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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 proposes a driving torque control algorithm of micro-scale electric vehicle (NOVEL-I) at the scene of preceding vehicle following. First, an inter-vehicle distance controller aiming to keep a safe inter-vehicle distance is designed. Next, the experiments of vehicle following situation by automatic driving with the designed controller are realized and the effectiveness of the controller is verified. Next, the designed controller is applied to the human-vehicle closed-loop system as driver assistance systems, thereby enhancing driving comfort and reducing driving workload for human. Finally, the experiment of vehicle following by cooperative driving between the human and the controller is conducted. The experimental result in cooperative driving is compared with manual driving, and the effectiveness of the controller is verified.