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Decision Tree Method to Analyze the Performance of Lane Support Systems

Author

Listed:
  • Giuseppina Pappalardo

    (Department of Civil Engineering and Architecture, University of Catania, 95123 Catania, Italy)

  • Salvatore Cafiso

    (Department of Civil Engineering and Architecture, University of Catania, 95123 Catania, Italy)

  • Alessandro Di Graziano

    (Department of Civil Engineering and Architecture, University of Catania, 95123 Catania, Italy)

  • Alessandro Severino

    (Department of Civil Engineering and Architecture, University of Catania, 95123 Catania, Italy)

Abstract

Road departure is one of the main causes of single vehicle and frontal crashes. By implementing lateral support systems, a significant amount of these accidents can be avoided. Typical accidents are normally occurring due to unintentional lane departure where the driver drifts towards and across the line identifying the edge of the lane. The Lane Support Systems (LSS) uses cameras to “read” the lines on the road and alert the driver if the car is approaching the lines. Anyway, despite the assumed technology readiness, there is still much uncertainty regarding the needs of vision systems for “reading” the road and limited results are still available from in field testing. In such framework the paper presents an experimental test of LSS performance carried out in two lane rural roads with different geometric alignments and road marking conditions. LSS faults, in day light and dry pavement conditions, were detected on average in 2% of the road sections. A decision tree method was used to analyze the cause of the faults and the importance of the variable involved in the process. The fault probability increased in road sections with radius less than 200 m and in poor conditions of road marking.

Suggested Citation

  • Giuseppina Pappalardo & Salvatore Cafiso & Alessandro Di Graziano & Alessandro Severino, 2021. "Decision Tree Method to Analyze the Performance of Lane Support Systems," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:846-:d:481376
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    References listed on IDEAS

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    1. Francesca Pagliara & Filomena Mauriello & Lucia Russo, 2020. "A Regression Tree Approach for Investigating the Impact of High Speed Rail on Tourists’ Choices," Sustainability, MDPI, vol. 12(3), pages 1-15, January.
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    2. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.
    3. Yunshun Zhang & Qishuai Xie & Minglei Gao & Yuchen Guo, 2023. "The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    4. Federico Orsini & Mariaelena Tagliabue & Giulia De Cet & Massimiliano Gastaldi & Riccardo Rossi, 2021. "Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    5. Sehyun Tak & Sari Kim & Hwapyeong Yu & Donghoun Lee, 2022. "Analysis of Relationship between Road Geometry and Automated Driving Safety for Automated Vehicle-Based Mobility Service," Sustainability, MDPI, vol. 14(4), pages 1-13, February.
    6. Philipp Luz & Li Zhang & Jinyue Wang & Volker Schwieger, 2021. "Lane-Level Map-Aiding Approach Based on Non-Lane-Level Digital Map Data in Road Transport Security," Sustainability, MDPI, vol. 13(17), pages 1-18, August.
    7. Darko Babić & Dario Babić & Mario Fiolić & Arno Eichberger & Zoltan Ferenc Magosi, 2021. "A Comparison of Lane Marking Detection Quality and View Range between Daytime and Night-Time Conditions by Machine Vision," Energies, MDPI, vol. 14(15), pages 1-15, August.
    8. Ghulam E Mustafa Abro & Saiful Azrin B. M. Zulkifli & Kundan Kumar & Najib El Ouanjli & Vijanth Sagayan Asirvadam & Mahmoud A. Mossa, 2023. "Comprehensive Review of Recent Advancements in Battery Technology, Propulsion, Power Interfaces, and Vehicle Network Systems for Intelligent Autonomous and Connected Electric Vehicles," Energies, MDPI, vol. 16(6), pages 1-31, March.

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