Investigating the factors underlying perceived speed and risk is crucial to ensure safe driving. However, existing studies on this topic usually measure speed and risk perception indirectly after a driving session, which makes it difficult to trace dynamic effects and time points of potential misestimates. To address this problem, we developed and validated a novel continuous method for dynamically measuring risk and speed perceptions. To study the factors affecting risk and speed perception, we presented participants with videos captured on the same racing track from the same point of view but with different drivers who varied in their speed and risk profiles. During the experiment, participants used a joystick to continuously rate the subjectively perceived risk of driving in the first block and the perceived speed in the second block. Our analysis of these dynamic ratings indicates that risk and speed estimates were decoupled, with curves resulting in decreased speeds but increased risk ratings. However, a close distance to the car in front increased both speed and risk. Based on actual and estimated speed data, we found that overtaking cars on curves resulted in participants overestimating their own speed, whereas an increase in the distance to the car in front on a straight course led to underestimations of their own speed. Our results showcase the usefulness of dynamic rating profiles for in-depth investigations into situations that could result in drivers misjudging speed or risk and will thus help the development of more intelligent, human-centered driving assistance systems.
Copyright: © 2023 Ju, Wallraven. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.