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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

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.

Details

OriginalspracheEnglisch
Aufsatznummer104751
Seitenumfang31
FachzeitschriftTransportation Research Part C: Emerging Technologies
Jahrgang166 (2024)
PublikationsstatusVeröffentlicht - 30 Juli 2024
Peer-Review-StatusJa

Externe IDs

ORCID /0000-0001-6555-5558/work/171064787

Schlagworte

Schlagwörter

  • Collision avoidance, Computational models, Looming, Perceived risk, Task difficulty