Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Study on Large Power Tractor Driver's Heart Rate and Fatigue in Sowing Work

In order to reduce the driving fatigue in sowing work, this paper based on heart rate （HR） as the main indicator to survey, tested and analyzed the fatigue condition of the drivers of three imported tractors and one domestic tractor in sowing work. The results showed that when driving the imported tractors in sowing work, the HR increasing rate was 10.4%-14.3%, labor intensity belonged to the light level; when driving domestic tractor in sowing work, the HR increasing rate was 23.4%-33.0%, it was remarkably bigger than that of driving imported tractors （P〈0.05）, labor intensity belonged to the middle level. The main effects on driving fatigue included the control methods, tractors＇ cab environment, processing time, operating content, and so on. Finally, we proposed the concrete measures and suggestions to reduce driving fatigue and improve drivers＇ work condition.

FATIGUE LIVES FOR INDUCTION HARDENED SHAFTS MATERIALS ACCORDING TO THE ENVIRONMENTAL TEMPERATURES

Rotary bending fatigue tests were carried out with two kinds of materials, S43C and S50C, using the front engine and front drive shaft （FF shaft） of vehicle. The specimens were induction hardened about 1.0mm depth from the specimen surface, and the hardness value on the surface was about HRC56-60. The tested environment temperatures were -30, 25 and 80℃ in order to look over effect of the induction hardening and the environmental temperatures on the fatigue characteristics. The fatigue limit of induction hardened specimens increased more about 45% than non-hardened specimens showing that the endurances of S43C and S50C were 98.1 and 107.9MPa in non-hardened samples, 147.1 and 156.9MPa in hardened samplesrespectably. The maximum tensile and compressive stress on the small circular defect was about ＋250 and -450MPa respectively when circular defect is situated on top and bottom. The fatigue life increased 80, 25 and -30℃ in order regardless of hardening. In comparison of the fatigue lives on the basis of tested result at 25℃, the fatigue lives of non-hardened specimens decreased about 35%, but that of hardened specimens decreased about only 5% at 80℃ more than at 25℃. And fatigue life of non-hardened and hardened specimens were about 110% and 120% higher at -30℃ than that of 25℃. Based on the result of stress distribution near the defect, the tensile and compressive stress repeatedly generated by load direction were the largest on the small circular defect due to the stress concentration.

Analysis of HAZMAT truck driver fatigue and distracted driving with warning-based data and association rules mining

Professional drivers are more frequently exposed to longer driving distance and travel time,leading to a higher possibility of safety risk for distraction and fatigue.The widespread and common use of commercial driver monitoring systems(DMS)provides a potential for data collection.It increases the amount of data characterizing driver behavior that can be used for further safety research.This study utilized DMS warning-based data and applied an association rule mining approach to explore risk factors contributing to hazardous materials(HAZMAT)truck driver inattention.A total of 499 HAZMAT truck driver inattentive warning events were used to find rules that will predict the occurrence of driver’s fatigue and distraction.First,Fisher’s exact tests were performed to examine the association between the frequency of driver inattentive behavior warnings and risk factors.Second,support,confidence,and lift values were used as measurements to quantify the relative strength of the association rules generated by the Apriori algorithm.Results show that speed between 40and 49 km/h,relatively longer travel time(3-6 h),freeway,tangent section,off-peak hour and clear weather condition are found to be highly associated with fatigue driving,while nighttime during 18:00 to 23:59,speed between 70 and 80 km/h,travel time between 1 and 3 h,freeways,acceleration less than 0.5 m/s^(2),visibility greater than 1000 m,and tangent roadway section are found to be highly associated with distracted driving.By focusing on the specific feature groups,these association rules would help in the development of mitigating distraction and fatigue driving countermeasures and enforcement approaches.

Fatigue driving detection based on Haar feature and extreme learning machine

As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers＇ fatigue driving by using the human eye tracking technology.

Research on Driver Fatigue Early Warning Method Based on ARM

Fatigue driving is one of the important reasons of road traffic accidents, fatigue driving is refers to the driver in a long time continuous driving or physical fatigue condition, and then come into being physiological and psychological function disorder. In order to overcome the limitation of single sensor in the fatigue test, aimed at the requirements of monitoring on the fatigue driving, this article designed an driver fatigue monitor system based ARM926EJ-S as a controller, it is used to determine the driver＇s fatigue and reduce the traffic accident. On the basis of fully considering the source correlation and complementary, it adopts the method of multi-source information fusion; by monitoring the pulse, heart rate, temperature of the human body, steering wheel grip strength to realized the fatigue level. The system of graphical interface adopts UCGUI. Finally, testing the main function modules of early warning system, the feasibility of the proposed early warning system is verified fusion .