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Improved Perception of Motorcycles by Simulator-Based Driving Education

Author

Listed:
  • Arno Eichberger

    (Institute of Automotive Engineering, Graz University of Technology, Inffeldgasse 11/2, 8010 Graz, Austria)

  • Marianne Kraut

    (Reco-Tech GmbH, 9143 Feistritz ob Bleiburg, Austria)

  • Ioana V. Koglbauer

    (Institute of Automotive Engineering, Graz University of Technology, Inffeldgasse 11/2, 8010 Graz, Austria)

Abstract

Research shows that about half of all motorcycle collisions with other vehicles were caused by the accident opponent, typically a passenger car. This study aimed to assess the effect of simulator training on improving car drivers’ perceptibility of motorcycles and thereby addressing this frequent type of motorcycle accident from the perspective of the initiator. For this purpose, a training program with different methods was conducted and tested in a driving simulator with 80 learner drivers aged between 15 and 27 years, assigned to a control group and three training groups: variable priority, equal priority, and equal priority with warning. The conflict scenarios were determined based on an analysis of motorcycle–car accidents. The variable priority training program resulted in better perceptibility of motorcycles as compared to the equal priority training program and equal priority with warning in two out of four test setups, i.e., urban roads with high contrast between motorcycle and the driving environment and on rural roads with a low contrast. Most participants rated each training method in the driving simulator as useful and would recommend it to other learner drivers. These results are important because they show that simulator training has a positive effect on the motorcycle detection performance of learner drivers. The early perception of motorcycles in car drivers is essential for preventing collisions between cars and motorcycles.

Suggested Citation

  • Arno Eichberger & Marianne Kraut & Ioana V. Koglbauer, 2022. "Improved Perception of Motorcycles by Simulator-Based Driving Education," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5283-:d:803620
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    References listed on IDEAS

    as
    1. Sadegh Arefnezhad & Arno Eichberger & Matthias Frühwirth & Clemens Kaufmann & Maximilian Moser & Ioana Victoria Koglbauer, 2022. "Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals," Energies, MDPI, vol. 15(2), pages 1-25, January.
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