A Universal Approach to Detect and Classify Road Surface Markings

Fabian Poggenhans and Markus Schreiber and Christoph Stiller
IEEE Int. Conf. Intelligent Transportation Systems
In autonomous driving, road markings are an essential element for high-precision mapping, trajectory planning and can provide important information for localization. This paper presents an approach to detect, classify and approximate a great variety of road markings using a stereoscopic camera system. We present an algorithm that is able to classify characters and arrows as well as stop-lines, pedestrian crossings, dashed and straight lines, etc. The classification is independent of orientation, position or the exact shape. This is achieved using a histogram of the marking width as main part of the feature vector for line-shaped markings and Optical Character Recognition (OCR) for characters. Classification is done by an Artificial Neural Network (ANN). We have evaluated our approach over a 10.5 km drive through an urban area.
Research focus
Safe and Intelligent Vehicles
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Published by
Fabian Poggenhans