The Study Of Driver's Low-alertness Detection Technology

Traffic accident, which directly relates to the loss of both people's lives and prosperities, is one of the problems concerned by the whole world. In recent years, the traffic accidents arise more and more frequently along with the growth of the number of cars and drivers, causing a lot more damages to people, society and world than any other form of natural and social disaster. Quantities of statistics show that more than 70 percent of road accidents are directly related with drivers and their driving behavior characteristics. Every year on the high-way, driver's low-alertness behavior is one of the important causes of serious accidents. The research is about to improve the driving features, ensuring the driving security and prevent the traffic accidents.The researches of driving behavior characteristics mainly focus on the driver's psychology cognitive function, physical stress response characteristics, driving adaptability and so on. Driver's physiology and psychology are mostly studied through the single factor analysis with the questionnaire data investigation and the laboratory simulation analysis. The changing rule of the driving behaviors is studied through statistical analysis and essential reason causing dangerous driving is studied through the driving behaviors'characteristics, providing scientific basis for traffic safety management.In particular the laboratory environment conditions of the simulation research and practical driving while driving the scene has essential difference in driving vigilance and motorists excited extent have bigger difference. The mathematical representative model, which is based on previous drivers driving behavior characteristic data statistics, certainly could conduct the driving rule. But when it comes to the application to the security of a driver personal, the model can hardly satisfy the practical application, giving consideration to the difference of the driver's physiology, psychology and the environmental varieties. The positively engineering practical research results about the monitoring and analyzing on driving behaviors in real time haven't been reported yet.Alertness degree is used for depicting the sensitivity when people focus on operating. People-machine interactive system needs people to keep focusing. For example, a driver needs to keep sensitivity when he drives in the highway. Driving sensitivity is a capability of responding effectively to the driving information without discontinuity. One tiny mistake may lead to a traffic accident. This inattentive behavior-driving in low alertness, is the research center of this paper. This subject is from the subject-research on the monitoring and warning analysis technology about drivers'unsafe driving behaviors, which is a sub-subject of the national science and technology support program subject-safe driving behavior analysis platform and monitoring technology and demonstration project. According to the existing problem, this study focuses on the research of the accidental mechanism causing by the drivers, the unsafe driving behavior rule, the characteristics and the deep reasons, broadening the depth and breadth of the mechanism of the safe driving behavior.Drivers in normal driving and alertness lower have different driving behavior characteristic: lane departure, dangerous relative distance between cars, the driver watch characteristics, the speed and direction controlling, the reaction of the driver and the working intensity. Based on the heart waves information, brain wave information, pulse pressure, blood pressure and metabolic product monitoring, these methods is accurate and directly to the correct evaluation and judgment. But contact measurement and complex instrumentations are hard to use in the vehicle real-time online. Aiming at the existing problems, the low-alertness driving behaviors are the research subject in this paper. Construct a real-time monitoring and analyzing platform for safe driving behaviors through influencing factors of the driving behaviors. Make the detection analysis technique on driving behaviors better from individual to the group, from point to surface and from outside to inside, according to the organizational structure and process of the driving behaviors. The experiment is set for different unsafe conditions, detecting the state of the driver and the vehicle including different drivers.The driver's low-alertness behavior characteristics is very complicated, including information of the lane departure, the position and velocity of other vehicles and other targets, the state of the driver and so on. The information of the driver, which is got by machine vision, consists of two parts:the information of the driver's awareness and the vehicular exterior characteristic information. The lane departure state and the dangerous vehicle-following distance are detected through real-time information about the departure and the relative distance between vehicles. Researches about real-time detections of facial features of drivers are extracted through the horizontal width of the eyes and the longitudinal distance between the mouth (pupil) to the eyes in the midpoint-the "T" type face information safety features. These are for the detections of the distraction state.Construct the low-alertness driving monitoring system based on multi-agents theory and extract the feature information about the low-alertness driving behaviors from quantities of statistics. The feature is classified by Bayesian and neural technique. The match of the multi-agents system and the strategic rule is based on Dempster-Shafer evidence theory, estimating the low-alertness driving behaviors intelligently and improving the adaptability, accuracy and intelligent of the representation on the low-alertness driving behaviors.