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

• Published in: Transportation research. Part C, Emerging technologies Vol. 166; p. 104751
• Main Authors: He, Xiaolin, Happee, Riender, Wang, Meng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024
• Subjects: Collision avoidance; Computational models; Looming; Perceived risk; Task difficulty; Task difficulty; Perceived risk; Looming; Computational models; Collision avoidance
• ISSN: 0968-090X
• DOI: 10.1016/j.trc.2024.104751

Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of perceived risk dynamics remains limited, and corresponding computational models are scarce. This study formulates a new computational perceived risk model based on potential collision avoidance difficulty (PCAD) for drivers of SAE Level 2 automated vehicles. PCAD quantifies task difficulty using the gap between the current velocity and the safe velocity region in 2D, and accounts for the minimal control effort (braking and/or steering) needed to avoid a potential collision, based on visual looming, behavioural uncertainties of neighbouring vehicles, imprecise control of the subject vehicle, and collision severity. The PCAD model predicts both continuous-time perceived risk and peak perceived risk per event. We analyse model properties both theoretically and empirically with two unique datasets: Datasets Merging and Obstacle Avoidance. The PCAD model generally outperforms three state-of-the-art models in terms of model error, detection rate, and the ability to accurately capture the tendencies of human drivers’ perceived risk, albeit at the cost of longer computation time. Our findings reveal that perceived risk varies with the position, velocity, and acceleration of the subject and neighbouring vehicles, and is influenced by uncertainties in their velocities. •A computational perceived risk model - PCAD was developed for drivers in SAE Level 2.•PCAD quantifies the minimal control effort to avoid a potential collision.•PCAD and three existing models were calibrated, evaluated and compared using two datasets.•PCAD generally outperforms the other three models in five aspects of model performance.•PCAD can adapt to various traffic conditions with the least recalibration efforts.