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Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

Author

Listed:
  • Hoseon Kim

    (Department of Smart City Engineering, Hanyang University Erica Campus, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea)

  • Jieun Ko

    (Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea)

  • Cheol Oh

    (Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea)

  • Seoungbum Kim

    (Department of Urban Engineering/Engineering Research Institute, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of Korea)

Abstract

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

Suggested Citation

  • Hoseon Kim & Jieun Ko & Cheol Oh & Seoungbum Kim, 2024. "Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic," Sustainability, MDPI, vol. 16(22), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9672-:d:1515311
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    References listed on IDEAS

    as
    1. Guangyang Hou, 2023. "Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    2. Tengilimoglu, Oguz & Carsten, Oliver & Wadud, Zia, 2023. "Implications of automated vehicles for physical road environment: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    3. Pan, Yuchen & Wu, Yu & Xu, Lu & Xia, Chengyi & Olson, David L., 2024. "The impacts of connected autonomous vehicles on mixed traffic flow: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
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