Robust Road Boundary Extraction in Unstructured Environments: A Few-Shot Adaptation Approach

Paper ID: EIJTEM_2026_13_2_104-116

Author's Name: Jay Gupta, Dr Meenakshi Gupta

Volume: 13

Issue: 2

Year: 2026

Page No: 104-116

Abstract:

Autonomous driving perception systems depend heavily on accurate road understanding. Most existing lane detection approaches assume the presence of clear lane markings and structured road layouts. However, such assumptions often fail in unstructured environments commonly found in developing regions. This paper proposes a robust road boundary extraction framework designed to operate in chaotic traffic scenarios where lane markings may be missing or unreliable. The proposed approach combines deep feature extraction with a few-shot adaptation mechanism that enables rapid learning of road boundary characteristics using minimal labeled data. The system integrates convolutional feature encoders with prototype-based adaptation to generalize across diverse road conditions. Experimental evaluation on publicly available datasets demonstrates improved robustness and generalization compared to conventional lane detection methods.

Keywords: Robust Road Boundary Extraction, Unstructured Environments, Few-Shot Adaptation Approach

References:

Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder–decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.
Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., ... & Schiele, B. (2016). The Cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3213–3223.
Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, 1126–1135.
Neven, D., De Brabandere, B., Proesmans, M., & Van Gool, L. (2018). Towards end-to-end lane detection: An instance segmentation approach. IEEE Intelligent Vehicles Symposium, 286–291.
Pan, X., Shi, J., Luo, P., Wang, X., & Tang, X. (2018). Spatial as deep: Spatial CNN for traffic scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7266–7275.
Qin, Z., Wang, H., & Li, X. (2020). Ultra fast structure-aware deep lane detection. Proceedings of the European Conference on Computer Vision, 276–291.
Snell, J., Swersky, K., & Zemel, R. (2017). Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems, 4077–4087.
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., ... & Darrell, T. (2020). BDD100K: A diverse driving dataset for heterogeneous multitask learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2636–2645.
Bertozzi, M., & Broggi, A. (1998). GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Transactions on Image Processing, 7(1), 62–81.
Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., ... & Schiele, B. (2016). The Cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3213–3223.
Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, 1126–1135.
Koch, G., Zemel, R., & Salakhutdinov, R. (2015). Siamese neural networks for one-shot image recognition. Proceedings of the ICML Deep Learning Workshop.
McCall, J. C., & Trivedi, M. M. (2006). Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation. IEEE Transactions on Intelligent Transportation Systems, 7(1), 20–37.
Neven, D., De Brabandere, B., Proesmans, M., & Van Gool, L. (2018). Towards end-to-end lane detection: An instance segmentation approach. IEEE Intelligent Vehicles Symposium, 286–291.
Pan, X., Shi, J., Luo, P., Wang, X., & Tang, X. (2018). Spatial as deep: Spatial CNN for traffic scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7266–7275.
Qin, Z., Wang, H., & Li, X. (2020). Ultra fast structure-aware deep lane detection. Proceedings of the European Conference on Computer Vision, 276–291.
Snell, J., Swersky, K., & Zemel, R. (2017). Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems, 4077–4087.
Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., & Urtasun, R. (2018). MultiNet: Real-time joint semantic reasoning for autonomous driving. IEEE Intelligent Vehicles Symposium, 1013–1020.
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., ... & Darrell, T. (2020). BDD100K: A diverse driving dataset for heterogeneous multitask learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2636–2645.

View PDF