Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network

. The driver is one of the most important factors in road traﬃc. Monitoring the driver’s driving status can greatly improve the safety and road operation eﬃciency of urban road traﬃc in the case of multiple traﬃc modes. Fatigue has a signiﬁcant impact on drivers’ safety on the road, particularly while driving in a monotonous environment for a long time. In this study, the eye movement parameters of 36 drivers were collected through the simulation experiment of a driving simulator. The pupil area and percentage of eye closure (PERCLOS) in driving scenes of the expressway and low-grade rural road were combined with the Stanford Sleepiness Scale (SSS) to determine the threshold of fatigue degree in diﬀerent monotonous driving scenarios. A recognition model of diﬀerent fatigue degrees of drivers is built based on the deep learning method of a long short-term memory network (LSTM) to detect the varied fatigue degrees of drivers. The result shows that the fatigue degree of drivers increases as driving time increases on both expressways and low-grade rural roads. In the same driving time, the driver felt tired faster on the expressway, and the fatigue degree was signiﬁcantly higher than that on the country road. The recognition rate of the established fatigue degree recognition model for driver’s awake state, mild fatigue, moderate fatigue, and severe fatigue is 100%, 93.1%, 98.4%, and 100% respectively, and the total recognition rate can reach 97.8%, which is higher than the recognition accuracy of the traditional machine learning approach.


Introduction
e trend of traffic intelligence is becoming more and more visible, thanks to the rapid development of the vehicle industry and the ongoing construction and optimization of roads and road administration facilities.Despite the emergence in an endless stream of emerging technologies nowadays, their application to traditional traffic problems still needs to be addressed.e driver's behavioral state is a key aspect of the driving process.Monitoring the driver's status during the driving process will serve to improve the safety and efficiency of urban road traffic, as well as contribute to more coordinated regional traffic development in the case of numerous modes of transportation.Individuals, families, and the country lose a lot of money due to traffic accidents, and drowsy driving is one of the leading causes of traffic accidents and fatalities.According to statistics, roughly 600,000 people died in traffic accidents each year around the world, resulting in a direct economic loss of $12.5 billion.57% of these accidents were related to driving fatigue.e probability of traffic accidents caused by driving fatigue accounts for more than half of the total number of accidents, which is 4 to 6 times that of ordinary driving [1].According to a report by the National Highway Traffic Safety Administration (NHTSA), driver's drowsiness accounted for approximately 83,000 crashes, 37,000 injuries, and 900 deaths in the United States alone [2].At the same time, according to the American Automobile Association survey, it was found that 21% of traffic fatalities were caused by driver fatigue [3].
Drivers often show themselves in a variety of ways when they are tired, including drooping eyes, increased blinking frequency, lower focus, and so on.It is frequently accompanied by sleepiness, exhaustion, and other symptoms.As a result, when drivers are fatigued, their driving conduct poses a significant risk to traffic safety.e monotonous driving environment, long-time driving, and the driver's physical condition are the main causes of fatigue driving.e driver's fatigue state is a process that gradually accumulates with the increase of driving time.Most studies now focus on the driver's awake and fatigue states, with only a few studies categorizing the driver's exhaustion state.At the same time, the fatigue condition of the driver varies depending on the driving environment.e goal of driving fatigue research is to identify drivers' fatigue levels in different driving situations, define the typical index of a driver's fatigue level, and provide a reference for developing solutions for different fatigue states.
e recognition model of the driver's fatigue condition is constructed using the deep learning method of a long shortterm memory network.e data set for training and verifying the model is the eye movement data of drivers obtained in driving simulation tests.e aim of this study is to verify the effectiveness and superiority of the deep learning method in recognizing the driver's fatigue state.
In this study, we use a combination of subjective fatigue detection and objective fatigue detection methods through driving simulation experiments conducted on a driving simulator, which will explore the differences in driver fatigue indicators in different monotonous driving environments.
e effective classification of fatigue levels is important for fatigue warning in monotonous driving environments.Moreover, the deep learning algorithm LSTM is used to establish a driver's fatigue recognition model, which can identify the driver's fatigue state, warn the driver of dangerous driving behavior, and improve the driver's driving safety while driving.

Literature Review
e effect of the driving environment on driving fatigue has been explored in some researches.Pilcher and Huffcutt pointed out that a complex road environment and traffic conditions will make drivers feel tired more easily [4].Mao believed that the more monotonous the road environment, the more likely it was to cause driver fatigue [5].iffault and Bergeron's research showed that the driver moved the steering wheel at a greater angle many times in the monotonous driving environment, which indicated that the driver was more cautious in the monotonous driving environment [6].Dinges found that drivers in monotonous driving environments were less alert, especially their visual response to driving was slower [7].However, these studies have only considered the effects of monotonous and complex driving environments on driver fatigue in isolation.In real-world driving, different monotonous driving environments have different effects on driver fatigue.
A recommended maximum continuous driving time has been proposed as a way to require drivers to take breaks during the journey.Fatigued driving is a gradual behavior, which occurs when drivers are unconscious.e individual differences of fatigue characteristics among different drivers are large, the fatigue characteristic values of drivers with the same fatigue degree are also different, and the changing trend of drivers with the same fatigue characteristic is also different [8].us, it is necessary to reduce the influences caused by individual characteristics as much as possible.e previous research on fatigue driving mainly focused on how to find and identify fatigue.According to previous studies, the main methods to identify driving fatigue are as follows: (1) defining a predetermined driving duration threshold to identify fatigue driving.(2) fatigue driving is detected and recognized through many aspects of the driver or vehicle, such as physiological response, cognitive distraction, facial expression, vehicle condition, and so on.However, driver fatigue is a process that gradually accumulates over time and the fatigue characteristics of different fatigue states have different changing trends [9].Driving fatigue should be classified to study because fatigue has a characteristic of gradual behavior.Ahlstrom et al. divided the fatigue state of drivers into three levels: awake, fatigue, and severe fatigue based on the Karolinska Sleepiness Scale (KSS) [10].Larue et al. used the 5 minutes before driving as a reference standard and used the driver's alertness and the number of microsleeps as evaluation indicators to divide fatigue into 4 levels [11].Li et al. analyzed the steering wheel angle changes in three states: awake, fatigue, and extreme fatigue.ey found that drivers frequently corrected the steering wheel in small increments when they were awake.When they were fatigued, they corrected the steering wheel less frequently and with larger and faster movements.ey even showed no movement for a short time and then suddenly corrected the steering wheel significantly when they were extremely fatigued [12].Zhang [13] selected the EEG Shannon entropy and sample entropy, EMG approximate entropy, and ocular wavelet time-frequency analysis indicators based on neurobiology.en, he defined fatigue into normal, mild fatigue, mood swings, and extreme fatigue, respectively.e driver's driving state is divided into awake and fatigue states.
is two-level division of the fatigue state may be slight fatigue, but also may be serious fatigue.
e subsequent fatigue warning will either be too early or too late.Both have a great impact on driving safety.erefore, the key to driving fatigue research is to find a way to effectively segment the driver's fatigue level and achieve effective fatigue warning for the follow up.
At present, the detection methods of driving fatigue mainly include driver's physiological signals (such as driver's EEG and ECG), driver's physiological response characteristics (such as human eye movement and blink information), driver's operation behavior (such as steering wheel rotation angle), and vehicle state information (such as using the change of vehicle trajectory and lane departure) [14].e results obtained based on physiological signal detection are the most accurate and they can best reflect the driver's driving fatigue characteristics.A detection method based on the EEG signal was proposed by Wang et al. to judge driving fatigue in real-time by analyzing the driver's nervous system 2 Journal of Advanced Transportation [15].Due to the high detection cost and invasive detection, it has a great impact on the driver's normal driving behavior, which is not commonly used and requires improvement.At present, the detection method based on the driver's eye state is the most effective and convenient method to detect the fatigue of drivers.Because it is a noninvasive detection, it is more suitable for practical applications.e PERCLOS method is the most commonly used driving fatigue detection method based on the driver's eye state.e PERCLOS value adopts the ratio of eyelid closure time to a period is used as the fatigue detection index.e greater its value, the deeper the degree of driving fatigue.e pupil diameter of people tends to decrease regularly with the deepening of fatigue [16], so the pupil area can also be used as an index parameter for fatigue judgment.
ere are a lot of researches on the detection methods of driving fatigue.To improve the accuracy of fatigue detection, Xu et al. collected multisource data such as vehicle lateral position and steering wheel manipulation through driving simulation tests to calculate fatigue characteristic indexes.A decision tree model for fatigue level prediction was established by combining the driver's subjective fatigue level and conducting a comprehensive evaluation of the fatigue level through video playback.e model had a correct prediction rate of 64.3%, a relatively low accuracy rate, and low precision [17].Qu et al. used the evaluation method of a facial video expert and established a database of driver's awake, fatigue, and very fatigue states, respectively, through driving simulator driving simulation experiments.en, he selected the optimal combination of characteristic indicators by a specific algorithm after extracting the characteristic indicators of driver's fatigue operation characteristics to establish a 3-level fatigue monitoring model for drivers based on SVM [18].e accuracy obtained by this model can reach 87.7% when tested under simulator working conditions.Recently, driver fatigue evaluation using advanced deep learning techniques based on physiological responses has been reported by Gao et al. [19,20].Deep learning can greatly improve the accuracy of driver fatigue detection through its powerful information processing ability and strong robustness, which can greatly improve the ability of fatigue detection.

Experimental Method
e study used a driving simulator in investigating the thresholds of the fatigue level in different monotonous driving environments.
en, the fatigue level recognition model was built to identify different fatigue levels.e theoretical framework is presented in Figure 1.

Driving Simulator and Driving Scenes.
e test used the high simulation driving simulator as shown in Figure 2. e motion system of the driving simulator is 3 degrees of freedom.A Volkswagen Polo car with no engine is used in the driving simulator, which includes a steering wheel, braking force feedback, electrical sensor, and sound system.
e driving simulator's functions are identical to those of a real car to ensure it is the same as a real driving situation.e driving visual scene is mainly provided by a projection system composed of four projectors, with a visual range of 250 °. e effectiveness of the driving simulator passed the system test, demonstrating that the simulator's simulation degree can fulfill research demands.
To explore the impact of different monotonous environments on drivers' long-term fatigue driving, two types of roads are mainly set in the driving scenes.Table 1 and Figure 3 describe these two driving scenarios in detail.To restore the real driving scene as much as possible, green grass, trees, and a small number of village buildings are set on both sides of the road, and a small number of vehicles that do not affect normal driving are set on the road.

Subjects.
According to previous studies, the number of subjects in the experiments was in the range of 10-38 [21][22][23][24][25]. e number of subjects in this study is 36, which could meet the minimum sample size requirement.Before the experiment, the gender, age, nap habits, and driving age of the 36 subjects were recorded through a questionnaire survey.
e driving factor variables are summarized in Table 2. e survey results show that the subjects are mainly 22-28 years old and have been driving for 1-6 years.e subjects are mainly young and middle-aged experienced drivers, which can minimize the impact of driving experience on driving behavior.All subjects are required to hold a valid driver's license; be in good physical condition; have no drug-taking history within 1 month before the test; not drink alcohol within 24 hours before the test; and not drink coffee, strong tea, and functional drinks within 12 hours before the test [21].

Test Process.
ese 36 subjects were randomly assigned to two scenarios to simulate driving situations.Scenario#1 and scenario#2 were assigned 18 personnel equally, and the process of assigning personnel ensured that the driving experience and other factors of the subjects in both scenarios were kept as equal as possible.e drivers of both driving scenarios went through the same test process, filling out the basic driver information questionnaire before entering the driving simulator.
e experiment process is shown in Figure 4.
Because the drivers often feel tired in the afternoon and early morning.For experimental ease of implementation, we choose a time between 12:30 and 2:30 in the afternoon to do the test.e drivers have just finished lunch and need a midday break, which is more likely to cause sleepiness at this time.During the test, the driver is not allowed to undertake any secondary tasks, and there is no need to change lanes or switch the lights while driving.e test car uses an automatic transmission, so the driver does not need to change gears.e driver does not use mobile phones, radios, music players, or other equipment during the test.ere are a small number of other traffic vehicles on the road, but they do not block the lane where the driving vehicle is located.Moreover, the driver needs to wear an eye tracker to record the eye movement data during driving.e driving simulation experiment steps are as follows: (1) e driver familiarizes with the driving simulation system, understands relevant precautions and the purpose of the experiment, and fills in relevant information (age, driving age, nap habits, etc.).(2) e driver conducts a simulated driving test drive for 10 minutes, to enable the subject to reach a high level of fatigue within a limited time, and then conduct the formal experiment.
(3) e driver wears the eye tracking device to confirm the normal transmission and storage of eye-tracking data during driving.
(4) e driver's speed is kept at about 110 km/h in scenario#1 and 60 km/h in scenario#2, and the continuous 1 h simulated driving experiment was conducted.(5) After the start of the experiment, a subjective fatigue evaluation of the driver was conducted at 0 min, 30 min, and 60 min.Journal of Advanced Transportation (6) e experiment is carried out in sequence, and the data are saved after completion of all drivers.According to the drivers' subjective fatigue state, values were obtained before the experiment, 30 minutes in the experiment, and at the end of the experiment.

Experimental Result
e expressway and low-grade rural highway can be counted separately, the results are shown in Figure 5.
As shown in figure (a), all subjects remained awake at the beginning of the experiment in scenario#1.As the experiment progressed, the driver's fatigue and sleepiness increased.When the experiment lasted for 30 min, 89% of the subjects subjectively felt mild fatigue and 11% felt moderate fatigue.When the experiment lasted for 60 min, 83% of drivers feel that they are in a state of severe fatigue, while only 17% of drivers feel moderate fatigue.
As shown in figure (b), all subjects also remained awake at the beginning of the experiment in scenario#2.When the experiment lasted for 30 min, 17 drivers felt only mild fatigue, one driver felt moderate fatigue.When the experiment lasted for 60 min, half of the subjects felt moderate fatigue and the other half of the subjects felt severe fatigue.
When the fatigue states of drivers in the two scenarios are analyzed together, as shown in Figure 6,it can be seen from the figure that in the driving process of the expressway and the rural highway, the subjective fatigue state of drivers gradually deepens with the increase of driving time.At 30 minutes of the expressway scene experiment, the drivers' fatigue degree is slightly higher than that of the rural highway.At 60 minutes, the drivers' fatigue degree is much higher than that of the rural highway.As can be seen from the trend line of subjective fatigue degree in the figure, from the trend line of subjective fatigue in the graph, it can be seen that the trend line rises faster and has a greater linear slope for the highway than for the rural road. is indicates that drivers who drive on the highway for a long time are more likely to feel fatigued subjectively and the rate of fatigue deepening is faster.Subjective fatigue is higher for highway drivers for the same driving time.

Pupil Area.
As a noninvasive detection method, the detection of eye movement parameters can effectively and conveniently detect the driver's fatigue state.Generally, the pupil diameter of normal and fully rested people is about 2.5-4 mm. e pupil diameter can be an effective index to measure the driver's fatigue.With the increase of driving time, the driver's pupil diameter shows a regular shrinking trend [16].en, the driver's pupil data collected by the eye tracker can be collected at a frequency of 60 Hz, and the average pupil area of the driver can be calculated by D-Lab software.Take all drivers every 10 minutes as a driving time section in both scenarios, calculate the average value of the stable pupil area value, and get a total of 217 sample values in 60 minutes.According to the time series, all sample points form a pupil area data scatter diagram, which is represented in Figure 7.
It can be seen from the figure that the pupil area of the driver in both scenes shows a regular decreasing trend with the increase of driving time.e sample points are sorted according to the time series and 35 sample points are taken every 10 minutes, it can be seen that the reduction trend of the driver's pupil area in the two scenes is different.When the experiment is conducted for 25 minutes in scenario#1, there is a point with an apparent decreasing trend, indicating that there is an obvious change in the driver's fatigue at the moment.When the experiment lasted for 40 minutes, there is a very significant downward trend, and the driver's fatigue is much deeper at this moment than it had been previously.In scenario#2, the curve as a whole shows a regular decreasing trend.At the 98th sample point and the 136th sample point, the decreasing trend is faster than the previous ones.is serves as a reference for determining the pupil area threshold for different levels of fatigue later.

PERCLOS. PERCLOS refers to the time proportion of eye closure time, which has a high correlation with fatigue.
According to the definition of PERCLOS, the calculation method of the PERCLOS value is shown in formula (1).
where t i is the time when the pupil is covered longitudinally by the eyelid and T is the total detection time.
In the actual application process, PERCLOS can be calculated by calculating the proportion of closed frames to the total frames [27], as shown in the following formula: where n is the number of frames with eyes closed and N is the number of video frames in a certain time.e eye movement equipment is used to collect data in this experiment, and the data acquisition frequency is 60 Hz.Because the difference between the EM, P70 and P80 standards of the PERCLOS fatigue driving detection method is not significant [28], the critical judgment threshold for eye closure is set to 70%.In the detection of the closed state of As can be seen from the four-time period graphs in Figure 8, the PERCLOS values of the drivers in both scenarios increase with time.e abnormal values that appear     Journal of Advanced Transportation at 15 min, 30 min, and 45 min are caused by instrumentation errors and measurement errors, so they are not argued afterward.Instead, the data obtained from the statistics of the four-time periods show that the fatigue levels of drivers in different scenarios during the same time are different.e PERCLOS values for drivers in the rural road scenario are all greater than the values for drivers in the expressway scenario.
is indicates that the fatigue threshold ranges of drivers in different driving environments are different and need to be analyzed separately.
e stable PERCLOS mean value sample points of drivers were arranged according to time series, and after comparing the data, characteristics of each time in Figure 8, the PERCLOS mean value graph of drivers was obtained according to 15-minute periods, as shown in Figure 9.It is clear from the graph that the PERCLOS values of drivers show a gradual increase with increasing driving time.Whether in the expressway or rural road, the driver's driving fatigue gradually deepens with the increase of driving time.
e PERCLOS value of the driver on the expressway rises more slowly and is smaller than on the country road, indicating that the driver on the expressway feels fatigued more rapidly and at a higher level than the driver on the country road during the same driving duration.

Fatigue Grade Judgment Index
Although there will be individual differences in the fatigue index parameters of each individual, there is a distribution pattern within a certain range.It can be assumed that the individual fatigue index values show a normal distribution within the respective grade.Referring to the subjective fatigue state values of drivers, the pupil area and PERCLOS values after experimental analysis and processing are also used as the basis, and then the parameter changes in Figures 7, 8, and 9 are taken into account.According to the analysis results, the threshold of fatigue degree can be divided by two fatigue index parameters: pupil area and the PERCLOS value.e standard division of the fatigue grade index is shown in Table 4.

Introduction to the Long Short-Term Memory Network (LSTM).
e long and short-term memory network (LSTM) is a special type of RNN.It can learn long dependencies and avoid the gradient explosion and gradient disappearance of RNN.It was first proposed by Hochreiter and Schmidhuber [29] and has been improved and popularized by many people.Because it works very well on a variety of problems, it is now widely used.e main feature of LSTM is that its storage unit is essentially an accumulator [30] and c t represents the current state of memory cells, and gates can be used to protect and control the state of cells.e state of cells can generally be realized by multiple gates.At each input, when the input gate opens, the state of the cell will be remembered.When the forget gate f t opens, the last cell state c t−1 will be forgotten.When the output gate o t is opened, the cell state will be transmitted to the final state h t .e advantage of using storage cells and gates to control information transmission is that it can prevent the gradient from disappearing rapidly.e unit structure of LSTM is shown in Figure 10.
Graves proposed the propagation implementation method of the LSTM network [31], and its equation is as follows.
Forward Pass: Input Gates Forget Gates Cells Output Gates Cell Outputs where w represents the weight of the connection of different units, a t i represents the network input to unit i at time t, and b t i represents activation of unit i at time t. e subscripts l, ∅, and ω represent the input gate, forget gate, and output gate of the block, respectively.e peephole weights from cell c to the input gate, forget gate, and output gates are denoted as w cl , w c∅ , and w cω .s t c is the state of cell c at time.Iand K represent the number of inputs and outputs, respectively, and H denotes the number of cells in the hidden layer.f, g, and h are all the activation functions.
Backward Pass: e purpose of the backward pass is to calculate the gradient and thus update the parameters.Journal of Advanced Transportation Cell Outputs Output Gates Forget Gates Input Gates where G is the total number of inputs to the hidden layer.L represents the loss function used for training.
In the structure of LSTM, its input, output units, and cell state are one-dimensional, and multiple LSTM network structures can be connected to form a more complex structure.e formulas of LSTM in this paper are the same as that proposed by Graves [32], and its key equations are as follows: where σ represents sigmoid function, i, f, o, and c represent input gate, forget gate, output gate, and memory cell, respectively, ∘ represents the Hadamard product.

Model
Building.e occurrence of driver fatigue is closely related to the time axis, and it is gradually deepened with the increase of time.Driver fatigue presents regular characteristics on time series, and its data characteristics belong to time series data.
e LSTM model is time-dependent and has excellent performance on time series, which is very suitable for processing and predicting events in time series.Because driving fatigue has the feature of longtime driving, and the LSTM model is very good at handling long series data.erefore, the identification of the driver's fatigue state in this paper can be converted into a multiclassification problem for the awake, mildly fatigued, moderately fatigued, and severely fatigued driving states.e data set is based on the pupil area and PERCLOS values obtained from the experiments with drivers in the expressway scenario.Moreover, the labeled data are made according to the thresholds of different fatigue levels defined in the above.e labels for sober, mild fatigue, moderate fatigue, and severe fatigue correspond to data labels of 0, 1, 2, and 3, respectively.ere are 8140 pieces of data in the data set and 2035 pieces of data are contained under each label, of which 70% are used for training and the remaining 30% are used for testing.
e training set is used to establish the driver's fatigue grade identification model and parameter optimization in the model, and the test set is used to test the generalization ability of the model.
Before the sample data enters the network model, it is necessary to reshape each sample data into a three-dimensional vector.Each sample vector contains 2 features: the fatigue feature indicators X1 (pupil area value) and X2 (PERCLOS value).en, Min-Max normalization is used for the data to make the model fit faster and achieve the training effect better.In this paper, a one-way LSTM structure is used to construct the network model.ere are two LSTM layers.After the second LSTM output layer, two fully connected layers are connected to output the classification and recognition probability.e network model structure is shown in Figure 11.

Model Training and Results.
e LSTM fatigue grade recognition model is implemented in the Python language on the pytorch1.7.1 platform.
e min-batch training method is used and the sample batch is 64.e cross-entropy loss function is selected as the cost function and the Adam optimizer is selected to train the network, and the learning rate l r is 0.0001.Dropout is used for LSTM layer units to prevent overfitting in training, with a value of 0.8.e dimensions of the input and output of each layer of the network are shown in Table 5.
e model operates on a hardware environment with an RTX 2060 graphics card and AMD 4800 h CPU and a win-dow10 64 bit system with 16 GB of memory and uses a GPU to accelerate the training.When the number of network iterations is 55, the network training results perform well in the training set.When the number of iterations is increased, the network model is overfitted.e loss change rate and accuracy of the finally trained model are shown in Figures 12 and 13.
It can be seen from Figure 12 that the loss rate of the network gradually decreases and tends to be stable during the training process.Similarly, it can be seen from Figure 13 that the accuracy rate of the network model gradually increases and tends to be stable during the iterative training process.
e data set has been divided into a training set and test set before, and 30% of the data set is used for testing.

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Journal of Advanced Transportation erefore, the sample of the test set is 2444.e test set is tested on the trained LSTM network to evaluate the performance of the model's ability to identify fatigue levels according to its performance on the test set.Finally, the trained long short-term memory network model is tested on the test set of 2444 data, and the confusion matrix of recognition results is shown in Table 6 below.
It can be seen from the confusion matrix in the above table that the accuracy of awake state recognition is 100%, that of mild fatigue state is 93.1%, that of moderate fatigue state is 98.4%, that of severe fatigue state is 100%, and the total accuracy is 97.8%.
Lei classified drivers' fatigue into four states: awake, mild fatigue, moderate fatigue, and severe fatigue [33].He used the driving simulator and a BIOPAC multichannel physiological recorder to obtain the driver's EEG signals.After extracting the corresponding features, a driver fatigue level recognition model was trained by using the support vector machine (SVM) method.Xu et al. divided fatigue into awake, moderate fatigue, and severe fatigue, respectively.After using a driving simulator and extracting relevant indicators, he used an ordered Logit (OL) model and an artificial neural network (ANN) model to evaluate the fatigue state [34].
e accuracy of the LSTM model used in this paper is compared with the accuracy of these models, and the results are shown in Table 7.
It can be seen from Table 7 that the deep learning approach works better than the traditional machine learning approach in the driver's fatigue status recognition based on the driver's physiological response signals.e long shortterm memory network model showed a substantial improvement over the OL model, ANN model, and decision tree model in the recognition rate of each level of fatigue and the total recognition rate.Compared with the SVM model, the LSTM network model improved the recognition rate of moderate fatigue by 13% and the overall recognition rate by 6.7%.erefore, the LSTM model is very effective in recognizing the fatigue level of drivers.Note.
-indicates that the value was not assigned with any meaning for the related indicator.

Conclusion
e era of intelligent transportation is coming, and the future must be the era of autonomous driving.However, there is still a long time for autonomous driving in the true sense.People are the most important link in a traffic system composed of people, vehicles, and roads.By monitoring the driver's driving status, we can effectively achieve regional traffic safety and operational efficiency in multiple traffic modes, improve traffic economy, and enable the coordinated and benign development of regional traffic.
Based on the driving simulator experiment, the classification threshold of driver's fatigue degree in two monotonous environments of the expressway and low-grade rural highway is defined from the eye movement parameters of 36 drivers and refers to the driver's subjective fatigue state.e results show that the thresholds of different fatigue levels of them are different, and the drivers on the freeway will feel fatigued faster.Driver's fatigue level in the expressway is deeper than that in the rural highway during the same driving time.Because fatigue is a gradual process, it needs to be analyzed and processed in stages and the driver's fatigue state will be different in different monotonous driving environments.e deep learning method is also used to test the data samples obtained from the experiment.
e results show that the total accuracy of identifying different fatigue states can reach 97.8%, which is significantly improved compared with the method based on the traditional machine learning model in the traditional literature.
ere are only 38 subjects in this study, which did not fully cover the physiological signal characteristics of drivers, so more experimental samples are needed in the future.Moreover, only two common monotonous driving scenarios were considered for the study, while the real monotonous driving environment scenarios are much more than that.Although advanced driving simulators can provide driving scenarios that are highly similar to the real driving situation, making the driver's perception and reactions highly similar to the real driving situation, some factors such as projection resolution and lighting conditions in the experiment can also affect the driver, so the real driving situation is still needed as a reference in the future study.Although this paper distinguishes different fatigue thresholds and uses the deep learning model to identify the fatigue degree, the identification after obtaining the data cannot identify the fatigue state synchronously while driving [35][36][37][38][39][40][41][42][43][44].

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Journal of Advanced Transportation human eyes, both human eyes are generally open and closed at the same time.To reduce the error, it is considered closed when one of the human eyes is closed.In both scenarios, the data were processed in a time interval of 15 min to obtain the average stable PERCLOS value for each driver during this period.All the obtained PERCLOS values were plotted according to the time series to obtain a box plot, and the results are shown in Figure 8.

Figure 7 :
Figure 7: Mean pupil area of drivers.

Figure 6 :
Figure 6: Subjective fatigue state value of subjects.

Figure 12 :Figure 13 :
Figure 12: Change of loss rate during LSTM model training.

Table 1 :
Description of the driving scenarios.

Table 2 :
Driving factor variable.Note.-indicates that the value was not assigned with any meaning for the related indicator.
[26]Subjective Fatigue Survey.A subjective fatigue survey is one of the important means to study driving fatigue.e most widely used fatigue measurement scale is the Stanford Sleepiness Scale[26].eSSSscale contains 1 ∼ 7 different fatigue grades (the fatigue grade is expressed by S), and the fatigue degree is deepened in turn.As shown in Table3, S � 1 denotes complete vitality and vigor, while S � 7 indicates very tired and at the beginning of sleep.Subjects need to choose one of the seven fatigue levels to represent their current fatigue state.e advantage of the subjective evaluation table is that it is easy to operate and can be repeated.

Table 5 :
Dimensions of each layer of the network.

Table 6 :
Confusion matrix of classification results of LSTM model on the test set.

Table 7 :
Comparison of results between models.