Optimal experts' knowledge selection for intelligent driving risk detection systems

This paper presents a method for the selection of the optimal combination of experts' knowledge needed for the generation of a reliable driving risk ground truth. The driving risk of a controlled driving session, recorded in a highly realistic truck simulator, was evaluated by a large number of traffic safety experts. The risk evaluations were grouped in several clusters in order to find experts with high agreement. Next, a method for the selection of the optimal experts' evaluations is proposed. We found, through the experiments performed in this study, that a low number of experts are sufficient for the properly detection of driving risks. In addition, we show some of the advantages of the consideration of traffic safety experts' knowledge for the generation of a driving risk ground truth.