RESUMEN

Drivers are more likely to feel fatigue when driving on the desert highway due to its single line, monotonous road side landscape, and small traffic volume, etc., so the method of setting fatigue warning signs on desert highway should be studied for. In this paper, field investigation and field test are carried out on the section of Uma Expressway crossing Tengger Desert, and the same road scene and line shape as the actual one are constructed by using uc-win road. Driving simulation experiments were carried out by using driving simulation cabin, collecting heart and muscle electric indexes and analyzing fatigue characteristic law. On this basis, simulation experiments of different fatigue warning signs and stimulation intervals were carried out in multiple groups, and the pattern of cardiac and electromyographic indexes and the interaction law were analyzed based on the artifact correction method after denoising the measured data, so that the optimal setting method was obtained according to the stimulation effect. Based on the artifact correction method, the measured data were de-noised and analyzed the changes of ECG and EMG indexes and the interaction law, and the best setting method was obtained according to the stimulation effect. Finally, the fatigue level was classified based on the cohesive hierarchical system cluster analysis method to verify the rationality of the fatigue warning sign setting scheme. The results show that the driver's psychological fatigue and physiological fatigue both show obvious fluctuation growth, and the growth trend exists in four stages, namely, smooth fluctuation (0-30min), initial fatigue (35-85min), adjustment stage (85-160min), and severe fatigue (after 160min), and psychological fatigue is earlier than physiological fatigue, and the driver's regulation effect on physiological fatigue is better than psychological fatigue. Analysis of the psychological indexes of the four groups of traffic signs shows that the fatigue warning signs with black characters on a white background and set at an interval of 60-80 km have the most obvious effect on driver stimulation in a desert highway with a design speed of 80-100 km/h.

RESUMEN

Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.

Asunto(s)

RESUMEN

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

RESUMEN

The study of the relationship between Daylight Saving Time (DST) and road safety has yielded contrasting results, most likely in relation to the inability of crash-database approaches to unravel positive (ambient lighting-related) and negative (circadian/sleep-related) effects, and to significant geographical differences in lighting-related effects. The aim of this study was to investigate the effects of DST on driving fatigue, as measured by driving-based, physiological and subjective indicators obtained from a driving simulator experiment. Thirty-seven participants (73 % males, 23 ± 2 years) completed a series of 50-min trials in a monotonous highway environment: Trial 1 was in the week prior to the Spring DST transition, Trial 2 in the following week, and Trial 3 in the fourth week after the transition. Thirteen participants returned for Trial 4, in the week prior to the Autumn switch to civil time, and Trial 5 in the following week. Significant adverse effects of DST on vehicle lateral control and eyelid closure were documented in Trial 2 and Trial 3 compared to Trial 1, with no statistical differences between Trials 2 and 3. Further worsening in vehicle lateral control was documented in Trials 4 and 5. Eyelid closure worsened up to Trial 4, and improved in Trial 5. Participants were unaware of their worsening performance based on subjective indicators. In conclusion, DST has a detrimental impact on driving fatigue during the whole time during which it is in place. Such an impact is comparable, for example, to that associated with driving with a blood alcohol concentration of 0.5 g/L.

RESUMEN

Among the causes of the annually traffic accidents, driving fatigue is the main culprit. In consequence, it is of great practical significance to carry out the research of driving fatigue detection and early warning system. However, there are still two problems in the latest methods of driving fatigue detection: one is that a single information cannot precisely reflect the actual state of the driver in different fatigue phases, another one is the detection effect is not very well or even difficult to detect under abnormal illumination. In this paper, the multi-task cascaded convolutional networks (MTCNN) and infrared-based remote photo-plethysmography (rPPG) theory are used to extract the driver's facial and physiological information, and the multi-modal specific fatigue information is deeply excavated, and the multi-modal feature fusion model is constructed to comprehensively analyze the driver's fatigue variation tendency. Aiming at the matter of low detection accuracy under abnormal illumination, the multi-modal features extracted from visible light images and infrared images are fused by multi-loss reconstruction (MLR) module, and the driving fatigue detection module is established which is based on Bi-LSTM model by utilizing fatigue timing. The experiments were validated under all-weather illumination scenarios and were carried out on the datasets NTHU-DDD, UTA-RLDDD and FAHD. The results show that the multi-modal driving fatigue detection model has better performance than the single-modal model, and the accuracy is improved by 8.1%. In the abnormal illumination such as strong and weak light, the accuracy of the method can reach 91.7% at the highest and 83.6% at the lowest. Meanwhile, in the normal illumination, it can reach 93.2%.

RESUMEN

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

Asunto(s)

RESUMEN

Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.

RESUMEN

INTRODUCTION: Loss of attention leads to less steady driving within the lane and is one of the main causes of road accidents. To improve road safety, vehicle-based parameters such as steering wheel angle and lateral position are used to objectively assess driving performance, especially in monotonous driving tasks. METHOD: The present driving simulator study investigated the extent to which eight commonly used parameters are independent indicators of driving performance. Fifteen participants undertook a monotonous highway driving task for 1 h. Four steering angle parameters were examined: average steering angle (ASA), standard deviation of steering angle (SDSA), steering angle range (SAR), and steering reversal rate (SRR); as well as four lateral position parameters: mean lateral position (MLP), standard deviation of lateral position (SDLP), lateral position range (LPR), and the out-of-lane duration. Measurements were averaged across 2-minute epochs. Repeated measures correlation analysis evaluated the similarity between each parameter, and the variance inflation factor test evaluated the multicollinearity of all the parameters. RESULTS: The results demonstrated that some parameters are highly correlated and should not be used together to assess driving performance. It is recommended that the optimal combination is ASA and SAR to assess steering angle, and SDLP and out-of-lane to assess lateral position. Out-of-lane, as a factor directly contributing to road safety, is recommended because it has the least correlation with other parameters. PRACTICAL APPLICATIONS: If implemented, these recommendations may improve the assessment of driving performance in future studies.

Asunto(s)

RESUMEN

Driving fatigue is very likely to cause traffic accidents, seriously threatening the lives and properties of drivers. Therefore, accurate detection and effective mitigation of driving fatigue are crucial for ensuring the personal safety of drivers. This study proposes a method to relieve driving fatigue by properly reducing the temperature to stimulate the human sympathetic nerve. The method uses the intelligent cooling and blowing device on the car seat cushion to achieve cold stimulation of the sympathetic nerve of the driver by reducing the temperature of the driver's hip, back and neck, so as to increase the excitement of the sympathetic nerve, keep the driver alert and achieve the purpose of fighting driving fatigue. In view of the fact that the traditional fatigue detection method is easily affected by environmental factors and individual differences, this study uses the order recurrence plot (ORP) method to detect driving fatigue based on electroencephalogram (EEG) signals. The results show that ORP textures drawn by EEG signals of the two driving conditions (normal driving condition and sensory cold stimulation driving condition) are significantly different, and the quantization parameters determinism (DET) and average diagonal line length (DLL) values are significantly different. Cold stimulation of the subjects' hips, back and neck to alleviate driving fatigue was the best when the temperature was 21 °C. In addition, compared with the traditional methods of fatigue relief, the sensory cold stimulation method proposed in this study does not easily to produce tolerance and has no damage to the body.

RESUMEN

Electroencephalograph (EEG) has been a reliable data source for building brain-computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG samples to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.

RESUMEN

Fatigue-related traffic accidents and fatalities have been extensively studied by scholars globally.Specialized vehicles,due to their unique mission profiles,are more likely to cause driving-related fatigue and serious consequences.This paper reviews the current research of fatigue driving by using an inductive analysis method to summarize the mechanisms,risk factors,and monitoring methods.This paper also offers a vision of priorities and methodologies for research in the future.It is recommended that the mechanisms of driving fatigue be explored at the molecular biological level and that fatigue monitoring systems be made more feasible via the combined application of non-intrusive monitoring in order to reduce the toll on life and property taken by driving fatigue.

RESUMEN

Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.

Asunto(s)

RESUMEN

Introduction: Establishing a driving fatigue monitoring system is of utmost importance as severe fatigue may lead to unimaginable consequences. Fatigue detection methods based on physiological information have the advantages of reliable and accurate. Among various physiological signals, EEG signals are considered to be the most direct and promising ones. However, most traditional methods overlook the functional connectivity of the brain and fail to meet real-time requirements. Methods: To this end, we propose a novel detection model called Attention-Based Multi-Semantic Dynamical Graph Convolutional Network (AMD-GCN). AMD-GCN consists of a channel attention mechanism based on average pooling and max pooling (AM-CAM), a multi-semantic dynamical graph convolution (MD-GC), and a spatial attention mechanism based on average pooling and max pooling (AM-SAM). AM-CAM allocates weights to the input features, helping the model focus on the important information relevant to fatigue detection. MD-GC can construct intrinsic topological graphs under multi-semantic patterns, allowing GCN to better capture the dependency between physically connected or non-physically connected nodes. AM-SAM can remove redundant spatial node information from the output of MD-GC, thereby reducing interference in fatigue detection. Moreover, we concatenate the DE features extracted from 5 frequency bands and 25 frequency bands as the input of AMD-GCN. Results: Finally, we conduct experiments on the public dataset SEED-VIG, and the accuracy of AMD-GCN model reached 89.94%, surpassing existing algorithms. Discussion: The findings indicate that our proposed strategy performs more effectively for EEG-based driving fatigue detection.

RESUMEN

Fatigue and sleepiness are complex bodily states associated with monotony as well as physical and cognitive impairment, accidents, injury, and illness. Moreover, these states are often characteristic of professional driving. However, most existing work has focused on motor vehicle drivers, and research examining train drivers remains limited. As such, the present study psychophysiologically examined monotonous driving, fatigue, and sleepiness in a group of passenger train drivers and a group of non-professional drivers. Sixty-three train drivers and thirty non-professional drivers participated in the present study, which captured 32-lead electroencephalogram (EEG) data during a monotonous driving task. Fatigue and sleepiness were self-evaluated using the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Karolinksa Sleepiness Scale, and the Checklist of Individual Strength. Unexpectedly, fatigue and sleepiness scores did not significantly differ between the groups; however, train drivers generally scored lower than non-professional drivers, which may be indicative of individual and/or industry attempts to reduce fatigue. Across both groups, fatigue and sleepiness scores were negatively correlated with theta, alpha, and beta EEG variables clustered towards the fronto-central and temporal regions. Broadly, these associations may reflect a monotony-associated blunting of neural activity that is associated with a self-reported fatigue state.

RESUMEN

Introduction: Driving fatigue has been shown to increase the risk of accidents and potentially fatal crashes. Fatigue is a serious risk that some drivers do not take seriously. Previous studies investigated the effects of driving fatigue in the Malaysian oil and gas transportation industry by employing survey questionnaires. However, they did not explain the behavior of fatigue. Besides, these results required validation by a more reliable method that can describe how fatigue occurs. Methods: Thus, in this study, we used the Psychomotor Vigilance Test (PVT-192) and a short survey to address driving fatigue behavior and identify the influences of driving fatigue on driving performance in real life (on the road) with actual oil and gas tanker drivers. The total participants in the experimental study were 58 drivers. Results: For the analysis, a Wilcoxon Signed Ranks Test, Z value and Spearman's rho were used to measure the significant difference between the pre and post-tests of PVT and the correlation between the fatigue variables and driving performance. Discussion: During the experiment's first and second days, this study's results indicated that driving fatigue gradually escalated. Likewise, there was a negative correlation based on the test of the relationship between the PVT data and the driving performance survey data. Additionally, the drivers suffer from accumulative fatigue, which requires more effort from the transportation company management to promote the drivers awareness of fatigue consequences.

Asunto(s)

RESUMEN

A large number of traffic accidents were caused by drowsiness while driving. In-vehicle alert system based on physiological signals was one of the most promising solutions to monitor driving fatigue. However, different physiological modalities can be used, and many relative studies compared different modalities without considering the implementation feasibility of portable or wearable devices. Moreover, evaluations of each modality in previous studies were based on inconsistent choices of fatigue label and signal features, making it hard to compare the results of different studies. Therefore, the modality comparison and fusion for continuous drowsiness estimation while driving was still unclear. This work sought to comprehensively compare widely-used physiological modalities, including forehead electroencephalogram (EEG), electrooculogram (EOG), R-R intervals (RRI) and breath, in a hardware setting feasible for portable or wearable devices to monitor driving fatigue. Moreover, a more general conclusion on modality comparison and fusion was reached based on the regression of features or their combinations and the awake-to-drowsy transition. Finally, the feature subset of fused modalities was produced by feature selection method, to select the optimal feature combination and reduce computation consumption. Considering practical feasibility, the most effective combination with the highest correlation coefficient was using forehead EEG or EOG, along with RRI and RRI-derived breath. If more comfort and convenience was required, the combination of RRI and RRI-derived breath was also promising.

Asunto(s)

RESUMEN

BACKGROUND: Driving fatigue is one of the main factors leading to traffic accidents. So, it is necessary to detect driver fatigue accurately and quickly. NEW METHOD: To precisely detect driving fatigue in a real driving environment, this paper adopts a classification method for driving fatigue based on the wavelet scattering network (WSN). Firstly, electroencephalogram (EEG) signals of 12 subjects in the real driving environment are collected and categorized into two states: fatigue and awake. Secondly, the WSN algorithm extracts wavelet scattering coefficients of EEG signals, and these coefficients are used as input in support vector machine (SVM) as feature vectors for classification. RESULTS: The results showed that the average classification accuracy of 12 subjects reached 99.33%; the average precision rate reached 99.28%; the average recall rate reached 98.27%; the average F1 score reached 98.74%; and the average classification accuracy of the public data set SEED-VIG reached 99.39%. The average precision, recall rate and F1 score reached 99.27%, 98.41% and 98.83% respectively. COMPARISON WITH EXISTING METHODS: In addition, the WSN algorithm is compared with traditional convolutional neural network (CNN), Sparse-deep belief networks (SDBN), Spatio-temporal convolutional neural networks (STCNN), Long short-term memory (LSTM), and other methods, and it is found that WSN has higher classification accuracy. CONCLUSION: Furthermore, this method has good versatility, providing excellent recognition effect on small sample data sets, and fast running time, making it convenient for real-time online monitoring of driver fatigue. Therefore, the WSN algorithm is promising in efficiently detecting driving fatigue state of drivers in real environments, contributing to improved traffic safety.

Asunto(s)

RESUMEN

The cold and hypoxic environment at high altitudes can easily lead to driving fatigue. For improving highway safety in high-altitude areas, a driver fatigue test is conducted using the Kangtai PM-60A car heart rate and oxygen tester to collect drivers' heart rate oximetry in National Highway 214 in Qinghai Province. Standard deviation (SDNN), mean (M), coefficient of RR (two R heart rate waves), RR interval coefficient of variation (RRVC), and cumulative rate of driving fatigue based on the driver's heart rate RR interval are calculated using SPSS. This study aims to derive degree of driving fatigue (DFD) in high-altitude areas when driving from lower to higher altitude. The analysis shows that the DFD growth trend of different altitude ranges presents an S-shaped curve. The driving fatigue thresholds in the altitude range of 3000-3500, 3500-4000, 4000-4500, and 4500-5000 m are 2.86, 3.82, 4.54, and 10.2, which are significantly higher than that of ordinary roads in plain areas. The start times of severe fatigue in the four altitude ranges are 35, 34, 32, and 25 minutes. The start time of driving fatigue continued to advance with the increase of age, and the DFD continued to increase with the increase of age. Results provide an empirical basis for the design of the horizontal alignment index system and antifatigue strategies to improve highway safety in high-altitude areas.

Asunto(s)

RESUMEN

Driving fatigue detection based on EEG signals is a research hotspot in applying brain-computer interfaces. EEG signal is complex, unstable, and nonlinear. Most existing methods rarely analyze the data characteristics from multiple dimensions, so it takes work to analyze the data comprehensively. To analyze EEG signals more comprehensively, this paper evaluates a feature extraction strategy of EEG data based on differential entropy (DE). This method combines the characteristics of different frequency bands, extracts the frequency domain characteristics of EEG, and retains the spatial information between channels. This paper proposes a multi-feature fusion network (T-A-MFFNet) based on the time domain and attention network. The model is composed of a time domain network (TNet), channel attention network (CANet), spatial attention network (SANet), and multi-feature fusion network(MFFNet) based on a squeeze network. T-A-MFFNet aims to learn more valuable features from the input data to achieve good classification results. Specifically, the TNet network extracts high-level time series information from EEG data. CANet and SANet are used to fuse channel and spatial features. They use MFFNet to merge multi-dimensional features and realize classification. The validity of the model is verified on the SEED-VIG dataset. The experimental results show that the accuracy of the proposed method reaches 85.65 %, which is superior to the current popular model. The proposed method can learn more valuable information from EEG signals to improve the ability to identify fatigue status and promote the development of the research field of driving fatigue detection based on EEG signals.

Asunto(s)

RESUMEN

Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiological signals. Still, lighting and the environment affect the detection results based on facial expressions. In contrast, the electroencephalographic (EEG) signal is a physiological signal that directly responds to the human mental state, thus reducing the impact on the detection results. This paper proposes a log-Mel spectrogram and Convolution Recurrent Neural Network (CRNN) model based on EEG to implement driver fatigue detection. This structure allows the advantages of the different networks to be exploited to overcome the disadvantages of using them individually. The process is as follows: first, the original EEG signal is subjected to a one-dimensional convolution method to achieve a Short Time Fourier Transform (STFT) and passed through a Mel filter bank to obtain a logarithmic Mel spectrogram, and then the resulting logarithmic Mel spectrogram is fed into a fatigue detection model to complete the fatigue detection task for the EEG signals. The fatigue detection model consists of a 6-layer convolutional neural network (CNN), bi-directional recurrent neural networks (Bi-RNNs), and a classifier. In the modeling phase, spectrogram features are transported to the 6-layer CNN to automatically learn high-level features, thereby extracting temporal features in the bi-directional RNN to obtain spectrogram-temporal information. Finally, the alert or fatigue state is obtained by a classifier consisting of a fully connected layer, a ReLU activation function, and a softmax function. Experiments were conducted on publicly available datasets in this study. The results show that the method can accurately distinguish between alert and fatigue states with high stability. In addition, the performance of four existing methods was compared with the results of the proposed method, all of which showed that the proposed method could achieve the best results so far.