A Hybrid Deep Learning–Machine Learning Framework For Robust Road Lane Detection In Intelligent Traffic Control Systems
DOI:
https://doi.org/10.29284/savm0226Keywords:
Lane Detection, Deep Learning, Machine Learning, Convolutional Neural Network, Intelligent Traffic Control Systems, Autonomous Driving, Feature Fusion, Semantic Segmentation, Computer Vision, ADAS.Abstract
Road lane detection is a critical component of intelligent traffic control systems (ITCS) and autonomous driving platforms, yet it remains challenging under adverse conditions such as occlusion, poor illumination, and degraded lane markings. This paper proposes a novel hybrid framework that synergistically integrates deep learning (DL) and traditional machine learning (ML) techniques for robust lane detection. The deep learning branch employs a ResNet-50 backbone with spatial attention mechanisms and LSTM temporal modules coupled with a DeepLabV3+ semantic segmentation head for pixel-level lane extraction. Concurrently, the machine learning branch utilizes Histogram of Oriented Gradients (HOG) feature extraction, Canny edge detection, Hough Transform, and a Support Vector Machine–Random Forest ensemble classifier. A multi-level feature fusion module combines outputs from both branches using weighted confidence scoring. Extensive experiments on the TuSimple and CULane benchmark datasets demonstrate that the proposed hybrid framework achieves 97.42% accuracy on TuSimple and 79.8% F1-score on CULane, outperforming state-of-the-art methods while maintaining real-time inference at 62 FPS. The framework exhibits exceptional robustness across diverse environmental conditions, including night, rain, shadow, and crowded traffic scenarios.
References
1. Aly, M. (2008). Real-time detection of lane markers on urban streets. IEEE Intelligent Vehicles Symposium, pp. 7–12.
2. Bar Hillel, A., Lerner, R., Levi, D., & Raz, G. (2014). Recent progress in road and lane detection: a survey. Machine Vision and Applications, 25(3), 727–745.
3. Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. ECCV, pp. 801–818.
4. Dataintelo. (2024). Lane-by-Lane Detection with AI Market Research Report 2033. Market Report.
5. Dimension Market Research. (2025). Autonomous Driving Market Report.
6. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR, pp. 770–778.
7. Kim, Z. (2008). Robust lane detection and tracking in challenging scenarios. IEEE Transactions on Intelligent Transportation Systems, 9(1), 16–26.
8. Li, X., et al. (2024). LanEvil: Benchmarking the robustness of lane detection to environmental illusions. ACM Multimedia.
9. Liu, L., Chen, J., et al. (2025). Lane line detection based on cross-convolutional hybrid attention. Nature Scientific Reports, 15, 01167.
10. Nature. (2025). Cascade Drive: A unified deep learning framework for multi-featured lane detection. Scientific Reports, 15, 06567.
11. Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., & Van Gool, L. (2018). Towards end-to-end lane detection: an instance segmentation approach. IEEE Intelligent Vehicles Symposium, pp. 286–291.
12. NHTSA. (2023). Estimating effectiveness of lane keeping assist systems in fatal road departure crashes. Publication 813663.
13. Pan, X., Shi, J., Luo, P., Wang, X., & Tang, X. (2018). Spatial as deep: Spatial CNN for traffic scene understanding. AAAI Conference on Artificial Intelligence.
14. Qin, Z., Wang, H., & Li, X. (2020). Ultra-fast structure-aware deep lane detection. ECCV, pp. 276–291.
15. Qin, Z., Zhang, P., & Li, X. (2022). Ultra-fast deep lane detection with hybrid anchor-driven ordinal classification. IEEE TPAMI.
16. SkyQuest Technology. (2025). Lane Departure Warning System Market Size, Share and Analysis.
17. Tabelini, L., Berriel, R., Paixao, T. M., Badue, C., De Souza, A. F., & Oliveira-Santos, T. (2021). Keep your eyes on the lane: Real-time attention-guided lane detection. CVPR, pp. 294–302.
18. Wang, J., et al. (2023). Efficient spatial and channel net for lane marker detection. Nature Scientific Reports, 13, 47071.
19. WHO. (2024). Global Status Report on Road Safety.
20. Zheng, T., et al. (2022). CLRNet: Cross-layer refinement network for lane detection. CVPR, pp. 898–907.
21. Zion Market Research. (2024). Lane Departure Warning System Market Report.
22. AAA Foundation for Traffic Safety. (2024). Potential Reduction in Crashes, Injuries and Deaths from Large-Scale Deployment of ADAS.
23. DEKRA. (2024). Road Safety Report 2024.
24. IIHS. (2024). Advanced driver assistance: real-world benefits. Insurance Institute for Highway Safety.
25. Hou, Y., Ma, Z., Liu, C., & Loy, C. C. (2019). Learning lightweight lane detection CNNs by self-attention distillation. ICCV, pp. 1013–1021.
26. Liu, R., et al. (2021). CondLaneNet: A top-to-down lane detection framework based on conditional convolution. ICCV.
27. Feng, S., et al. (2025). ElasticLaneNet: An efficient geometry-flexible lane detection framework. WACV.
28. MDPI Sensors. (2024). A framework for optimizing deep learning-based lane detection. Sensors, 24(24), 8099.
29. Xu, H., et al. (2025). Grid anchor lane detection based on attribute correlation. Applied Sciences, 15(2), 699.
30. Intellias. (2025). Intelligent traffic management systems: 6 key features. Technical Report.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
