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The analysis of driver's behavioral tendency under different emotional states based on a Bayesian Network

Affective factors have been associated with an array of driving behaviors. However, the mechanism by which emotion influences driving behaviors remains largely unknown. In the present study, a probabilistic approach for characterizing the emotional influence on driving behavioral decision-making was proposed. Firstly, a series of experiments were designed to obtain the human-vehicle-environment data when the drivers, whose emotional states were effectively induced, were driving a vehicle in the car-following scene. Next, a Bayesian Network (BN) was developed to model the causal relationships between driving behavioral tendency, driver's emotion and other indicators related to vehicle driving. Finally, the driver's different tendencies in driving behavior caused by emotional factors were analyzed through calculating the probability distribution of driving behavioral tendency under different emotional conditions in the BN. The research results are not only beneficial to improve the accuracy of driving behavior identification and prediction which is of great significance for vehicle active safety, but also have a potential promoting effect on human-computer harmonious interaction of vehicle system.