COMPUTER VISION APPLIED TO ROAD LINES RECOGNITION USING MACHINE LEARNING.

According to the Department for Transport statistics in UK, around 100.000 accidents were reported in 2013 [13], and almost 25% of them were related to impairment or distraction factors. Advanced Driver Assistance Systems (ADAS) are a powerful tool for road safety that can help to mitigate this problem. This paper presents a robust road lane detection and classification algorithm, one of the most important tasks in ADAS. This paper describes a road line detection algorithm based on a segmentation algorithm designed according to the constraints defined in the legal regulation for road marks. Later, pairs of lines, separated a fixed distance, are searched in the bird view of the road image. The bird view transformation is applied to the captured images, using the extrinsic parameters estimation algorithm reported in [10]. After the extraction of the road lines profiles, they are characterized using a specifically designed descriptor based on both space and frequency values. The descriptors are used in the supervised training of a Support Vector Machines classifier, whose performance is compared against the previous version of the module, a heuristic based approach. The performed tests showed a considerable increase of the system performance using the SVM approach, in comparison with the previous heuristic approach. [ABSTRACT FROM AUTHOR]