  Perceived risk is crucial in designing trustworthy and acceptable vehicle
automation systems. However, our understanding of its dynamics is limited, and
models for perceived risk dynamics are scarce in the literature. This study
formulates a new computational perceived risk model based on potential
collision avoidance difficulty (PCAD) for drivers of SAE level 2 driving
automation. PCAD uses the 2D safe velocity gap as the potential collision
avoidance difficulty, and takes into account collision severity. The safe
velocity gap is defined as the 2D gap between the current velocity and the safe
velocity region, and represents the amount of braking and steering needed,
considering behavioural uncertainty of neighbouring vehicles and imprecise
control of the subject vehicle. The PCAD predicts perceived risk both in
continuous time and per event. We compare the PCAD model with three
state-of-the-art models and analyse the models both theoretically and
empirically with two unique datasets: Dataset Merging and Dataset Obstacle
Avoidance. The PCAD model generally outperforms the other models in terms of
model error, detection rate, and the ability to accurately capture the
tendencies of human drivers' perceived risk, albeit at a longer computation
time. Additionally, the study shows that the perceived risk is not static and
varies with the surrounding traffic conditions. This research advances our
understanding of perceived risk in automated driving and paves the way for
improved safety and acceptance of driving automation systems.

Human-Computer Interaction