Study On Detecting And Evaluating Technique Of Motor Driver Fatigue

With the development of society, motor driver fatigue is increasingly reported as a problem concerning the road traffic safety. Recent research reveals that approximately 25-30% of vehicle collisions result from driver fatigue. The chance for fatalities is very high. Many of us have experienced sleepiness while driving. Fatigue may adversely affect drivers' alertness and ability to drive safely. The study situation on detecting and evaluating techniques of motor driver fatigue in the world (mainly in the U.S.A) is reviewed in this paper. There are two kinds of research method for motor driver fatigue. One is the subjective investigating and evaluating method. The commonly used investigating forms include subjective investigating form, driver self-record form, driver sleep habit investigating form and Stanford Sleepiness Scale rating. Another is the objective detecting and evaluating method. The commonly used detecting and evaluating techniques include the drowsy driver detection system, the steering attention monitor, the road alert system and the psychomotor vigilance tester, etc.Fatigue driving is a serious problem that leads to thousands of vehicle crashes each year. The drowsiness and fatigue may play an important role in traffic crashes.Of the drowsiness-detection measures and technologies evaluated in this study, the measure referred to as "PERCLOS" is found to be the most reliable and valid determination of a drivers alertness level. PERCLOS is the percentage of eyelid closure over the pupil over time and reflects slow eyelid closures ( "droops" ) rather than blinks. FWHA and NHTSA consider PERCLOS to be among the most promising known real-time measures of alertness for in-vehicle drowsiness-detection systems..In order to detecting and evaluating driver fatigue, the definition of motor driver fatigue must be given. The motor driver fatigue is considered that the physiological function, psychological function and driving operating efficiency of a motor driver decrease to such level he is unable to control the vehicle safely and correctly when any unexpected situation is encountered.A convincing case has been made that slow eyelid droop PERCLOS has the best potential to detect driver fatigue. But commercially available software technology was not reported and the predictive validity of that technology was not high.The mechanisms of PERCLOS are describe in this paper. A PERCLOS-based recognition algorithms of motor driver fatigue is developed and achieved. By using the image recognition and gray scale mode matching technique. A mathematical model o f motor driver's fatigue is proposed. Skin color pixels in chromatic color space can be represented using a Gaussian distribution. A human face skin color self-adaptive parameters model is developed.A hardware system for detecting and evaluating motor driver fatigue is set up. A set of color video camera points directly toward the driver's face in order to detect driver fatigue. A software system for acquisition, recognition and evaluation of the driver's face image is developed to realize the PERCLOS -based recognition algorithms of motor driver fatigue mentioned above. A feasible method based on the mathematical model and symmetry analysis is presented to detect and locate the driver's eye in an image and the gray scale mode matching technique is used to determine the driverfatigue level. An example for detecting the eyelid closure over the pupil over time is given. The combinations of mathematical model and symmetry analysis increases the robustness of the performance while the target is deformed The scheme is suitable for human face location in intelligent human-machine interface and provides a groundwork to practical application efface recognition techniques.The laboratory experiments are conducted to verify the feasibility of the detecting and evaluating techniques of motor driver fatigue mentioned above and to determine the threshold of motor driver fatigue. Based on the experiment of 38 drivers who are self-stated fatigue, the th...