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Enhanced Safety in Multi-Lane Automated Driving Through Semantic Features

Author

Listed:
  • Zhou Li

    (Hunan Biological and Electromechanical Polytechnic, China)

  • Jiajia Li

    (Hunan Biological and Electromechanical Polytechnic, China)

  • Gengming Xie

    (State Grid Hunan Electric Power Company Limited, China)

  • Varsha Arya

    (Lebanese American University, Lebanon)

  • Hao Li

    (Hunan Biological and Electromechanical Polytechnic, China)

Abstract

Accurate lane detection is crucial for the safety and reliability of multi-lane automated driving, where the complexity of traffic scenarios is significantly heightened. Leveraging the semantic segmentation capabilities of deep learning, we develop a modified U-Net architecture tailored for the precise identification of lane lines. Our model is trained and validated on a robust dataset from Kaggle, comprising 2975 annotated training images and 500 test images with masks. Empirical results demonstrate the model's proficiency, achieving a peak accuracy of 95.19% and a Dice score of 0.928, indicating exceptional precision in segmenting lanes. These results represent a notable contribution to the enhancement of safety in automated driving systems.

Suggested Citation

  • Zhou Li & Jiajia Li & Gengming Xie & Varsha Arya & Hao Li, 2024. "Enhanced Safety in Multi-Lane Automated Driving Through Semantic Features," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-13, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-13
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