Development of a Hurry Driving Detection Algorithm Based on the Driving-State Transition Model

Abstract

In Japan, a report has shown that being hurry is the greatest category of the driver mental states during car accidents. In this paper, we present the analysis and detection algorithm especially developed for hurry driving in several driving situations. The most effective driving feature in each situation is selected based on the comparative statistical analysis of the driver behavior in the normal and hurry driving conditions. It is found that the features that classify the hurry driving well in most situations are the time headway, the host vehicle velocity and the longitudinal acceleration. Through these features, the appropriate classification rules are determined, which allows us to later develop the hurry-driving detection algorithm based upon the driving-state transition model. The algorithm is then validated by offline simulations with collected driving data. The results clearly confirm the practicability and effectiveness of the proposed algorithm.