Fuzzy Logic to Evaluate Driving Maneuvers: An Integrated Approach to Improve Training

Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us from building better training programs. Currently, there is a need to develop and evaluate advanced driving assistance systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel intelligent driver training system that analyzes crash risks for a given driving situation, providing avenues for the improvement and personalization of driver training programs. The analysis takes into account numerous variables synchronously acquired from the driver, the vehicle, and the environment. The system then segments out the maneuvers within a drive. This paper further presents the fuzzy set theory to develop the safety inference rules for each maneuver executed during the drive, and presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving maneuvers and then provides a comprehensive analysis of the drive used to give feedback to novice drivers.