Traffic Sign Recognition System based on Belief Functions Theory

Advanced Driver Assistance Systems (ADAS) have a strong interest in road safety. This type of assistance can be very useful for collision warning systems, blind spot detection and track maintenance assistance. Traffic Sign Recognition system is one of the most important ADAS technologies based on artificial intelligence methodologies where we obtain efficient solutions that can alert and assist the driver and, in specific cases, accelerate, slow down or stop the vehicle. In this work, we will improve the effectiveness and the efficiency of machine learning classifiers on traffic signs recognition process in order to satisfy ADAS reliability and safety standards. Hence, we will use MLP, SVM, Random Forest (RF) and KNN classifiers on our traffic sign dataset first, each classifier apart then, by fusing them using the Dempster-Shafer (DS) theory of belief functions. Experimental results confirm that by combining machine learning classifiers we obtain a significant improvement of accuracy rate compared to using classifiers independently.