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Recently, a number of researches on detecting such inattentive driving have been carried out, and various detection methods have been proposed. In this paper, using driving data which can be measured with existing on-board vehicle sensors, the authors focus on headway distance behavior which relates to rear-end collision as well as the steering behavior which can be used to detect driver drowsiness. The emphasis of this study is that the inattentive driving can be potentially detected from the driving behavior deviated from the normative characteristics considering the transfer function from the environment to the driving operation. Therefore, intrusive measurement methods such as physiological measurement are not required. In addition, to detect the inattentive state by the criterion of the individual driver, the driver assistance system can be realized in the way that it can be adapted not only to various types of drivers, but also to the driver real-time driving state.

Highly-precise ego-localization and mapping techniques from the road shape features are key elements in order to realize an autonomous driving system for vehicle in urban area which has complex environments. The objective of this study is to develop an autonomous driving system based on mapping and ego-localization using a LIDAR. To handle curved path tracking scenario, this paper proposes a desired steering angle generator considering a constructed map using the LIDAR in real time combined with the feedback control of the preview lateral deviation. The effectiveness of the proposed control method is verified by simulation and test drives using the autonomous path tracking control system.

Small size electric vehicles require active safety technology to prevent road accidents as well as protect drivers and passengers from severe damages. This paper proposes an intersection collision avoidance system by automatic electric braking utilizing in-wheel-motors of electric vehicles. The proposed vehicle control system focuses on the risk assessment in consideration of pedestrians. Consequently, a risk avoidance algorithm based on Potential Fields theory is described to handle the hazardous situations which change dynamically with pedestrian motion. Finally, the effectiveness of the autonomous driving system is verified by the experiments using the micro-scale electric vehicle. The results show that the proposed autonomous vehicle can accomplish the right turn with negotiating a moving pedestrian.

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.

The content of this paper is related to the analysis on driver characteristics in vehicle following situation using drive recorder. The characteristics are extracted from captured data related to rear-end collision incident which are collected on public road experiment using taxi. Main topic of this paper is concerning how drivers change their rear-end distance and cruising velocity according to snowy or icy road surface conditions. The characteristics are compared with public road experiment and experiment on proving ground. From the experimental results, the error of road surface estimation of driver could be one of reasons for the trigger of rear-end collision incident in snowfall season.

It is necessary to examine the driver behavior in intersections since many traffic accidents occur in the area. During yellow light, drivers occasionally cause the dilemma either passing through the intersection or stopping at stop line. In this paper, using the drive recorder equipped vehicle, many incident data that occurred during yellow light has been investigated. Using the collected data, actual dangerous zone during yellow light was extracted. Because the zone is one of the targets for accident prevention, the obtained understandings are useful in considering measures for enhancing safety such as road-vehicle communications.