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August 22, 2025INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTOpen Access

Deep Learning Based Lane Detection for Auto Driving Vehicles

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Authors

HSHarisankar SadasivanKSK T Siddesh

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Overview

This approach improves lane detection accuracy in autonomous driving systems, suggesting greater safety and reliability.

Key Points

  • This deep learning model improves lane detection accuracy significantly, ensuring safer navigation for autonomous vehicles in various conditions.
  • The model achieves superior results on benchmark datasets like TuSimple and CULane, outperforming traditional methods.
  • The approach uses semantic segmentation via a convolutional neural network, specifically U-Net, effectively classifying lanes in images.
  • Overall, this method may enable more reliable lane-keeping technologies for autonomous systems, enhancing their operational capabilities.

Cite This Study

Sadasivan et al. (2025) studied this question.

synapsesocial.com/papers/68af5418ad7bf08b1eadb5eahttps://doi.org/10.55041/ijsrem51949
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Review of Deep Learning-Based Lane Detection Methods in Complex Environments2025
  2. 2Deep Learning for Lane Detection: A Comparative Analysis2024
  3. 3Lane Detection, Segmentation, Pothole Detection and Traffic Sign Recognition for ADAS2024 · 1 citations
  4. 4Lane detection networks based on deep neural networks and temporal information2024 · 7 citations
  5. 5ENet-21: An Optimized light CNN Structure for Lane Detection2024 · 2 citations