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.

Power Spectral Analysis of Heart Rate Variability of Driver Fatigue

This investigation was to evaluate the driving fatigue based on power spectral analysis of heart rate variability (HRV) under vertical vibration. Forty healthy male subjects (29.7±3.5 years) were randomly divided into two groups, Group A (28.8±4.3 years) and Group B (30.6±2.7 years). Group A (experiment group) was required to perform the simulated driving and Group B (control group) kept calm for 90 min. The frequency domain indices of HRV such as low frequency (0.04 0.15 Hz, LF), high frequency (0.150.4 Hz, HF), LF/HF together with the indices of hemodynamics such as blood pressure (BP) and heart rate (HR) of the subjects between both groups were calculated and analyzed after the simulated driving. There were significances of the former indices between both groups (P<0.05). All the data collected after experiment of Group A was observed the remarkable linear correlation (P<0.05) and parameters and errors of their linear regression equation were stated (α=0.05, P<0.001) in this paper, respectively. The present study investigated that sympathetic activity of the subjects enhanced after the simulated driving while parasympathetic activities decreased. The sympathovagal balance was also improved. As autonomic function indictors of HRV reflected fatigue level, quantitative evaluation of driving mental fatigue from physiological reaction could be possible.

Color space lip segmentation for drivers＇ fatigue detection

to the chroma distribution diversity （CDD） between lip color and skin color, the lip color area is segmented by the back propagation neural network （BPNN） with three typical color features. Isolated noisy points of the lip color area in binary image are eliminated by a proposed re- gion connecting algorithm. An improved integral projection algorithm is presented to locate the lip boundary. Whether a driver is fatigued is recognized by the ratio of the frame number of the images with mouth opening continuously to the total image frame number in every 20s. The experiments show that the proposed algorithm provides higher correct rate and reliability for fatigue driving detec- tion, and is superior to the single color feature-based method in the lip color segmention. Besides, it improves obviously the accuracy and speed of the lip boundary location compared with the traditional integral projection algrothm.

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.

Driving fatigue fusion detection based on T-S fuzzy neural network evolved by subtractive clustering and particle swarm optimization

In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver＇s facial features reflecting fatigue and one indirect vehicle behavior feature indicating fatigue are considered. Meanwhile, T-S fuzzy neural network（TSFNN）is adopted to recognize the driving fatigue of drivers. For the structure identification of the TSFNN, subtractive clustering（SC） is used to confirm the fuzzy rules and their correlative parameters. Moreover, the particle swarm optimization （PSO）algorithm is improved to train the TSFNN. Simulation results and experiments on vehicles show that the proposed algorithm can effectively improve the convergence speed and the recognition accuracy of the TSFNN, as well as enhance the correct rate of driving fatigue detection.

Visual fatigue relief zone in an extra-long tunnel using virtual reality with wearable EEG-based devices

Long-time driving and monotonous visual environment increase the safety risk of driving in an extra-long tunnel. Driving fatigue can be effectively relieved by setting the visual fatigue relief zone in the tunnel. However, the setting form of visual fatigue relief zone, such as its length and location, is difficult to be designed and quantified. By integrating virtual reality(VR) apparatus with wearable electroencephalogram(EEG)-based devices, a hybrid method was proposed in this study to assist analyzers to formulate the layout of visual fatigue relief zone in the extra-long tunnel.The virtual environment of this study was based on an 11.5 km extra-long tunnel located in Yunnan Province in China.The results indicated that the use of natural landscape decoration inside the tunnel could improve driving fatigue with the growth rate of attention of the driver increased by more than 20%. The accumulation of driving fatigue had a negative effect on the fatigue relief. The results demonstrated that the optimal location of the fatigue relief zone was at the place where driving fatigue had just occurred rather than at the place where a certain amount of driving fatigue had accumulated.

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.

Reducing the Effects of Driving Fatigue with Magnetic Stimulation

The purpose of this study was to assess the effects of reducing driving fatigue with magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points using heart rate (HR), reaction time (RT) testing, critical flicker fusion frequency (CFF) and subjective evaluation. Twenty healthy subjects were randomly divided into two groups: A-group (study group) and B-group (control group). All subjects were required to be well rested before the experiment. The subjects were engaged in high speed driving at a constant vehicle velocity of 80 km/h continuously for three hours on a test course simulating an expressway. During the driving magnitopunctures were applied to the Dazhui (DU14) point and Neiguan (PC6) points for the A-group when the subject performed the task for two and half hours, and for the B-group magnitopunctures were applied to non-acupuncture points at the same time session. In this study RT exbited a significant delay in B-group (P<0.01) but no found in A-group after the driving task. CFF and subjective evaluation also exhibited significant differences between the two groups after the driving task (P<0.05). The findings showed that magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points could reduce the effects of driving fatigue.

Spectral Analysis of Blood Pressure Variability as a Quantitative Indicator of Driving Fatigue

The quantitative detector of driver fatigue presents appropriate warnings and helps to prevent traffic accidents.The aim of this study was to quantifiably evaluate driver mental fatigue using the power spectral analysis of the blood pressure variability (BPV) and subjective evaluation. In this experiment twenty healthy male subjects were required to perform a driving simulator task for 3-hours. The physiological variables for evaluating driver mental fatigue were spectral values of blood pressure variability (BPV)including very low frequency (VLF), low frequency (LF),high frequency (HF). As a result, LF, HF and LF/HF showed high correlations with driver mental fatigue but not found in VLF. The findings represent a possible utility of BPV spectral analysis in quantitatively evaluating driver mental fatigue.

Experimental study of fatigue degree quantification for multi-feature fusion identification

A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the reaction and operation abilities of drivers about traffic signals.By comparison experiment with that EEG signal based,multivariate statistical analysis and fusion identification based on BP neural network(BPNN) results show that the experimental procedure is simple and practical,and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory,and also can achieve accurate and reliable quantification of fatigue degree,especially under the associated action of multiple fatigue feature parameters.

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.

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 .

A systematic review of the effect of various interventions on reducing fatigue and sleepiness while driving

Purpose： To identify and appraise the published studies assessing interventions accounting for reducing fatigue and sleepiness while driving. Methods： This systematic review searched the following electronic databases： Medline, Science direct, Scopus, EMBASE, PsyclNFO, Transport Database, Cochrane, BIOSIS, ISI Web of Knowledge, specialist road injuries journals and the Australian Transport and Road Index database. Additional searches included websites of relevant organizations, reference lists of included studies, and issues of major injury journals published within the past 15 years. Studies were included if they investigated interventions/exposures accounting for reducing fatigue and sleepiness as the outcome, measured any potential interventions for mitigation of sleepiness and were written in English. Meta-analysis was not attempted because of the heterogeneity of the included studies. Results： Of 63 studies identified, 18 met the inclusion criteria. Based on results of our review, many interventions in the world have been used to reduce drowsiness while driving such as behavioral （talking to passengers, face washing, listening to the radio, no alcohol use, limiting the driving behavior at the time of 12 p.m. - 6 a.m. etc）, educational interventions and also changes in the environment （such as rumble strips, chevrons, variable message signs, etc）. Meta-analysis on the effect of all these in- terventions was impossible due to the high heterogeneity in methodology, effect size and interventions reported in the assessed studies. Conclusion： Results of present review showed various interventions in different parts of the world have been used to decrease drowsy driving. Although these interventions can be used in countries with high incidence of road traffic accidents, precise effect of each intervention is still unknown. Further studies are required for comparison of the efficiency of each intervention and localization of each intervention ac- cording to the traffic pattems of each country.

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.