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Lane segmentation and center line prediction – a multitask framework for autonomous driving systems

Author

Listed:
  • Bharath Haridas Aithal
  • Madhumita Dey
  • Buridi Chaithanya Lakshmi

Abstract

Accurate lane information is critical for ensuring autonomous vehicles understand the structure and limits of the urban environment, thereby facilitating safe navigation and driving environment. These tasks especially pose significant challenges where lane markings are faded or worn out, particularly in developing countries devoid of appropriate lane markings. The present research work addresses these aspects by integrating offset and lane-changing decision-making for Autonomous Driving Systems (ADS). The research presents the first-of-its-kind lane segmentation dataset with segmentation labels, depicting complex Indian urban scenarios and poor road conditions. The proposed multitask framework investigated on the test data for the Indian Road dataset attains an IoU score of 0.95, proving its efficiency and robustness for complex urban environments.

Suggested Citation

  • Bharath Haridas Aithal & Madhumita Dey & Buridi Chaithanya Lakshmi, 2026. "Lane segmentation and center line prediction – a multitask framework for autonomous driving systems," Transportation Planning and Technology, Taylor & Francis Journals, vol. 49(6), pages 1423-1444, August.
    • Handle: RePEc:taf:transp:v:49:y:2026:i:6:p:1423-1444
    DOI: 10.1080/03081060.2025.2502413
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