Combining experts knowledge for driving risks recognition

In this paper, a methodology to recognize driving risk situations as the solution of a combination of information problem is presented. A collection of simulated sessions in a highly realistic truck simulator were designed and executed. Several internal truck magnitudes and visual information from the driver and the road were collected in each session. Two traffic safety experts were asked to evaluate the driving risk of the exercises using a simulation reproduction tool (developed for this purpose) and a Visual Analog Scale (VAS). These evaluations were used to define four different, and complementary, models for driving risk recognition. A method to calculate these models by the maximization of a similarity measure between expert evaluations is presented. Finally, a third traffic safety expert was consulted for validation purposes. Results show that the proposed models are useful and able to recognize abnormal drivers behavior. Good generalization results were obtained when the parameters learned for each risk definition were validated in additional simulated sessions.