An agile verification framework for traffic sign classification algorithms in heavy vehicles
This paper presents an agile approach to facilitate the rapid development of traffic sign classification algorithms in heavy vehicles under a wide range of visibility conditions. A vision based traffic sign recognition system makes a significant contribution to improving the transportation safety by enhancing the driver's awareness on important road signs in an automotive cockpit environment. It has therefore been conducive to the ongoing innovation of Advanced Driver Assistance Systems (ADAS) which paves the way to autonomous driving. The paper introduces a real time framework which can benchmark the performance of the image processing and machine learning algorithms at the electronic system level using an open loop hardware in the Loop (HiL) simulation. The presented research provides a generative model with real world datasets to improve the classification performance of machine learning algorithms. A driving scenario considering speed limit traffic signs describes an automatic parameter selection to find the best separating hyperplane for the support vector machine (SVM) classifiers. The framework supports an evolutionary verification process for the traffic sign classification algorithms on the electronic control unit (ECU) in the laboratory.
2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)