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.