Driving Detection Method Research Articles

Fatigue Driving Detection Methods based on Human-computer Interaction

Fatigue Driving Detection Methods based on Human-computer Interaction

Driver fatigue detection method based on temporal–spatial adaptive networks and adaptive temporal fusion module

The reduction of traffic accidents by determining the driver’s state through fatigue detection is a worthy research issue. Most of the current fatigue driving detection method fails to fully utilize the temporal features of fatigue. To address this problem, this paper proposes a driver fatigue detection method combined with temporal–spatial adaptive networks (TSNet) and adaptive temporal fusion module (ATFM). First, a frame sequence of T frames is obtained by strided sampling of the input video and data enhancement. Subsequently, the temporal–spatial adaptive module (TSAM) is used as the core module and incorporated into Efficientnet-v2 to construct TSNet, which adaptively extracts temporal features according to different videos, adds attention weights to discriminative spatial and channel features, and fully extracts fatiguing temporal–spatial features of videos. Finally, ATFM is utilized to learn the weights between the fatigue classification scores of each frame in the frame sequence and adaptively fuses the fatigue classification scores of individual frames to obtain fatigue prediction results, increasing the extent of the influence of keyframes on the fatigue prediction results. In this paper, the proposed method achieves an accuracy of 89.42% on the NTHU-DDD dataset, which is better than other state-of-the-art methods, and the number of parameters of the proposed method is 24.70M, which is smaller than most of the methods. Through a series of comparative experiments, TSNet and ATFM alone also outperform models and modules with similar functionality.

A novel fatigue driving detection method based on whale optimization and Attention-enhanced GRU

Fatigue driving has emerged as the predominant causative factor for road traffic safety accidents. The fatigue driving detection method, derived from laboratory simulation data, faces challenges related to imbalanced data distribution and limited recognition accuracy in practical scenarios. In this study, we introduce a novel approach utilizing a gated recurrent neural network method, employing whale optimization algorithm for fatigue driving identification. Additionally, we incorporate an attention mechanism to enhance identification accuracy. Initially, this study focuses on the driver's operational behavior under authentic vehicular conditions. Subsequently, it employs wavelet energy entropy, scale entropy, and singular entropy analysis to extract the fatigue-related features from the driver's operational behavior. Subsequently, this study adopts the cross-validation recursive feature elimination method to derive the optimal fatigue feature index about operational behavior. To effectively capture long-range dependence relationships, this study employs the gated recurrent unit neural network method. Lastly, an attention mechanism is incorporated in this study to concentrate on pivotal features within the data sequence of driving behavior. It assigns greater weight to crucial information, mitigating information loss caused by the extended temporal sequence. Experimental results obtained from real vehicle data demonstrate that the proposed method achieves an accuracy of 89.84% in third-level fatigue driving detection, with an omission rate of 10.99%. These findings affirm the feasibility of the approach presented in this study.

A lightweight fatigue driving detection method based on facial features

A lightweight fatigue driving detection method based on facial features

An embedded device-oriented fatigue driving detection method based on a YOLOv5s

An embedded device-oriented fatigue driving detection method based on a YOLOv5s

Fatigue driving detection method based on Time-Space-Frequency features of multimodal signals

Fatigue detection for drivers in public transportation is crucial. To effectively detect the driver's fatigue state, we investigated the deep learning-based fatigue detection method and proposed a multimodality signal fatigue detection method. In the proposed method, the convolutional autoencoder (CAE) is used to fuse electroencephalogram (EEG) and electrooculography (EOG) signal features, and the convolutional neural network (CNN) is used to maintain spatial locality. After that, the fusion features are input into the recurrent neural network (RNN) for fatigue recognition. We tested the proposed algorithmic framework on the SEED-VIG dataset and evaluated it using two statistical indicators, root mean square error (RMSE) and correlation coefficient (COR), achieving the mean RMSE/COR of 0.10/0.93 and 0.11/0.88 on single modality EOG and EEG features, respectively, and improved performance to 0.08/0.96 on multimodality features. In addition, this paper analyzes the effect of different signal features on recognition results, and the comparison illustrates that the performance of the model using multimodality features is better than that of signle modality features. The experimental results show that the algorithm framework proposed in this paper outperforms other recognition algorithms, which also proves the effectiveness of the algorithm applied to fatigue driving detection.

Driving Detection Based on the Multifeature Fusion

In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple facial features of drivers. On the basis of blink, yawn and nod judgment, multiple characteristic values of blink frequency, yawn frequency, and nod frequency of drivers were extracted to establish a fatigue detection sample database, and a naive Bayes classifier was constructed to judge fatigue. When the driver appears fatigue driving, warning information is given in time in order to prevent traffic accidents. The experimental results show that two sample videos were selected for testing. The accuracy rate of video sample 1 and video sample 2 was 94.74% and 95.00%, respectively. Conclusion. In the actual driving environment video test results, the discriminant average accuracy of a driver fatigue state reaches 94.87%, which has a good performance.

Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect.

Fatigue driving has always received a lot of attention, but few studies have focused on the fact that human fatigue is a cumulative process over time, and there are no models available to reflect this phenomenon. Furthermore, the problem of incorrect detection due to facial expression is still not well addressed. In this article, a model based on BP neural network and time cumulative effect was proposed to solve these problems. Experimental data were used to carry out this work and validate the proposed method. Firstly, the Adaboost algorithm was applied to detect faces, and the Kalman filter algorithm was used to trace the face movement. Then, a cascade regression tree-based method was used to detect the 68 facial landmarks and an improved method combining key points and image processing was adopted to calculate the eye aspect ratio (EAR). After that, a BP neural network model was developed and trained by selecting three characteristics: the longest period of continuous eye closure, number of yawns, and percentage of eye closure time (PERCLOS), and then the detection results without and with facial expressions were discussed and analyzed. Finally, by introducing the Sigmoid function, a fatigue detection model considering the time accumulation effect was established, and the drivers’ fatigue state was identified segment by segment through the recorded video. Compared with the traditional BP neural network model, the detection accuracies of the proposed model without and with facial expressions increased by 3.3% and 8.4%, respectively. The number of incorrect detections in the awake state also decreased obviously. The experimental results show that the proposed model can effectively filter out incorrect detections caused by facial expressions and truly reflect that driver fatigue is a time accumulating process.

Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism

Fatigue driving is one of the main causes of traffic accidents today. In this study, a fatigue driving detection system based on a 3D convolutional neural network combined with a channel attention mechanism (Squeeze-and-Excitation module) is proposed. The model obtains information of multiple channels of grayscale, gradient and optical flow from the input frame. The temporal and spatial information contained in the feature map is extracted by three-dimensional convolution, after which the feature map is fed to the attention mechanism module to optimize the feature weights. EAR and MAR are used as fatigue analysis criteria and, finally, a full binary tree SVM classifier is used to output the four driving states. In addition, this study uses the frame aggregation strategy to solve the frame loss problem well and designs application software to record the driver’s status in real time while protecting the driver’s facial privacy and security. Compared with other classical fatigue driving detection methods, this method extracts features from temporal and spatial dimensions and optimizes the feature weights using the attention mechanism module, which significantly improves the fatigue detection performance. The experimental results show that 95% discriminative accuracy is achieved on the FDF dataset, which can be effectively applied to driving fatigue detection.

Fatigue Driving Detection Method Based on Time-Space-Frequency Features of Multimodal Signals

Fatigue Driving Detection Method Based on Time-Space-Frequency Features of Multimodal Signals

Driver Fatigue Detection Based on Parallel Neural Network

Aiming at the problem that the existing fatigue driving detection methods cannot make full use of eye features and semantic information, a driver eye fatigue detection method based on parallel neural network is proposed. This method detects human faces through a multi-task cascaded convolutional network, determines the driver’s eye area according to the face ratio relationship, uses the parallel structure of the convolutional neural network and the residual network to recognize the eye state opening and closing, and according to PERCLOS the criterion is to judge the fatigue state. The experimental results show that the method has a high accuracy rate and can effectively detect the fatigue driving state.

Real-driving-implementable drowsy driving detection method using heart rate variability based on long short-term memory and autoencoder

Abstract Drowsy driving is a fatal problem that may cause traffic accidents. Although many driver drowsiness detection methods have been proposed, most of them have problems in input data availability, robustness to real driving environments, or detection precision. A drowsiness detection method based on heart rate variability (HRV), which is an R-R interval (RRI) fluctuation obtained from an electrocardiogram (ECG), has been proposed since ECG is easy to measure by using a wearable sensor. HRV is related to the autonomic nervous system (ANS) and is affected by drowsiness. However, its drowsiness detection performance was not always satisfactory. This study proposes a new drowsiness detection method using raw RRI data instead of HRV to improve the drowsiness detection performance. The proposed method uses raw RRI time series as inputs, and a drowsiness detection model is trained based on long short-term memory (LSTM) and autoencoder (AE), which are types of neural networks. RRI data during driving were collected from 25 participants using a driving simulator. The drowsiness detection model was trained following the proposed method. The experimental result showed that the proposed method achieved an AUC of 0.88, a sensitivity of 81%, and a specificity of 91%, which was higher than the HRV-based method. The result suggests that it is better to use raw RRIs as inputs than HRV features.

A Fatigue Driving Detection Method based on Deep Learning and Image Processing

Driving fatigue is one of the important causes of traffic accidents. It is of great significance to study fatigue driving detection algorithms to improve human life and property safety. This paper proposes a fatigue driving detection method based on deep learning and image processing. First, the driver’s face image is obtained in real time through the camera and the face image is detected using the MTCNN model. Next the image processing is performed on the face image, including three steps: grayscale processing, binarization processing, and human eye detection. Then we check the legitimacy of the human eye image and calculate the eye closure rate, and finally use the PERCLOS principle to analyze the fatigue state of the driver. The experimental results show that the proposed method has high detection rate and low false alarm rate, and has strong practicality.

Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment

Abstract Driving fatigue is often an important cause of traffic accidents. Monitoring psychological parameters of driver to detect fatigue state is an effective approach to prevent traffic accident. In order to study driving fatigue state in low-voltage and hypoxia plateau environment, the research involved the aspects of subjective monitoring and objective monitoring by driver's real-time electroencephalogram (EEG) signal which was obtained by field driving fatigue test. Nonlinear and linear methods were used to analyze EEG signal in awake, critical, and fatigue three typical states. The EEGLAB in the MATLAB toolbox was used in nonlinear method to analyze the power spectral density map of θ, α, β wave in three typical states. The new EEG eigenvalues were collected, and the EEG power characteristic values were calculated to evaluate the trend of EEG signal in linear method. Combined nonlinear and linear methods with subjective data analysis, the energy characteristic of (α + θ)/β and (α + β)/θ were recommended as the indicator to evaluate driving fatigue characteristics in low-voltage and hypoxia plateau environment. This study provided foundation of theory and examination for the design of driving fatigue warning device in low-voltage and hypoxia plateau environment, which is of great significance for the development of driver fatigue detection system.

Fatigue driving detection method for low-voltage and hypoxia plateau area: A physiological characteristic analysis approach

Fatigue driving is one of the main reasons for fatal crashes and highway accidents for drivers. Driving fatigue detection method based on monitoring psychological indicators is an effective method to avoid traffic accidents. The driver’s heart rate (HR) and blood oxygen saturation (SpO2) data were obtained in low-voltage and hypoxia plateau area by driving fatigue test, that is, field driving fatigue test and drug intervention fatigue driving test. SPSS software was used to process the analysis data and combined with the driver’s subjective response, which can figure out the correlation between parameters and driving fatigue. This paper figures out that HR can be used as an effective indicator to determine the degree of driver fatigue, and SpO2 can only be used as an auxiliary indicator. The time spent driving to fatigue increased after taking the intervention drug, indicating that the intervention drug has the ability to resist driving fatigue and delay fatigue in low-voltage and hypoxia plateau environment. This study provides theoretical basis and new research fields for further research on low-voltage and hypoxia plateau areas to alleviate driving fatigue, ensure driving safety and design of fatigue warning devices.

A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

Abstract Fatigue-related traffic accidents have a higher mortality rate and cause more significant damage to the environment. To ensure driving safety, a real-time driver fatigue detection method based on convolutional neural network (CNN) is proposed in this paper. The proposed fatigue driving detection method is cascaded by two CNN-based stages, including a detecting phase and classifying phase. The Location Detection Network is designed to extract facial features and localize the driver’s eyes and mouth regions. Then the State Recognition Network is training to recognize the driver’s eyes and mouth status. Simulations show that the proposed method has good effect of real time process and high accuracy of detection. Experiments conducted on Raspberry Pi 4 embedded system indicate that the proposed method has a good performance in the real driving environment.

Situation awareness method using spectral analysis of random matrix for integrated energy system

Situation awareness is essential to ensure operation of integrated energy systems consisting of the electricity, gas and heat systems. However, the multi-energy flow characteristics of system result in strong coupling relationships among different subsystems including different detection variables, which bring new challenges to situation awareness. To address this issue, a data driven detection method based on spectral analysis of random matrix is proposed in this paper. Firstly, a detection matrix model, which combines different types of variables, is established to fully reflect the interdependencies among subsystems, both internal and external. Furthermore, a novel detection method, which analyzes the degree of the spectral deviation of presented model, is presented to accomplish situation awareness. The proposed method can effectively handle the problem of power-gas-heat coupling, multi-variable modeling and rapid situation judging without requiring complicated numerical model. With this effort, not only the changed time but also the position of changed node could be obtained simultaneously through only spectral computation. Finally, simulation results are presented to illustrate the effectiveness of the proposed detection method.

A Review on Fatigue Driving Detection

The socialization of automobile development has brought great convenience to people’s travel. However, the rapid increase in the number of vehicles has also caused a series of problems. The increase in traffic accidents has brought great social casualties and economic losses. Fatigue driving, which is an important factor in the traffic accident, has aroused people’s attention. This paper reviews all kinds of fatigue driving detection methods at present; compares various fatigue driving detection methods in terms of accuracy, real-time and cost; analyses the advantages and disadvantages of various methods; introduces the application of fatigue detection system in automobile; summarizes the current deficiencies and future development trends in the field of fatigue driving detection. The future research of this field will be more to the data fusion, computer vision and deep learning.

Knowledge driven change detection method for aircraft targets

Knowledge driven change detection method for aircraft targets

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.