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Review on Lane Detection and Tracking Algorithms of Advanced Driver Assistance System

Author

Listed:
  • Swapnil Waykole

    (School of Engineering, RMIT University, Melbourne, VIC 3000, Australia)

  • Nirajan Shiwakoti

    (School of Engineering, RMIT University, Melbourne, VIC 3000, Australia)

  • Peter Stasinopoulos

    (School of Engineering, RMIT University, Melbourne, VIC 3000, Australia)

Abstract

Autonomous vehicles and advanced driver assistance systems are predicted to provide higher safety and reduce fuel and energy consumption and road traffic emissions. Lane detection and tracking are the advanced key features of the advanced driver assistance system. Lane detection is the process of detecting white lines on the roads. Lane tracking is the process of assisting the vehicle to remain in the desired path, and it controls the motion model by using previously detected lane markers. There are limited studies in the literature that provide state-of-art findings in this area. This study reviews previous studies on lane detection and tracking algorithms by performing a comparative qualitative analysis of algorithms to identify gaps in knowledge. It also summarizes some of the key data sets used for testing algorithms and metrics used to evaluate the algorithms. It is found that complex road geometries such as clothoid roads are less investigated, with many studies focused on straight roads. The complexity of lane detection and tracking is compounded by the challenging weather conditions, vision (camera) quality, unclear line-markings and unpaved roads. Further, occlusion due to overtaking vehicles, high-speed and high illumination effects also pose a challenge. The majority of the studies have used custom based data sets for model testing. As this field continues to grow, especially with the development of fully autonomous vehicles in the near future, it is expected that in future, more reliable and robust lane detection and tracking algorithms will be developed and tested with real-time data sets.

Suggested Citation

  • Swapnil Waykole & Nirajan Shiwakoti & Peter Stasinopoulos, 2021. "Review on Lane Detection and Tracking Algorithms of Advanced Driver Assistance System," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11417-:d:657497
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    References listed on IDEAS

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    1. Nicholas Fiorentini & Massimo Losa, 2020. "Long-Term-Based Road Blackspot Screening Procedures by Machine Learning Algorithms," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
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    Cited by:

    1. Sudha Anbalagan & Ponnada Srividya & B. Thilaksurya & Sai Ganesh Senthivel & G. Suganeshwari & Gunasekaran Raja, 2023. "Vision-Based Ingenious Lane Departure Warning System for Autonomous Vehicles," Sustainability, MDPI, vol. 15(4), pages 1-11, February.
    2. Swapnil Waykole & Nirajan Shiwakoti & Peter Stasinopoulos, 2022. "Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle," Sustainability, MDPI, vol. 14(19), pages 1-20, September.

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