Yoshiyuki Umemura

Active safety systems are gradually becoming practical with advances in environmental sensing and vehicle control technologies. Analyses of driver errors leading to road traffic accidents, however, have shown that drivers still make mistakes in their cognition of the external world and in judging between safe/not safe.

Research into drivers' behavior and active safety has advanced with improvements in driver support technologies that help compensate for the cognition, judgment, and actions of drivers. This paper introduces recent trends in research based on the above viewpoints.

Mitsuteru Kokubun, Hiroyuki Konishi, Kazunori Higuchi,
Tetsuo Kurahashi, Yoshiyuki Umemura, Hiroaki Nishi

Drivers' "prejudice" is the major cause of road traffic accidents in Japan. Here, "prejudice" refers to a driver's cognitive status being such that he or she perceives an accidental risk as being smaller than an objective risk. In this study, a simple method named SUPREME is proposed to estimate a driver's perception of risk, both in real-time and quantitatively, using driving behavior data. In addition, a simple driving simulator named TEDDY was developed to easily assess a driver's prejudice. Sixty subjects participated in a prejudice assessment trial. The validity of the assessment technique was confirmed by analyzing the driver's selection of vehicle velocity when the degree of prejudice was assessed as being high. The relationship between the assessed prejudice and a conventional aptitude test was investigated. As a result, the assessed prejudice was judged to be related to the driver's tendency to be accident-prone. This study aims to establish a basis for new types of driver assistance and training programs that prevent prejudice in the ITS epoch.

Hiroyuki Konishi, Mitsuteru Kokubun, Kazunori Higuchi,
Tetsuo Kurahashi, Yoshiyuki Umemura

If the number of road traffic accidents is to be reduced, it is essential that drivers be able to accurately assess the risks presented by their surroundings. This research aimed to develop a model that would be capable of estimating the risks presented by a scene depicting an actual driving situation. We manually input hazard information such as the other cars and pedestrians appearing in a scene, and then used the accumulated data to devise a multiple regression formula to estimate the risk. In addition to the hazard information, we devised a multiple regression formula that also considers whether a vehicle is in an intersection, as well as the speed of the vehicle. We asked a team of driving instructors to evaluate the risks, and used their evaluations as standard risk values. Using 96 variables in the multiple regression formula, we obtained a correlation coefficient of 0.973. For the hazard information, we found that the coefficients for other vehicles and elderly pedestrians were given approximately the same weighting, while a parking vehicle was afforded about twice that.