Mikio Danno

Driver’s cognitive distraction detection using AdaBoost on pattern recognition basis

Detecting the mental and physical states which occur in a driver immediately before a traffic accident and then providing information to or warning the driver is an effective means of reducing traffic accidents. This study is focused on driver distraction, a state which can easily lead to traffic accidents, and reproduced this distraction in a driving simulator by providing conversation or arithmetic tasks to the subjects. Stereo cameras were used as the means to track subjectspsila eye and head movements. These movements were tracked and their standard deviations were set as classification features of pattern recognition, and the AdaBoost method was used to detect subject distraction. The interval between heart R-waves was also added as a classifier feature, in order to improve cognitive distraction detection performance. The results were then compared with the SVM method from the AIDE Project, which was carried out as part of the EU 6th Framework Programme.

Analysis of driver behavior based on traffic incidents for driver monitor systems

Research is being conducted into a large number of on-board driver monitor systems as a means of reducing traffic accidents. In order to improve the effectiveness of these systems, it is necessary to detect the driver behavior and mental and physical state immediately before an accident, and to inform or warn the driver of the danger, or else to send an intervention signal to the pre-crash safety system and other advanced vehicle safety systems. Previous research has been conducted for conditions of apparent risk, and has used drive recorders to analyze the causes of accidents and to investigate and analyze driver behavior and other factors which are present immediately before an accident. This study involved an investigation of near-miss accidents (hereafter referred to as ldquoincidentsrdquo) by means of interviews in order to determine the driver behavior and mental and physical state immediately before the incident, when there was the potential risk of an accident. The purpose of this study is to contribute to research concerning advanced vehicle safety systems. We will here provide an analysis of the results and propose direction for future research concerning driver monitor systems.

Measurement of Driver’s Visual Attention Capabilities Using Real-Time UFOV Method

This paper proposes a new real-time method to measure the driver’s useful field of view (UFOV) while driving a car in ordinary traffic situations in an urban environment. This is called the real-time useful field of view (rUFOV) method to discriminate it from conventional UFOV measurement, which is typically performed offline and with laboratory equipment developed by Visual Awareness Inc. The proposed real-time method first tracks traffic objects that appear in the driver’s peripheral vision using a road video camera, checks the degree of the driver’s attention to these objects using a driver monitoring camera, and finally calculates the percentage reduction in the driver’s UFOV using a database acquired over an extended period of time. Preliminary results showed better performance than originally expected. The rUFOV method was then incorporated into a driving simulation environment to enable more precise measurement of the driver’s gaze angle. This enabled the performance of safer tests for identifying conditions under which mental load reduced the driver’s visual capabilities, thus increasing the possibility of hasty driving, as well as the incorporation of more accurate control parameters into simulation software for risky driving scenarios. Consequently, this paper proposes a new methodology for measuring the driver’s UFOV as a potential real-time driver support system with automatic intrusive HMI adaptation and immediate alarm functions. The evaluation was conducted in two phases. First, the system was tested in real traffic using typical vehicle equipment and technically worked with a performance level of 81%.In the second phase, more test runs were performed in the simulator environment, which enabled near accident scenarios to be created without risking traffic safety and it was also measured its reaction time..

Detection of a driver's visual attention using the online UFOV method

This paper investigates whether a drivers useful field of view (UFOV) can be measured during normal driving tasks without the driver being aware of the system. We were able to develop an online method to measure the drivers UFOV (oUFOV) automatically using a real car driving test, in the city of Tampere. The system was merged with two different technical solutions that estimated if the driver paid attention to the traffic objects that appeared in his or her peripheral vision. Here, the original laboratory method using a PC display was expanded to monitor the driver for much longer periods. By incorporating the oUFOV method into a driving simulation environment in Japan, we were also able to conduct safer tests for the drivers with reduced visual conditions, such as the drivers mental status change (hasty driving), and replace more accurate control parameters with simulation software in cases of risky driving scenarios. Thus, we confirmed that the oUFOV can measure the visual conditions for the driver and be deployed as a real-time driver support system with an automatic intrusive HMI adaptation function or immediate alarming, in the future, without driver awareness, according to the drivers visual attention capability.

Neuropsychological approach to identifying risky driving behaviors

Driving is a complex behavior that can be affected by an individuals emotional and cognitive status, and the environment. Since a neuropsychological approach is useful in identifying the complex combination of factors that may cause risky driving behaviors, and because subjective perceptions may misguide the decision-making process, this approach based on brain wave indexes was examined. The merit of this approach is that it enables any member to make decisions regarding an individuals ability to drive, despite insufficient information.

Neuropsychological Approach to Identifying Risky Driving Behaviors

A neuropsychological approach based on brain wave indexes is proposed. This approach enables everyone to make decisions regarding an individuals ability to drive, despite little information.

Automated updating of camera parameters for user-friendly driver monitoring system

This paper points to a component technology of driver monitoring system which has not been explored yet, namely, automated camera calibration. It proposes that the calibration should not be left to be done manually, as is the case now, but should be automated. In particular, this paper describes an algorithm to automatically update the camera parameters of the in-vehicle driver monitoring camera system using a planar symmetry, and discusses the accuracy and effectiveness of automated updating.