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Existing driver assistance systems are based on averaged characteristics of drivers, so the systems may cause sense of discomfort to some drivers and the effectiveness on accident prevention degrades due to low system acceptance. To deal with this issue, an individual adaptive hurry driving detection system is proposed in this research. We proposed the detection method of hurry driving using hierarchical Bayesian model. Urban driving data are collected by a continuous-logging drive recorder (DR). Features of hurry driving behavior extracted by hierarchical Bayesian method. The probability of hurry driving state is estimated by using this model using this model.

This paper describes the classification algorithm of the driving steering intention based on brain-computer interface (BCI) using electroencephalogram (EEG). The classification algorithm of the driving steering intention was examined and developed. Experiments were conducted with 3 able-bodied subjects using driving simulator (DS). The drivers were instructed to operate the vehicle according to the series of three kinds of instructions (right steer, left steer, and straight running). Those instructions were given to the subject with random order, after the operation trigger had been signaled. The off-line analytical result shows that the driver's steering intention can be classified at the averaged probability of about 80%.