Road lane and traffic sign detection and tracking for autonomous urban driving

Abstract

The field of Intelligent Transport Systems (ITS) is advancing rapidly in the world. The ultimate aim of such systems is to realize fully autonomous vehicles. The researches in the field offer the potential for significant enhancements in safety and operational efficiency. Lane tracking is an important topic in autonomous navigation because the navigable region in a road usually stands between the lanes, especially in urban environments. Several approaches have been proposed, but Hough transform seems to be the dominant one among all. A robust lane tracking method is also required for reducing the effect of the noise and satifying processing time constraints. In this study, we present a new lane tracking method which uses a partitioning technique for obtaining Multi-resolution Hough Transform (MHT) of the acquired vision data. After the detection process, a Hidden Markov Model (HMM) based method is proposed for tracking the detected lanes. Traffic signs are important instruments to indicate the rules on roads. This makes them an essential part of the ITS researches. It is clear that leaving traffic signs out of concern will cause serious consequences. Although the car manufacturers have started to deploy intelligent sign detection systems on their latest models, the road conditions and variations of actual signs on the roads require much more robust and fast detection and tracking methods. Localization of such systems is also necessary because traffic signs differ slightly between countries. This study also presents a fast and robust sign detection and tracking method based on geometric transformation and genetic algorithms (GA). Detection is done by a genetic algorithm (GA) approach supported by a radial symmetry check so that false alerts are considerably reduced. Classification is achieved by a combination of SURF features with NN or SVM classifiers. A heuristic alternative to the SURF usage is also presented. Time and accuracy analysis can be found in relevant sections. This work is a part of the Automatic Driver Evaluation System (ADES) Project in Artificial Intelligence Laboratory of Boğaziçi University.