The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21952685

de Winter, Joost; Hoogmoed, Jim; Stapel, J.C.J. (Jork); Dodou, Dimitra; Bazilinskyy, Pavlo (2023): Supplementary data for the paper 'Predicting perceived risk of traffic scenes using computer vision'. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21952685.v1

Dataset

• Cognitive Sciences
• Automotive Engineering

Automated driving, Computer vision, Risk perception, Traffic psychology

CC BY-NC-SA 3.0

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by Joost de Winter , Jim Hoogmoed , J.C.J. (Jork) Stapel , Dimitra Dodou , Pavlo Bazilinskyy

  

Perceived risk, or subjective risk, is an important concept in the field of traffic psychology and automated driving. In this paper, we investigate whether perceived risk in images of traffic scenes can be predicted from computer vision features that may also be used by automated vehicles (AVs). We conducted an international crowdsourcing study with 1378 participants, who rated the perceived risk of 100 randomly selected dashcam images on German roads. The population-level perceived risk was found to be statistically reliable, with a split-half reliability of 0.98. We used linear regression analysis to predict (r = 0.62) perceived risk from two features obtained with the YOLOv4 computer vision algorithm: the number of people in the scene and the mean size of the bounding boxes surrounding other road users. When the ego-vehicle’s speed was added as a predictor variable, the prediction strength increased to r = 0.75. Interestingly, the sign of the speed prediction was negative, indicating that a higher vehicle speed was associated with a lower perceived risk. This finding aligns with the principle of self-explaining roads. Our results suggest that computer-vision features and vehicle speed contribute to an accurate prediction of population subjective risk, outperforming the ratings provided by individual participants (mean r = 0.41). These findings may have implications for AV development and the modeling of psychological constructs in traffic psychology.

• 2023-01-27 first online, published, posted

4TU.ResearchData

*.m, *.mat, *.xlsx, *.pdf, *.png, *.txt

Predicting perceived risk of traffic scenes using computer vision

Faculty of of Mechanical, Maritime and Materials Engineering, Delft University of Technology

Department of Industrial Design, Eindhoven University of Technology