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A Review on Handicap Sections and Situations to Improve Driving Safety of Automated Vehicles

Author

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  • Chang-Gyun Roh

    (Smart Mobility Research Center, Department of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi 10223, Korea)

  • I-Jeong Im

    (Smart Mobility Research Center, Department of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi 10223, Korea)

Abstract

An automated vehicle performs self-driving by utilizing information gathered through sensors attached to the vehicle. Sensor accuracy is thus mentioned as the major technology for enhancing driving safety. However, since urban centers are replete with sections and situations that handicap driving such as sensor recognition limitations and failures, it is necessary to conduct a study that prepares for driving handicaps. As such, this study aims to derive the sections and situations where driving safety is depreciated and review those problems via an analytic hierarchy process (AHP) analysis on driving handicap factors. The analysis result showed that the importance of these handicap situations is high, and it was confirmed that it is necessary to first review off-road sections, environmental factors (heavy rain or snow), merge sections, and sections with poor lane conditions. The result of this research has significance in reviewing road sections and potential situations that require primary verification for securing the driving safety of automated vehicles. It is expected to be utilized in the relevant studies as a basic study.

Suggested Citation

  • Chang-Gyun Roh & I-Jeong Im, 2020. "A Review on Handicap Sections and Situations to Improve Driving Safety of Automated Vehicles," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5509-:d:381834
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    References listed on IDEAS

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    1. Vinayak V Dixit & Sai Chand & Divya J Nair, 2016. "Autonomous Vehicles: Disengagements, Accidents and Reaction Times," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-14, December.
    2. David Sirkin & Sonia Baltodano & Brian Mok & Dirk Rothenbücher & Nikhil Gowda & Jamy Li & Nikolas Martelaro & David Miller & Srinath Sibi & Wendy Ju, 2016. "Embodied Design Improvisation for Autonomous Vehicles," Understanding Innovation, in: Hasso Plattner & Christoph Meinel & Larry Leifer (ed.), Design Thinking Research, pages 125-143, Springer.
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    Cited by:

    1. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    2. Huiyuan Xiong & Huan Liu & Jian Ma & Yuelong Pan & Ronghui Zhang, 2021. "An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios," Sustainability, MDPI, vol. 13(8), pages 1-16, April.

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