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Perceived risk is crucial in designing trustworthy and acceptable vehicleautomation systems. However, our understanding of its dynamics is limited, andmodels for perceived risk dynamics are scarce in the literature. This studyformulates a new computational perceived risk model based on potentialcollision avoidance difficulty (PCAD) for drivers of SAE level 2 drivingautomation. PCAD uses the 2D safe velocity gap as the potential collisionavoidance difficulty, and takes into account collision severity. The safevelocity gap is defined as the 2D gap between the current velocity and the safevelocity region, and represents the amount of braking and steering needed,considering behavioural uncertainty of neighbouring vehicles and imprecisecontrol of the subject vehicle. The PCAD predicts perceived risk both incontinuous time and per event. We compare the PCAD model with threestate-of-the-art models and analyse the models both theoretically andempirically with two unique datasets: Dataset Merging and Dataset ObstacleAvoidance. The PCAD model generally outperforms the other models in terms ofmodel error, detection rate, and the ability to accurately capture thetendencies of human drivers' perceived risk, albeit at a longer computationtime. Additionally, the study shows that the perceived risk is not static andvaries with the surrounding traffic conditions. This research advances ourunderstanding of perceived risk in automated driving and paves the way forimproved safety and acceptance of driving automation systems.

A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)