving

Post-Takeover Proficiency in Conditionally Automated Driving: Understanding Stabilization Time with Driving and Physiological Signals

In the realm of conditionally automated driving, understanding the crucial transition phase after a takeover is paramount. This study delves into the concept of post-takeover stabilization by analyzing data recorded in two driving simulator experiments. By analyzing both driving and physiological signals, we investigate the time required for the driver to regain full control and adapt to the dynamic driving task following automation. Our findings show that the stabilization time varies between measured parameters. While the drivers achieved driving-related stabilization (winding, speed) in eight to ten seconds, physiological parameters (heart rate, phasic skin conductance) exhibited a prolonged response. By elucidating the temporal and cognitive dynamics underlying the stabilization process, our results pave the way for the development of more effective and user-friendly automated driving systems, ultimately enhancing safety and driving experience on the roads.

Using voice recognition to measure trust during interactions with automated vehicles

Trust in an automated vehicle system (AVs) can impact the experience and safety of drivers and passengers. This work investigates the effects of speech to measure drivers' trust in the AVs. Seventy-five participants were randomly assigned to high-trust (the AVs with 100% correctness, 0 crash, and 4 system messages with visual-auditory TORs) and low-trust group (the AVs with a correctness of 60%, a crash rate of 40%, 2 system messages with visual-only TORs). Voice interaction tasks were used to collect speech information during the driving process. The results revealed that our settings successfully induced trust and distrust states. The corresponding extracted speech feature data of the two trust groups were used for back-propagation neural network training and evaluated for its ability to accurately predict the trust classification. The highest classification accuracy of trust was 90.80%. This study proposes a method for accurately measuring trust in automated vehicles using voice recognition.

A user study of directional tactile and auditory user interfaces for take-over requests in conditionally automated vehicles

General introduction of unconditionally and conditionally automated vehicles is expected to have a highly positive impact on the society, from increased accessibility to mobility and road traffic safety, to decreased environmental and economic negative impacts. However, there are several obstacles and risks slowing down the adoption of this technology, which are primarily related to the human-machine interaction (HMI) and exchange of control between the vehicle and the human driver. In this article, we present key takeaways for HMI design of take-over requests (TOR) that the vehicle issues to inform the driver to take over control of the vehicle. The key takeaways were developed based on the results of a user study, where directional tactile-ambient (visual) and auditory-ambient (visual) TOR user interfaces (UI) were evaluated with regards to commonly used take-over quality aspects (attention redirection, take-over time, correct interpretation of stimuli, off-road drive, brake application, lateral acceleration, minimal time-to-collision and occurrence of collision). 36 participants took part in the mixed design study, which was conducted in a driving simulator. The results showed that drivers' attention was statistically significantly faster redirected with the auditory-ambient UI, however using the tactile-ambient UI resulted in less off-road driving and slightly less collisions. The results also revealed that drivers correctly interpreted the directional TOR stimuli more often than the non-directional one. Based on the study results, a list of key takeaways was developed and is presented in the conclusion of the paper. The results from this study are especially relevant to the TOR UI designers and the automotive industry, which tend to provide the most usable UI for ensuring safer end efficient human-vehicle interaction during the TOR task.