Method, device, equipment, medium and product for identifying driving perception based on electroencephalogram signals

Abstract

The invention provides a method, a device, equipment, a medium and a product for identifying driving perception based on electroencephalogram signals, and relates to the technical field of data processing. Comprising the steps of collecting initial sample electroencephalogram signals generated by a driver; extracting an intermediate sample electroencephalogram signal from the initial sample electroencephalogram signal; determining a first sample electroencephalogram signal when the driver is in a brake response stage and a first sample electroencephalogram signal when the driver is in a brake sensing stage from the intermediate sample electroencephalogram signals based on the brake mark; performing data enhancement on the second sample electroencephalogram signal to obtain a sample electroencephalogram signal; inputting the sample electroencephalogram signal into a to-be-trained driving perception classification model to obtain a sample driving perception stage, and updating the to-be-trained driving perception model based on the sample driving perception stage corresponding to the sample electroencephalogram signal and the corresponding label until the trained driving perception classification model, therefore, the danger sensing state of the driver is monitored in real time.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, equipment, medium and product for identifying driving perception based on electroencephalogram signals. Background Art

[0002] With the rapid development of urbanization and the continuous advancement of autonomous driving technology, mixed traffic scenarios involving vehicles with different intelligence levels are becoming increasingly common. In recent years, the use of human factor driving data, especially physiological signals such as electroencephalogram (EEG) to monitor driver status has made significant contributions to traffic safety, traffic planning and vehicle intelligence. The processing technology of human factor driving data can deeply understand the driver's behavior during driving and provide technical support for improving traffic safety and efficiency.

[0003] At present, most related technologies focus on using electroencephalogram signals (EEG data) to monitor and evaluate the driver's driving behavior, such as acceleration, deceleration, turning, changing lanes, etc., but cannot monitor the driver's ability to perceive potential dangers. However, the driver's ability to perceive and respond to danger plays a vital role in dealing with mixed traffic scenarios involving different intelligence levels. Therefore, how to monitor the driver's danger perception status in real time, improve traffic safety, and reduce the occurrence of accidents has important practical significance for human society. Summary of the invention

[0004] Based on the above technical problems, the present invention provides a method, device, equipment, medium and product for identifying driving perception based on EEG signals, aiming to effectively monitor and evaluate the driver's danger perception state through EEG signals.

[0005] A first aspect of the present invention provides a method for identifying driving perception based on electroencephalogram signals, the method comprising: Collecting initial sample EEG signals generated by the driver simulating multiple driving simulation scenarios in the driving simulation platform; Extracting an intermediate sample EEG signal corresponding to a target period from the initial sample EEG signal, wherein the target period is a period consisting of a fixed time length corresponding to the moment of the braking mark in the driving simulation scene; Based on the braking mark, determining a first sample EEG signal of the driver in a braking response stage and a first sample EEG signal of the driver in a braking perception stage from the intermediate sample EEG signals; the label carried by the first sample EEG signal in the braking response stage is that the driver is in a danger response stage, and the label carried by the first sample EEG signal in the braking perception stage is that the driver is in a danger perception stage; Performing data enhancement on the second sample EEG signal to obtain a sample EEG signal to balance the number of the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; the second sample EEG signal is the first sample EEG signal with the smaller number between the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; Inputting the sample EEG signal into the driving perception classification model to be trained to obtain a sample driving perception stage corresponding to the sample EEG signal, and updating the driving perception model to be trained based on the sample driving perception stage corresponding to the sample EEG signal and a label corresponding to the sample EEG signal, until a trained driving perception classification model is obtained; The EEG signal to be tested is input into the trained driving perception classification model to obtain the driving perception stage of the driver corresponding to the EEG signal to be tested, and the driving perception stage indicates that the driver is in a danger response stage or a danger perception stage.

[0006] A second aspect of the present invention provides a device for identifying driving perception based on electroencephalogram signals, the device comprising: A signal acquisition module, used to collect initial sample EEG signals generated by the driver simulating multiple driving simulation scenarios in the driving simulation platform; A signal extraction module, used to extract an intermediate sample EEG signal corresponding to a target period from the initial sample EEG signal, wherein the target period is a period consisting of a fixed time length corresponding to the moment of the braking mark in the driving simulation scene; A signal differentiation module is used to determine, based on the braking mark, from the intermediate sample EEG signals a first sample EEG signal indicating that the driver is in a braking response phase and a first sample EEG signal indicating that the driver is in a braking perception phase; the label carried by the first sample EEG signal in the braking response phase is that the driver is in a danger response phase, and the label carried by the first sample EEG signal in the braking perception phase is that the driver is in a danger perception phase; a data enhancement module, used for performing data enhancement on the second sample EEG signal to obtain a sample EEG signal to balance the number of the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; the second sample EEG signal is the first sample EEG signal with the smaller number between the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; a model training module, used for inputting the sample EEG signal into a driving perception classification model to be trained, obtaining a sample driving perception stage corresponding to the sample EEG signal, and updating the driving perception model to be trained based on the sample driving perception stage corresponding to the sample EEG signal and a label corresponding to the sample EEG signal, until a trained driving perception classification model is obtained; The model application module is used to input the EEG signal to be tested into the trained driving perception classification model to obtain the driving perception stage of the driver corresponding to the EEG signal to be tested, and the driving perception stage indicates that the driver is in a danger response stage or a danger perception stage.

[0007] The third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for recognizing driving perception based on electroencephalogram signals according to the first aspect of the embodiment of the present invention is implemented.

[0008] The fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for identifying driving perception based on EEG signals according to the first aspect of an embodiment of the present invention is implemented.

[0009] The fifth aspect of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for identifying driving perception based on EEG signals according to the first aspect of an embodiment of the present invention.

[0010] In the method for identifying driving perception based on EEG signals provided by the present invention, initial sample EEG signals generated by the driver in a driving simulation scenario are collected, and intermediate sample EEG signals corresponding to a target period are extracted therefrom. Based on the braking mark in the driving simulation scenario, the first sample EEG signals of the driver in a braking response phase and the first sample EEG signals of the driver in a braking perception phase are determined from the intermediate sample EEG signals, and data enhancement is performed on the first sample EEG signals with a small number to obtain sample EEG signals for model training. The sample EEG signals are then input into a driving perception classification model to be trained to obtain a sample driving perception phase output by the model. The driving perception model to be trained is trained based on the sample driving perception phase corresponding to the sample EEG signals and the phase labels corresponding to the sample EEG signals to obtain a trained driving perception classification model. In this way, the trained driving perception classification model in this embodiment can predict the driver's danger perception phase and danger reaction phase, thereby achieving real-time monitoring of the driver's danger perception state through the driving perception classification model, providing important support for improving traffic safety, optimizing traffic planning and promoting vehicle intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0012] Figure 1 is a flowchart of a method for identifying driving perception based on EEG signals according to an embodiment of the present invention; Figure 2 is a schematic diagram of a simulation of a vehicle entering from the side, shown in one embodiment of the present invention; Figure 3 is an overall framework diagram of a driving perception classification model shown in one embodiment of the present invention; Figure 4 is a schematic diagram of confusion matrix results of a test set before and after data enhancement processing shown in one embodiment of the present invention; Figure 5 It is a structural block diagram of a device for identifying driving perception based on EEG signals provided by an embodiment of the present invention; Figure 6 It is a schematic diagram of an electronic device shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Please refer to Figure 1 , Figure 1 FIG. 1 is a flowchart showing a method for identifying driving perception based on EEG signals according to an embodiment of the present invention. Figure 1 As shown, the method for identifying driving perception based on EEG signals provided in this embodiment includes at least the following steps: Step S11: collecting initial sample EEG signals generated by the driver simulating multiple driving simulation scenarios in the driving simulation platform.

[0015] In this embodiment, multiple driving simulation scenarios related to braking events are simulated in the driving simulation platform, and drivers are called to perform simulation tests in the driving simulation platform, and the electroencephalogram (EEG) signals generated by the drivers simulating multiple driving simulation scenarios in the driving simulation platform are collected as initial sample electroencephalogram signals, and used as original sample data for training the driving perception classification model. Among them, electroencephalogram signals are an important biological signal used to record and analyze the electrical activity of the human brain.

[0016] The drivers can be male or female, and the selection criteria for drivers include but are not limited to: all participants have a corrected vision of no less than 5.0, hold a C1 or C2 driver's license, have some off-site driving experience, have no other mental or physical illness that may affect the test, are in good health, and have had enough sleep recently. Before participating in the test, participants are fully informed of the experimental procedures and precautions, and are ensured that the experiment will not cause harm to human health.

[0017] Step S12: extracting an intermediate sample EEG signal corresponding to a target period from the initial sample EEG signal, wherein the target period is a period consisting of a fixed time length corresponding to the moment of the braking mark in the driving simulation scene.

[0018] In this embodiment, corresponding braking marks are set for multiple driving simulation scenes in advance, and the braking marks represent the latest response of the driver to the danger in the driving simulation scene. Because the time period corresponding to the initial sample EEG signal is relatively long, it is not all valid data for the model training of the driving perception classification model. Therefore, in this embodiment, the target period (epoch) is determined by a certain time period before and after the moment of the braking mark. The role of the target period is to divide the long continuous data into short time periods for time series analysis and processing. Specifically, the target period of this embodiment is a period composed of fixed time lengths corresponding to the moment of the braking mark in the driving simulation scene. It should be noted that the fixed time lengths corresponding to the front and back can be the same or different, and there is no restriction on this. In an optional example, the period from 0.5s before the moment of the braking mark to 1.5s after the moment of the braking mark is set as the target period, and the duration of the target period is 2s.

[0019] In this way, this embodiment can extract the intermediate sample EEG signals corresponding to the target period from the initial sample EEG signals. For example, the epoch extraction function in the MNE-Python tool library is used to extract the intermediate sample EEG signals corresponding to the target period from the initial sample EEG signals. At this time, the intermediate sample EEG signals are EEG signals for the driver to effectively perceive and respond to dangers in the driving simulation scenario. The intermediate sample EEG signals help to capture short-term changes and dynamic characteristics in the data.

[0020] Step S13: Based on the braking mark, determine the first sample EEG signal of the driver in the braking response stage and the first sample EEG signal of the driver in the braking perception stage from the intermediate sample EEG signals; the label carried by the first sample EEG signal in the braking response stage is that the driver is in the danger response stage, and the label carried by the first sample EEG signal in the braking perception stage is that the driver is in the danger perception stage.

[0021] In this embodiment, after obtaining the intermediate sample EEG signal, the first sample EEG signal of the driver in the braking response stage can be determined from the intermediate sample EEG signal based on the braking mark, and the first sample EEG signal of the driver in the braking perception stage can be determined from the intermediate sample EEG signal. Among them, the braking response stage can be the stage where the driver responds to the danger and takes braking action, and the braking perception stage can be the stage where the driver perceives the danger but has not yet made a relevant response. Among them, the label carried by the first sample EEG signal of the braking response stage is: the driver is in the danger response stage, and the label carried by the first sample EEG signal of the braking perception stage is: the driver is in the danger perception stage.

[0022] Different from the related art which only focuses on the second-level hazard perception, this embodiment focuses on the analysis of the first-level hazard perception and the second-level hazard perception through EEG signals: the driver's risk perception at each stage of the dangerous scene is monitored and evaluated through EEG signals to monitor the driver's hazard perception state in real time and classify the driver's hazard perception state during driving. Among them, the first-level hazard perception refers to the driver's alert state for the possible danger in the current environment, and the second-level hazard perception (hazard response) refers to the driver's response to the danger that has occurred.

[0023] Step S14: Perform data enhancement on the second sample EEG signal to obtain a sample EEG signal to balance the number of the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; the second sample EEG signal is the first sample EEG signal with a smaller number between the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage.

[0024] Since the EEG signals in the braking perception stage are more difficult to capture, it is impossible to obtain the data corresponding to the current driver's danger perception process through obvious labels, so some of the collected data may be invalid. This leads to an imbalance in the number of the first sample EEG signals in the braking response stage and the first sample EEG signals in the braking perception stage in the data set of the first sample EEG signals collected. This imbalance may lead to overfitting problems if the model is directly trained.

[0025] Therefore, in this embodiment, the first sample EEG signal with a smaller number among the first sample EEG signals in the braking response stage and the first sample EEG signals in the braking perception stage is determined as the second sample EEG signal, and data enhancement is performed on the second sample EEG signal to obtain a sample EEG signal, so that the number of the first sample EEG signals in the braking response stage and the first sample EEG signals in the braking perception stage is balanced. Among them, the sample EEG signals include: the first sample EEG signals in the braking response stage and the first sample EEG signals in the braking perception stage, which are balanced in number. It can be understood that each sample EEG signal after data enhancement in this embodiment carries a corresponding label.

[0026] Step S15: Input the sample EEG signal into the driving perception classification model to be trained to obtain the sample driving perception stage corresponding to the sample EEG signal, and update the driving perception model to be trained based on the sample driving perception stage corresponding to the sample EEG signal and the label corresponding to the sample EEG signal, until a trained driving perception classification model is obtained.

[0027] In this embodiment, after obtaining the sample EEG signal, the sample EEG signal can be input into the driving perception classification model to be trained. Through processing by the driving perception classification model to be trained, the output of the driving perception classification model to be trained is obtained: the sample driving perception stage corresponding to the sample EEG signal, which is the model output of the model training stage, indicating that the driver corresponding to the sample EEG signal is in the danger response stage, or in the danger perception stage.

[0028] After obtaining the sample driving perception stage corresponding to the sample EEG signal, the model parameters of the driving perception model to be trained can be updated based on the sample driving perception stage corresponding to the sample EEG signal and the label corresponding to the sample EEG signal until a trained driving perception classification model is obtained. In this embodiment, the trained driving perception classification model can classify the driver's danger perception stage during driving based on the EEG signal: determine whether the driver is in the danger response stage or the danger perception stage.

[0029] Step S16: inputting the EEG signal to be tested into the trained driving perception classification model to obtain the driving perception stage of the driver corresponding to the EEG signal to be tested, wherein the driving perception stage indicates that the driver is in a danger response stage or a danger perception stage.

[0030] In this embodiment, the EEG signal to be tested is an EEG signal that needs to be classified for driving perception. The EEG signal to be tested can be a real-time EEG signal collected by the driver during the driving process. The driving perception classification model of this embodiment can be used to automatically identify the driver's danger perception state based on the EEG signal to be tested, and determine whether the driver is in the danger response stage or the danger perception stage, thereby improving traffic safety and reducing the occurrence of accidents, which has important practical significance for human society.

[0031] The collected EEG signal to be tested can be input into a trained driving perception classification model to obtain the driving perception stage of the driver corresponding to the EEG signal to be tested output by the trained driving perception classification model, wherein the predicted driving perception stage indicates that the driver is in a dangerous response stage, or in a dangerous perception stage. The driving perception stage indicates the driving perception state, the dangerous response stage indicates that the driver is in a dangerous response state, and the dangerous perception stage indicates that the driver is in a dangerous perception stage.

[0032] In this embodiment, the trained driving perception classification model can predict the driver's danger perception stage and danger response stage, thereby realizing real-time monitoring of the driver's danger perception state through the driving perception classification model, identifying the driver's perception of environmental hazards at different driving stages, and realizing automatic identification of the driver's danger perception state, providing important support for improving traffic safety, optimizing traffic planning and promoting vehicle intelligence.

[0033] In combination with the above embodiments, in one implementation, the present invention further provides a method for identifying driving perception based on EEG signals. In the method, in addition to the above steps, steps S21 and S22 may be included, and the above step S13 "based on the braking mark, determining from the intermediate sample EEG signals the first sample EEG signal of the driver in the braking response stage and the first sample EEG signal of the driver in the braking perception stage" may specifically include steps S23 and S24: Step S21: establishing a plurality of driving simulation scenarios, wherein the plurality of driving simulation scenarios at least include: a driving simulation scenario of emergency braking of a vehicle ahead, a driving simulation scenario of pedestrians crossing the road, and a driving simulation scenario of a lateral vehicle changing lanes suddenly.

[0034] In this embodiment, multiple driving simulation scenarios are established, and different driving simulation scenarios correspond to different deceleration (braking) trigger conditions. The multiple driving simulation scenarios at least include: a driving simulation scenario of emergency braking of the vehicle ahead, a driving simulation scenario of pedestrians crossing the road, and a driving simulation scenario of a lateral vehicle changing lanes sharply.

[0035] In the driving simulation scenario of emergency braking of the vehicle in front, the emergency braking of the vehicle in front is simulated: the main vehicle (i.e. the simulation test vehicle) follows the vehicle in front while driving, and the vehicle in front keeps the same speed as the main vehicle. However, the vehicle in front suddenly brakes after driving a few meters (such as 50 meters), and the speed will quickly drop to 0. The driver must decelerate quickly to avoid a rear-end collision when approaching this situation.

[0036] In the driving simulation scenario of a lateral vehicle changing lanes suddenly, the simulation is about a lateral vehicle changing lanes suddenly: if the lateral vehicle in front changes lanes suddenly, when the main vehicle is driving in the designated lane, the lateral vehicle in front will drive to a fixed position at 0.7 times the speed of the main vehicle, and make an emergency lane change to the lane where the main vehicle is located after several meters (such as 50 meters). The driver must slow down after discovering this change to avoid a rear-end collision. Of course, the situation where the lateral vehicle behind accelerates and changes lanes suddenly can also be simulated, and the present invention does not limit this.

[0037] In the driving simulation scenario of pedestrian crossing the road, the situation of pedestrian crossing the road is simulated: when the main vehicle arrives at a fixed position, the pedestrian crosses the road in front of the main vehicle at a certain speed (such as 5 m / s). The driver must react quickly and slow down to avoid hitting the pedestrian.

[0038] Take the example of a lateral vehicle changing lanes suddenly. Figure 2 As shown, Figure 2 FIG. 1 is a schematic diagram showing a simulation of a vehicle entering from the side according to an embodiment of the present invention. Figure 2 In the figure, the blue car is the main car, and the red car is the car entering from the side. Figure 2 It can be seen that in this scenario, there will be a normal driving stage, stage 1 (stage 1) and stage 2 (stage 2). Among them, stage 1 is the stage when the driver of the main vehicle perceives that there is a car driving side by side in the side lane and perceives that the car in the side lane may change lanes, which is the danger perception stage; stage 2 is the stage when the driver of the main vehicle finds that the car in the side lane changes lanes and responds to braking behavior, which is the danger response stage.

[0039] It can be understood that, except for the sudden lane change of lateral vehicles, in fact, the above-mentioned multiple driving simulation scenarios will go through the normal driving stage, the danger perception stage and the danger response stage. At this time, it is necessary to set different braking marks in multiple driving simulation scenarios, and extract the first sample EEG signal of the driver in the braking response stage and the first sample EEG signal of the driver in the braking perception stage from the initial sample EEG signals in each driving simulation scenario.

[0040] Step S22: The moment of the braking mark of the driving simulation scenario of emergency braking of the vehicle in front is set to the moment when the vehicle in front decelerates to zero; the moment of the braking mark of the driving simulation scenario of pedestrians crossing the road is set to the moment when the pedestrians start to cross the road; the moment of the braking mark of the driving simulation scenario of a sudden lane change of the lateral vehicle is set to the moment when the lateral vehicle suddenly changes lanes.

[0041] In this embodiment, for the driving simulation scenario of emergency braking of the vehicle ahead, the moment of the brake mark of the driving simulation scenario of emergency braking of the vehicle ahead can be set to the moment when the vehicle ahead decelerates to zero; for the driving simulation scenario of pedestrians crossing the road, the moment of the brake mark of the driving simulation scenario of pedestrians crossing the road can be set to the moment when the pedestrians start to cross the road; for the driving simulation scenario of a sudden lane change of a side vehicle, the moment of the brake mark of the driving simulation scenario of a sudden lane change of a side vehicle can be set to the moment when the side vehicle changes lanes sharply.

[0042] Step S23: Determine, among the intermediate sample EEG signals, the intermediate sample EEG signals that are before the moment of the braking mark as the first sample EEG signals of the driver in the braking perception stage.

[0043] In this embodiment, since the braking mark of this embodiment is the latest response of the driver to the danger by braking in the driving simulation scenario, it is considered that before the moment when the braking mark is located, the driver is still in the stage of discovering the danger but not responding. At this time, the intermediate sample EEG signal before the moment when the braking mark is located in the intermediate sample EEG signal can be determined as the first sample EEG signal of the driver in the braking perception stage.

[0044] Step S24: Determine, among the intermediate sample EEG signals, the intermediate sample EEG signals that are after the moment of the braking mark as the first sample EEG signals of the driver in the braking response stage.

[0045] In this embodiment, it can be considered that after the moment when the brake mark is located, the driver must respond to avoid danger. Therefore, the intermediate sample EEG signal after the moment when the brake mark is located in the intermediate sample EEG signal can be determined as the first sample EEG signal of the driver in the braking response stage.

[0046] In combination with the above embodiments, in one implementation, the present invention further provides a method for identifying driving perception based on EEG signals. In the method, the "inputting the sample EEG signal into the driving perception classification model to be trained to obtain the sample driving perception stage corresponding to the sample EEG signal" in the above step S15 may specifically include steps S31 to S34: Step S31: inputting the sample EEG signal into the two-dimensional convolution filter to obtain a plurality of F1 feature maps; the plurality of F1 feature maps correspond to sample EEG signals of different bandpass frequencies.

[0047] In this embodiment, the driving perception classification model to be trained includes at least: a two-dimensional convolution filter, a deep convolution module, and a separable convolution module. When the sample EEG signal is input into the driving perception classification model to be trained, the first convolution layer of the driving perception classification model to be trained is a two-dimensional convolution filter, which first inputs the sample EEG signal into the two-dimensional convolution filter, processes the sample EEG signal through the two-dimensional convolution filter, and obtains a plurality of F1 feature maps output by the two-dimensional convolution filter, wherein the plurality of F1 feature maps respectively correspond to sample EEG signals of different bandpass frequencies.

[0048] Step S32: Process each of the multiple F1 feature maps through the deep convolution module, and control the number of spatial filters learned by each F1 feature map by depth to obtain a first feature map.

[0049] In this embodiment, the second convolution layer of the driving perception classification model to be trained is a deep convolution module. After obtaining multiple F1 feature maps output by the two-dimensional convolution filter, each of the multiple F1 feature maps can be processed by the deep convolution module, and the number of spatial filters to be learned for each F1 feature map is controlled by the depth (i.e., parameter D), so as to obtain the first feature map output by the deep convolution module. The kernel size of the deep convolution module in this embodiment is (C, 1). Unlike the traditional convolutional neural network, the deep convolution module in this embodiment acts on each feature map instead of fully connecting all previous feature maps.

[0050] Step S33: Process the first feature map through a separable convolution module to obtain a second feature map.

[0051] In this embodiment, the third convolution layer of the driving perception classification model to be trained is a separable convolution module, and the first feature map can be processed by the separable convolution module to obtain a second feature map output by the separable convolution module.

[0052] Step S34: performing batch normalization on the second feature map along the feature map dimension to obtain the sample driving perception stage corresponding to the sample EEG signal.

[0053] In this embodiment, after obtaining the second feature map, the second feature map can be batch normalized along the feature map dimension to obtain the sample driving perception stage corresponding to the sample EEG signal output by the driving perception classification model to be trained.

[0054] In combination with the above embodiments, in one implementation, the present invention further provides a method for identifying driving perception based on EEG signals. In this embodiment, the convolution kernel length of the two-dimensional convolution filter is half of the data sampling rate corresponding to the sample EEG signal, and the driving perception classification model to be trained also includes: a pooling layer with an average size of (1,4); the above step S34 may specifically include step S41 and step S42: Step S41: performing batch normalization on the second feature map along the feature map dimension to obtain a third feature map.

[0055] In this embodiment, before using nonlinear activation, the model can first apply batch normalization along the feature map dimension, that is, batch normalization is performed on the second feature map, and random dropout technology is used to prevent overfitting to obtain a third feature map.

[0056] Step S42: Use the pooling layer to process the third feature map to reduce the data sampling rate corresponding to the sample EEG signal, and obtain the sample driving perception stage corresponding to the sample EEG signal.

[0057] In this embodiment, after obtaining the third feature map, a pooling layer with an average size of (1,4) is used to process the third feature map to reduce the data sampling rate corresponding to the sample EEG signal, and a maximum norm constraint of 1 is used to normalize the weight of each spatial filter to obtain the sample driving perception stage corresponding to the sample EEG signal.

[0058] In an optional implementation, binary cross entropy can be selected as the loss function of the driving perception classification model to be trained, as shown below: ; in, is the output of the driving perception classification model to be trained, is the label corresponding to the sample EEG signal. The driving perception model to be trained can be updated based on the loss function until the loss function converges to obtain a trained driving perception classification model.

[0059] In addition, in a specific embodiment, the Adam optimization algorithm can be used to update the network parameters. The initial learning rate is set to 0.001 to optimize the performance of the model. Regarding parameter settings, the driving perception classification model of this embodiment can be a binary classification model with 59 channels. DropoutRate is set to 0.5, the convolution kernel length is 32, F1 is 8, D is 2, F2 is 16, the batch size is 16, and 1000 iterations are performed.

[0060] The driving perception classification model provided in this embodiment has a powerful feature extraction capability for EEG signals. Compared with the previous method that relies on manual feature extraction, the deep learning-based method can learn complex high-dimensional features that are incomprehensible to humans through a large amount of data, thereby improving the classification accuracy and generalization performance of the model through parameter adjustment.

[0061] In one embodiment, Figure 3 As shown, Figure 3 FIG. 1 is an overall framework diagram of a driving perception classification model shown in an embodiment of the present invention. Figure 3 In the , the driving behavior classification model includes at least: a two-dimensional convolution filter (conv2d), a depthwise convolution module (depthwise_conv2d) and a separable convolution module (separable_conv2d).

[0062] In an optional embodiment, the collected intermediate sample EEG signals can be divided (e.g., a total of 2391 target cycles of intermediate sample EEG signals from 30 subjects are collected), half of the data (i.e., 1196 intermediate sample EEG signals) are designated as a training set, one quarter (i.e., 597 intermediate sample EEG signals) are allocated as validation data, and the remaining one quarter (i.e., 598 intermediate sample EEG signals) are used for testing purposes.

[0063] In combination with the above embodiments, in one implementation, the present invention further provides a method for identifying driving perception based on EEG signals. In this embodiment, the "performing data enhancement on the second sample EEG signal to obtain a sample EEG signal" in the above step S14 may specifically include steps S51 to S53: Step S51: determining k adjacent sample EEG signals of the second sample EEG signal.

[0064] In this embodiment, the second sample EEG signals of the training set and the test set can be data enhanced by SMOTE oversampling. Specifically, k adjacent sample EEG signals of the second sample EEG signal can be determined first, where k is a natural number greater than 0. Generally speaking, k=5.

[0065] Among them, the adjacent sample EEG signal is the sample EEG signal whose Euclidean distance with the second sample EEG signal is lower than the threshold. That is, by calculating the Euclidean distance between the sample EEG signal (sample) and the second sample EEG signal, according to the threshold, the sample EEG signal whose Euclidean distance is lower than the threshold is taken as the nearest neighbor sample, that is, the adjacent sample EEG signal.

[0066] In an optional embodiment, generally speaking, the number of first sample EEG signals in the braking perception phase is less than the number of first sample EEG signals in the braking response phase.

[0067] Step S52: randomly selecting a target adjacent sample EEG signal from the k adjacent sample EEG signals, and calculating the difference between the distances of the second sample EEG signal and the target adjacent sample EEG signal.

[0068] In this embodiment, one adjacent sample EEG signal among k adjacent sample EEG signals is randomly selected as the target adjacent sample EEG signal, and the difference diff between the distances of the second sample EEG signal and the target adjacent sample EEG signal is calculated.

[0069] Step S53: Based on the difference and the random number, a new synthetic sample EEG signal is generated between the second sample EEG signal and the target adjacent sample EEG signal, until the number of the generated new synthetic sample EEG signals balances the number of the first sample EEG signals in the braking response phase and the first sample EEG signals in the braking perception phase, thereby obtaining the sample EEG signal composed of the new synthetic sample EEG signal and the first sample EEG signal.

[0070] In this embodiment, after obtaining the difference diff, a new synthetic sample EEG signal can be generated between the second sample EEG signal and the target adjacent sample EEG signal based on the difference diff and a random number r between 0 and 1. The new synthetic sample EEG signal and the second sample EEG signal are first sample EEG signals belonging to the same stage, such as the first sample EEG signal belonging to the braking response stage, or the first sample EEG signal belonging to the braking perception stage.

[0071] The specific formula is as follows: ; in, To synthesize sample EEG signals, is the second sample EEG signal.

[0072] In this embodiment, the steps can be repeated: randomly selecting a target adjacent sample EEG signal from the k adjacent sample EEG signals, and calculating the difference between the distances between the second sample EEG signal and the target adjacent sample EEG signal, to, step: based on the difference and the random number, generating a new synthetic sample EEG signal between the second sample EEG signal and the target adjacent sample EEG signal, until the number of the generated new synthetic sample EEG signals balances the number of the first sample EEG signals in the braking response stage and the first sample EEG signals in the braking perception stage, and obtaining a sample EEG signal, which includes: the new synthetic sample EEG signal and the first sample EEG signal before data enhancement (i.e., the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage).

[0073] The data enhancement method proposed in this embodiment is used to upsample the data, generate approximate data points near the second sample EEG signal, and increase the sample size to solve problems such as unbalanced data distribution.

[0074] like Figure 4 As shown, Figure 4 FIG. 1 is a schematic diagram of a confusion matrix result of a test set before and after data enhancement processing, shown in an embodiment of the present invention. Figure 4 In the figure, the left side shows the confusion matrix result of the test set before data augmentation processing, and the right side shows the confusion matrix result of the test set after data augmentation processing. Stage 0 (stage0) and stage 1 (stage1) are different driving perception stages, the horizontal axis is the predicted result (Predicted label), and the vertical axis is the true label (True label). Figure 4 As can be seen on the left, the classification results are biased towards the danger response stage. Although the accuracy is not low in this case, it is suboptimal because many EEG signals in the danger perception stage are inaccurately classified as the danger response stage, which fails to classify correctly. Attempting to adjust the weight values ​​of these two stages also fails to alleviate the overfitting problem. Figure 4 As can be seen from the right side of , through the data enhancement method proposed in this embodiment, the classification result can effectively identify the danger perception stage and the danger response stage, and provide more reliable evaluation indicators.

[0075] In combination with the above embodiments, in one implementation, the present invention further provides a method for identifying driving perception based on EEG signals. In this method, in addition to the above step S12 "extracting the intermediate sample EEG signals corresponding to the target period from the initial sample EEG signals", step S61 may also be included, and the above step S12 "extracting the intermediate sample EEG signals corresponding to the target period from the initial sample EEG signals" may specifically include step S62: Step S61: The initial sample EEG signal is subjected to high-frequency noise filtering to obtain a third sample EEG signal whose frequency range is lower than the filtering threshold.

[0076] In this embodiment, after obtaining the initial sample EEG signal, the initial sample EEG signal can be subjected to high-frequency noise filtering to obtain a third sample EEG signal with a frequency range lower than the filtering threshold (such as 40 Hz), and the third sample EEG signal is a clean EEG signal. For example, the initial sample EEG signal can be subjected to high-frequency noise filtering through a linear phase FIR filter from 0.5 Hz to 40 Hz to eliminate unnecessary frequency components and noise, retain the frequency range of interest, reduce noise interference, and improve the overall signal quality.

[0077] Step S62: extracting the intermediate sample EEG signal corresponding to the target period from the third sample EEG signal.

[0078] In this embodiment, after the third sample EEG signal is obtained, an intermediate sample EEG signal corresponding to the target period is extracted from the third sample EEG signal.

[0079] In another optional embodiment, after obtaining the third sample EEG signal, the sampling frequency of the data can be reduced (for example, 1000Hz can be reduced to 500Hz by directly sampling at equal intervals), which helps to reduce the amount of data, reduce the computational complexity, and adapt to the requirements of the subsequent analysis method for a lower sampling rate. Then, since the EEG signal may include an eye movement signal, independent component analysis (ICA) is used to separate the mixed third sample EEG signal, retaining only the EEG to obtain the fourth sample EEG signal. Among them, the purpose of ICA is to separate different source signals (such as EEG and eye activity) in the EEG data. Using ICA, for example, a 64-channel EEG signal can be converted into a 64-dimensional signal. One dimension of this signal may be a separated eye movement signal. Therefore, by deleting the signals of some of the dimensions, the eye movement signal can be removed for more accurate subsequent analysis and interpretation. Finally, the intermediate sample EEG signal corresponding to the target period is extracted from the fourth sample EEG signal.

[0080] In summary, the method for identifying driving perception based on EEG signals provided by the embodiment of the present invention is used to classify EEG data, and comprehensively analyze and identify data driven by human factors, which solves the classification problem of the danger perception stage during driving, and adopts a deep learning method based on convolutional neural network. The algorithm has a strong feature extraction capability for EEG signals. Compared with the previous method that relies on manual feature extraction, the method based on deep learning can learn complex high-dimensional features that humans cannot understand through a large amount of data, thereby improving the classification accuracy and generalization performance of the model through parameter adjustment. In this method, the EEG signal in the experimental scene is filtered by high-frequency noise to obtain a clean EEG signal with a frequency range below 40Hz. The data is upsampled using a data enhancement algorithm to generate approximate data points near the existing samples and increase the sample size to solve problems such as unbalanced data distribution. Finally, the data is input into the network to establish a driving perception classification model that can identify the driver's perception of environmental dangers at different driving stages. The model realizes the automatic recognition of the driver's danger perception state to predict the driver's danger perception and reaction stage. The effectiveness of this method in addressing the classification challenges of human factor-driven data in mixed traffic scenarios. This method can also be further used to monitor and evaluate drivers' risk perception at various stages of dangerous scenarios, providing important support for improving traffic safety, optimizing traffic planning, and advancing vehicle intelligence.

[0081] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0082] Based on the same inventive concept, an embodiment of the present invention provides a device for identifying driving perception based on EEG signals. Figure 5 , Figure 5 FIG. 1 is a structural block diagram of a device for recognizing driving perception based on EEG signals provided by an embodiment of the present invention. Figure 5 As shown, the device for recognizing driving perception based on EEG signals of this embodiment may include: A signal acquisition module, used to collect initial sample EEG signals generated by the driver simulating multiple driving simulation scenarios in the driving simulation platform; A signal extraction module, used to extract an intermediate sample EEG signal corresponding to a target period from the initial sample EEG signal, wherein the target period is a period consisting of a fixed time length corresponding to the moment of the braking mark in the driving simulation scene; A signal differentiation module is used to determine, based on the braking mark, from the intermediate sample EEG signals a first sample EEG signal indicating that the driver is in a braking response phase and a first sample EEG signal indicating that the driver is in a braking perception phase; the label carried by the first sample EEG signal in the braking response phase is that the driver is in a danger response phase, and the label carried by the first sample EEG signal in the braking perception phase is that the driver is in a danger perception phase; a data enhancement module, used for performing data enhancement on the second sample EEG signal to obtain a sample EEG signal to balance the number of the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; the second sample EEG signal is the first sample EEG signal with the smaller number between the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; a model training module, used for inputting the sample EEG signal into a driving perception classification model to be trained, obtaining a sample driving perception stage corresponding to the sample EEG signal, and updating the driving perception model to be trained based on the sample driving perception stage corresponding to the sample EEG signal and a label corresponding to the sample EEG signal, until a trained driving perception classification model is obtained; The model application module is used to input the EEG signal to be tested into the trained driving perception classification model to obtain the driving perception stage of the driver corresponding to the EEG signal to be tested, and the driving perception stage indicates that the driver is in a danger response stage or a danger perception stage.

[0083] Optionally, the device further comprises: A scenario establishment module, used to establish multiple driving simulation scenarios, wherein the multiple driving simulation scenarios include at least: a driving simulation scenario of emergency braking of a vehicle ahead, a driving simulation scenario of pedestrians crossing the road, and a driving simulation scenario of a lateral vehicle changing lanes sharply; A mark setting module is used to set the time when the braking mark of the driving simulation scene of emergency braking of the front vehicle is set to the time when the front vehicle decelerates to zero; set the time when the braking mark of the driving simulation scene of pedestrians crossing the road is set to the time when the pedestrians start to cross the road; set the time when the braking mark of the driving simulation scene of the lateral vehicle changing lanes sharply is set to the time when the lateral vehicle changes lanes sharply; The signal distinguishing module comprises: A first determination module is used to determine, among the intermediate sample EEG signals, the intermediate sample EEG signals that are before the moment when the braking mark is located as the first sample EEG signals of the driver in the braking perception stage; The second determination module is used to determine the intermediate sample EEG signals that are located after the moment of the braking mark among the intermediate sample EEG signals as the first sample EEG signals of the driver in the braking response stage.

[0084] Optionally, the driving perception classification model to be trained includes at least: a two-dimensional convolution filter, a deep convolution module, and a separable convolution module; Model training module, including: A first convolution module is used to input the sample EEG signal into the two-dimensional convolution filter to obtain a plurality of F1 feature maps; the plurality of F1 feature maps correspond to sample EEG signals of different bandpass frequencies; A second convolution module, used to process each of the multiple F1 feature maps through the depth convolution module, and control the number of spatial filters learned by each F1 feature map through depth to obtain a first feature map; a third convolution module, configured to process the first feature map through a separable convolution module to obtain a second feature map; A normalization module is used to perform batch normalization on the second feature map along the feature map dimension to obtain a sample driving perception stage corresponding to the sample EEG signal.

[0085] Optionally, the convolution kernel length of the two-dimensional convolution filter is half of the data sampling rate corresponding to the sample EEG signal, and the driving perception classification model to be trained further includes: a pooling layer with an average size of (1,4); The normalization module comprises: A batch processing module, used for performing batch normalization on the second feature map along the feature map dimension to obtain a third feature map; A pooling processing module is used to use the pooling layer to process the third feature map to reduce the data sampling rate corresponding to the sample EEG signal and obtain the sample driving perception stage corresponding to the sample EEG signal.

[0086] Optionally, the data enhancement module includes: An adjacent sample determination module, used to determine k adjacent sample EEG signals of the second sample EEG signal; a difference determination module, configured to randomly select a target adjacent sample EEG signal from the k adjacent sample EEG signals, and calculate a difference between the distances of the second sample EEG signal and the target adjacent sample EEG signal; A synthetic sample generating module is used to generate a new synthetic sample EEG signal between the second sample EEG signal and the target adjacent sample EEG signal based on the difference and the random number, until the number of the generated new synthetic sample EEG signals makes the number of the first sample EEG signals in the braking response phase and the first sample EEG signals in the braking perception phase balanced, thereby obtaining the sample EEG signal composed of the new synthetic sample EEG signal and the first sample EEG signal.

[0087] Optionally, the device further comprises: A filtering module, used for filtering the initial sample EEG signal through high-frequency noise to obtain a third sample EEG signal with a frequency range lower than a filtering threshold before extracting the intermediate sample EEG signal corresponding to the target period from the initial sample EEG signal; The signal extraction module comprises: The signal extraction submodule is used to extract the intermediate sample EEG signal corresponding to the target period from the third sample EEG signal.

[0088] Based on the same inventive concept, another embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the method for identifying driving perception based on EEG signals as described in any of the above embodiments of the present invention are implemented.

[0089] Based on the same inventive concept, another embodiment of the present invention provides an electronic device, such as Figure 6 shown. Figure 6 1 is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when executed by the processor, the steps in the method for recognizing driving perception based on EEG signals described in any of the above embodiments of the present invention are implemented.

[0090] Based on the same inventive concept, another embodiment of the present invention provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps in the method for identifying driving perception based on EEG signals as described in any of the above embodiments of the present invention.

[0091] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0092] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0093] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, embodiments of the present invention may take the form of complete hardware embodiments, complete software embodiments, or embodiments combining software and hardware. Furthermore, embodiments of the present invention may take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0094] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0098] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0099] The above is a detailed introduction to the method, device, equipment, medium and product for recognizing driving perception based on EEG signals provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for identifying driving perception based on EEG signals, characterized in that: The method comprises: Collecting initial sample EEG signals generated by the driver simulating multiple driving simulation scenarios in the driving simulation platform; Extracting an intermediate sample EEG signal corresponding to a target period from the initial sample EEG signal, wherein the target period is a period consisting of a fixed time length corresponding to the moment of the braking mark in the driving simulation scene; Based on the braking mark, determining a first sample EEG signal of the driver in a braking response stage and a first sample EEG signal of the driver in a braking perception stage from the intermediate sample EEG signals; the label carried by the first sample EEG signal in the braking response stage is that the driver is in a danger response stage, and the label carried by the first sample EEG signal in the braking perception stage is that the driver is in a danger perception stage; Performing data enhancement on the second sample EEG signal to obtain a sample EEG signal to balance the number of the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; the second sample EEG signal is the first sample EEG signal with the smaller number between the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; Inputting the sample EEG signal into the driving perception classification model to be trained to obtain a sample driving perception stage corresponding to the sample EEG signal, and updating the driving perception model to be trained based on the sample driving perception stage corresponding to the sample EEG signal and a label corresponding to the sample EEG signal, until a trained driving perception classification model is obtained; The EEG signal to be tested is input into the trained driving perception classification model to obtain the driving perception stage of the driver corresponding to the EEG signal to be tested, and the driving perception stage indicates that the driver is in a danger response stage or a danger perception stage.

2. The method for identifying driving perception based on EEG signals according to claim 1, characterized in that: The method further comprises: Establishing a plurality of driving simulation scenarios, wherein the plurality of driving simulation scenarios at least include: a driving simulation scenario of emergency braking of a vehicle ahead, a driving simulation scenario of pedestrians crossing the road, and a driving simulation scenario of a lateral vehicle changing lanes sharply; The time when the brake mark of the driving simulation scene of emergency braking of the front vehicle is set to the time when the front vehicle decelerates to zero; the time when the brake mark of the driving simulation scene of pedestrians crossing the road is set to the time when the pedestrians start to cross the road; the time when the brake mark of the driving simulation scene of the lateral vehicle changing lanes sharply is set to the time when the lateral vehicle changes lanes sharply; Based on the braking mark, determining the first sample EEG signal of the driver in the braking response stage and the first sample EEG signal of the driver in the braking perception stage from the intermediate sample EEG signals, including: Determine, among the intermediate sample EEG signals, the intermediate sample EEG signals that are before the moment when the braking mark is located as the first sample EEG signal of the driver in the braking perception stage; Among the intermediate sample EEG signals, the intermediate sample EEG signals after the moment of the braking mark are determined as the first sample EEG signals of the driver in the braking response stage.

3. The method for identifying driving perception based on EEG signals according to claim 1, characterized in that: The driving perception classification model to be trained includes at least: a two-dimensional convolution filter, a deep convolution module, and a separable convolution module; Inputting the sample EEG signal into a driving perception classification model to be trained to obtain a sample driving perception stage corresponding to the sample EEG signal, including: Inputting the sample EEG signal into the two-dimensional convolution filter to obtain a plurality of F1 feature maps; the plurality of F1 feature maps correspond to sample EEG signals of different bandpass frequencies; Processing each of the multiple F1 feature maps by the deep convolution module, and controlling the number of spatial filters learned by each F1 feature map by depth, to obtain a first feature map; Processing the first feature map through a separable convolution module to obtain a second feature map; The second feature map is batch normalized along the feature map dimension to obtain a sample driving perception stage corresponding to the sample EEG signal.

4. The method for identifying driving perception based on EEG signals according to claim 3, characterized in that: The convolution kernel length of the two-dimensional convolution filter is half of the data sampling rate corresponding to the sample EEG signal, and the driving perception classification model to be trained also includes: a pooling layer with an average size of (1,4); Batch normalizing the second feature map along the feature map dimension to obtain a sample driving perception stage corresponding to the sample EEG signal, including: Performing batch normalization on the second feature map along the feature map dimension to obtain a third feature map; The third feature map is processed using the pooling layer to reduce a data sampling rate corresponding to the sample EEG signal, and a sample driving perception stage corresponding to the sample EEG signal is obtained.

5. The method for identifying driving perception based on EEG signals according to any one of claims 1 to 4, characterized in that: Performing data enhancement on the second sample EEG signal to obtain a sample EEG signal, including: Determine k adjacent sample EEG signals of the second sample EEG signal; Randomly selecting a target adjacent sample EEG signal from the k adjacent sample EEG signals, and calculating the difference between the distances of the second sample EEG signal and the target adjacent sample EEG signal; Based on the difference and the random number, a new synthetic sample EEG signal is generated between the second sample EEG signal and the target adjacent sample EEG signal, until the number of the generated new synthetic sample EEG signals balances the number of the first sample EEG signals in the braking response phase and the first sample EEG signals in the braking perception phase, thereby obtaining the sample EEG signal composed of the new synthetic sample EEG signal and the first sample EEG signal.

6. The method for identifying driving perception based on EEG signals according to any one of claims 1 to 4, characterized in that: Before extracting the intermediate sample EEG signal corresponding to the target period from the initial sample EEG signal, the method further includes: The initial sample EEG signal is subjected to high-frequency noise filtering to obtain a third sample EEG signal having a frequency range lower than a filtering threshold; Extracting an intermediate sample EEG signal corresponding to a target period from the initial sample EEG signal includes: An intermediate sample EEG signal corresponding to the target period is extracted from the third sample EEG signal.

7. A device for identifying driving perception based on EEG signals, characterized in that: The device comprises: A signal acquisition module, used to collect initial sample EEG signals generated by the driver simulating multiple driving simulation scenarios in the driving simulation platform; A signal extraction module, used to extract an intermediate sample EEG signal corresponding to a target period from the initial sample EEG signal, wherein the target period is a period consisting of a fixed time length corresponding to the moment of the braking mark in the driving simulation scene; A signal differentiation module is used to determine, based on the braking mark, from the intermediate sample EEG signals a first sample EEG signal indicating that the driver is in a braking response phase and a first sample EEG signal indicating that the driver is in a braking perception phase; the label carried by the first sample EEG signal in the braking response phase is that the driver is in a danger response phase, and the label carried by the first sample EEG signal in the braking perception phase is that the driver is in a danger perception phase; a data enhancement module, used for performing data enhancement on the second sample EEG signal to obtain a sample EEG signal to balance the number of the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; the second sample EEG signal is the first sample EEG signal with the smaller number between the first sample EEG signal in the braking response stage and the first sample EEG signal in the braking perception stage; a model training module, used for inputting the sample EEG signal into a driving perception classification model to be trained, obtaining a sample driving perception stage corresponding to the sample EEG signal, and updating the driving perception model to be trained based on the sample driving perception stage corresponding to the sample EEG signal and a label corresponding to the sample EEG signal, until a trained driving perception classification model is obtained; The model application module is used to input the EEG signal to be tested into the trained driving perception classification model to obtain the driving perception stage of the driver corresponding to the EEG signal to be tested, and the driving perception stage indicates that the driver is in a danger response stage or a danger perception stage.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by the processor, the method for identifying driving perception based on EEG signals as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying driving perception based on EEG signals as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method for identifying driving perception based on EEG signals as described in any one of claims 1 to 6 is implemented.

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