Automatic driving risk detection based on hands activity

In this paper a novelty methodology to measure driving risk based on hands activity is presented. The proposed algorithm has been developed and tested on several driving sessions executed on a highly realistic truck simulator. The hands positions are used to feed a risk buffer that is in charge of penalizing wrong hands activities and praising good hands activities to generate a measure of the driving risk. In order to select the parameters of the proposed system, a genetic algorithm (GA) and a ground truth acquired from a group of traffic safety experts were considered. The results of the proposed methodology on several driving sessions showed its effectiveness to automatically detect risky situations related to bad driver´s hands behavior. The present system will be integrated in a global alarm system that will be included in an intelligent truck cabin for road transportation.