Driver State Detection Based on Cardiovascular System and Driver Reaction Information Using a Graphical Model

Traffic accidents are mainly caused by human error. In an aging society, the number of accidents attributed to elderly drivers is increasing. One noteworthy reason for this is operation misapplication. Studies have been conducted on the use of human-machine interfaces (HMIs) to inform the driver when he or she makes an error and encourage appropriate actions. However, the driver state during the erroneous action has not been investigated. The purpose of this study is to clarify the difference in the driver’s state between normal and surprising situations in a misapplication scenario, utilizing multimodal information such as biometric information and driver operation. We found significant changes in the interaction of components between the normal and the surprised driving state. The results could provide basic knowledge for the future development of a driver assistance system and driver state estimation using data acquired from multiple sensors in the vehicle.

Effect of Difference in Form of Driving Support Agent to Driver’s Acceptability<br/>—Driver Agent for Encouraging Safe Driving Behavior (2)

In recent years, the number of traffic accidents caused by elderly drivers has increased in Japan. However, a car is an important mode of transportation for the elderly. Therefore, to ensure safe driving, a system that can assist elderly drivers is required. In this study, we propose a driver-agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. This paper describes the prototype system based on the analysis of the teaching records of a human instructor, and the subjective evaluation of driving support to elderly and non-elderly driver from three different agent forms, a voice, visual, and robot. The result revealed that the robot form is more noticeable, familiar, and acceptable to the elderly and non-elderly than other forms.

Evaluation of Driver Distraction with Changes in Gaze Direction Based on a Vestibulo-Ocular Reflex Model

With the aim of improving parameter identification and, eventually, evaluating driver distraction with changes in gaze direction, we applied a genetic algorithm (GA) method to identify parameters for an existing vestibulo-ocular reflex (VOR) model. By changing the initial inputs to the GA and fixing two parameters pertaining to the horizontal direction, we achieved improved parameter identification with a lower mean-square error. The influence of driver distraction on eye movement with changes in gaze direction was evaluated from the difference between the predicted and observed VOR in the vertical axis. When a driver was given an additional mental workload, the mean-square error between the measured and simulated values was bigger than that in the absence of the mental workload. This confirmed the relationship between driver distraction and eye movement in the vertical direction. We hope that this method can be applied in evaluating driver distraction.

Relationship between Senior Drivers’ Cognitive and Physical Functions and Urban Driving Behaviors during Behind-the-Wheel Driving Education Sessions with Driving Instructors

Recently, traffic accidents caused by elderly drivers have been increasing. It is thought that the reasons of accidents are functions such as decline of eyesight, cognition and physical strength caused by aging. In order to assist safe driving for elderly drivers, it is necessary to sufficiently understand any possible relationship of various senior drivers’ cognition, physical strength and driving behavior. In this paper relationship of elderly driver’s cognition, physical strength and driving behaviors were analyzed using the result of driving instructions on urban road by driving school instructors.

Preliminary Study for Feasibility of Driver Agent in Actual Car Environment<br/>—Driver Agent for Encouraging Safe Driving Behavior (3)

Japan has become a more aged society and there are more drivers, 65 years of age and above. Cars represent an important mode of transportation for the elderly;however, in recent years, the number of traffic accidents caused by elderly drivers has been on the rise, and this has become a social issue. Thus, for the elderly drivers to encourage them to improve their driving, we study a driver agent system which consists of smartphone, communication robot and cloud service and provides the driving support by attention awakening and the feedback support based on driving behavior evaluation. In this paper, we presented a summary of the proposed agent and reported on a set of preliminary experiments using our agent in an actual car environment. From the analysis of subjective evaluations and fixation points during driving, the results revealed the possibility that the drivers accept the agent and supports from the agent during driving and that the agent in an actual car environment did not distract the driver.