Abstract
Detecting lane markings is a challenging task for vision-based systems due to uncontrolled lighting environments present on the roads. Road infrastructures surrounding the painted markings such as guardrails and curbs often reduce the accuracy of existing solutions. The mentioned infrastructure frequently behaves like lane markings increasing the occurrence of candidate features to be selected by lane detectors. Most of the lane detectors use machine learning techniques with long training phases and inflexible models to achieve some level of robustness, therefore an efficient approach capable of performing unsupervised learning is required. The adoption of an efficient model, which can monitor the ego-lane boundaries while identifying false positive references, is discussed in this paper. The proposed architecture allows the combination of multiple image-processing cues to improve accuracy and robustness on vision-based methods. Our method performed with high accuracy in lane marking detection under highly dynamic lighting, including in presence of guardrails.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hur, J., Kang, S.N., Seo, S.W.: Multi-lane detection in urban driving environments using conditional random fields. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 1297–1302. IEEE (2013)
Pomerleau, D., Jochem, T.: Rapidly adapting machine vision for automated vehicle steering. IEEE Expert 11, 19–27 (1996)
Bertozzi, M., Broggi, A.: Real-time lane and obstacle detection on the gold system. In: Proceedings of the 1996 IEEE Intelligent Vehicles Symposium, pp. 213–218. IEEE (1996)
Borkar, A., Hayes, M., Smith, M.: An efficient method to generate ground truth for evaluating lane detection systems. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1090–1093. IEEE (2010)
Kreucher, C., Lakshmanan, S.: Lana: a lane extraction algorithm that uses frequency domain features. IEEE Transactions on Robotics and Automation 15, 343–350 (1999)
Labayrade, R., Douret, J., Aubert, D.: A multi-model lane detector that handles road singularities. In: IEEE Intelligent Transportation Systems Conference, ITSC 2006, pp. 1143–1148. IEEE (2006)
Lu, W., Florez, S.A.R., Seignez, E., Reynaud, R., et al.: An improved approach for vision-based lane marking detection and tracking. In: International Conference on Electrical, Control and Automation Engineering, pp. 382–386 (2014)
Wang, Y., Dahnoun, N., Achim, A.: A novel system for robust lane detection and tracking. Signal Processing 92, 319–334 (2012)
Yoo, H., Yang, U., Sohn, K.: Gradient-enhancing conversion for illumination-robust lane detection. IEEE Transactions on Intelligent Transportation Systems 14, 1083–1094 (2013)
Sehestedt, S., Kodagoda, S., Alempijevic, A., Dissanayake, G.: Efficient lane detection and tracking in urban environments. In: Proc. European Conf. Mobile Robots, pp. 126–131 (2007)
Wang, Y., Shen, D.G., Teoh, E.K.: Lane detection using spline model. Pattern Recognition Letters 21, 677–689 (2000)
Zhou, S., Jiang, Y., Xi, J., Gong, J., Xiong, G., Chen, H.: A novel lane detection based on geometrical model and gabor filter. In: IEEE Intelligent Vehicles Symposium, pp. 59–64 (2010)
Felisa, M., Zani, P.: Robust monocular lane detection in urban environments. In: 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 591–596. IEEE (2010)
Bottazzi, V.S., Borges, P.V., Jo, J.: A vision-based lane detection system combining appearance segmentation and tracking of salient points. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 443–448. IEEE (2013)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56, 221–255 (2004)
Bottazzi, V.S., Borges, P.V.K., Stantic, B., Jo, J.: Adaptive regions of interest based on HSV histograms for lane marks detection. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications 2. AISC, vol. 274, pp. 677–687. Springer, Heidelberg (2014)
Deusch, H., Wiest, J., Reuter, S., Szczot, M., Konrad, M., Dietmayer, K.: A random finite set approach to multiple lane detection. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 270–275. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bottazzi, V.S., Jo, J., Stantic, B., Borges, P.V.K. (2015). An Efficient Ego-Lane Detection Model to Avoid False-Positives Detection of Guardrails. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-16841-8_67
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
Online ISBN: 978-3-319-16841-8
eBook Packages: EngineeringEngineering (R0)