Abstract
Vision-based lane analysis has been investigated to different degrees of completeness. While most studies propose novel lane detection and tracking methods, there is some research on estimating lane-based contextual information using properties and positions of lanes. According to a recent survey of lane estimation in [7], there are still open challenges in terms of reliably detecting lanes in varying road conditions. Lane feature extraction is one of the key computational steps in lane analysis systems. In this paper, we propose a lane feature extraction method, which enables different configurations of embedded solutions that address both accuracy and embedded systems’ constraints. The proposed lane feature extraction process is evaluated in detail using real-world lane data to explore its effectiveness for embedded realization and adaptability to varying contextual information such as lane types and environmental conditions. Accuracy of more than 90 % is obtained during the evaluation of the proposed method using real-world driving data.
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Satzoda, R.K., Trivedi, M.M. (2014). Vision-Based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization. In: Kisačanin, B., Gelautz, M. (eds) Advances in Embedded Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-09387-1_10
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DOI: https://doi.org/10.1007/978-3-319-09387-1_10
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