Analysis of Driver Workload on Risk Perception in Non-Safety-Critical Situations with Partial Automation

The acceptance of automated driving depends largely on the user's perception of risk and trust in the automated vehicle. One of the challenges in the design of user interfaces and interaction with the human driver is therefore to promote trust and reduce risk perception in non-safety-critical situations. The aim of this contribution is to investigate the effect of driver workload on perceived risk of drivers during partially automated driving and to attenuate the perceived risk associated with non-safety-critical situations by adapting driver-related factors. To address this objective, a section of the manD 1.0 (human driver monitoring) dataset is utilized, which includes the behavior of 39 drivers in car-following scenarios, where the perceived risk and trust in the automated system are affected by assigning a secondary auditory task to the participants. The results of the analysis show that driver interventions in automated driving, such as braking, steering, or taking control, as indicators of perceived risk, occur more frequently when drivers are not multitasking, even when the driving situation is not critical. The consideration of age, gender, and driving experience shows that driver characteristics have no significant influence on the takeover decision in this context. The proposed interaction can be integrated into a feedback loop to enhance trust when falsely high perceived risk is detected. Such an approach can be applied to a range of automation levels, including partially or entirely passive drivers, as well as to passengers in automated shuttles where the absence of a human driver may lead to increased perceived risk. Further studies are required to learn more about the qualitative and quantitative relationships between the different mental states of drivers and their improvement methods.