In this paper, a lane detection and classification algorithm based on a monocular camera for a two-wheeled vehicle in small roll angle conditions is described. The algorithm is designed to work in different illumination conditions, both in night time and daytime. First, all road line markings are identified using a linear lane model exploiting Hough transform and perspective filtering. Identified line markings are classified based on geometric features using a suitable SVM. Then, the boundaries of the actual lane occupied by the vehicle are detected among all the possible lane marking couples. Finally, detected lane is tracked using heuristic rules. The overall strategy has been validated using videos acquired with a motorcycle in different scenarios. Experimental results have proven the robustness of the algorithm with respect to illumination changes and small variations of motorcycle roll angle, with a overall detection accuracy of 94.8% and classification accuracy of 98.1%.

A two-wheeled vehicle oriented lane detection algorithm

Nava, Dario;Panzani, Giulio;Savaresi, Sergio M.
2018-01-01

Abstract

In this paper, a lane detection and classification algorithm based on a monocular camera for a two-wheeled vehicle in small roll angle conditions is described. The algorithm is designed to work in different illumination conditions, both in night time and daytime. First, all road line markings are identified using a linear lane model exploiting Hough transform and perspective filtering. Identified line markings are classified based on geometric features using a suitable SVM. Then, the boundaries of the actual lane occupied by the vehicle are detected among all the possible lane marking couples. Finally, detected lane is tracked using heuristic rules. The overall strategy has been validated using videos acquired with a motorcycle in different scenarios. Experimental results have proven the robustness of the algorithm with respect to illumination changes and small variations of motorcycle roll angle, with a overall detection accuracy of 94.8% and classification accuracy of 98.1%.
International Conference on Intelligent Transportation Systems (ITSC), conference proceedings
978-1-7281-0321-1
978-1-7281-0323-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1073614
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