A method and system for monitoring the workload of a road maintenance work zone driver

Abstract

The application discloses a kind of road maintenance work area driver's work load monitoring method and system, belong to road traffic safety technical field.The objective index information (including the waveform energy value of alpha wave, beta wave, pupil area and scanning video rate and vehicle acceleration) of road maintenance work area driver is obtained, and the objective work load level information of driver is calculated based on objective index information, and the objective work load level is divided into five grades of low, low, medium, high and high load, respectively corresponding to the objective work load value 20, 40, 60, 80 and 100 converted;Subjective work load value of driver is obtained by using NASA-TLX scale;Then the objective work load value and subjective work load value are weighted calculation according to weight 0.5, and the work load level information of driver is obtained, the work load of driver can be visualized, so as to effectively monitor the work load of road maintenance work area road section driver, improve road maintenance work area driving safety.

Description

[0001] This application is a divisional application of a patent application entitled "A method and system for monitoring the workload of drivers in a road maintenance work area". The original application was filed on February 26, 2023, with application number 202310093028.3. Technical Field

[0002] This invention belongs to the field of road traffic safety technology, and in particular relates to a method and system for monitoring the workload of drivers in road maintenance work areas. Background Technology

[0003] In recent years, with rapid economic development and an increase in car ownership, the road network has been continuously improved. As operating time increases, axle load frequency rises, and traffic volume grows, road surface wear and cracks become inevitable. Therefore, the number of road maintenance work zones has increased dramatically in recent years. Due to the semi-enclosed nature of these work zones, they inevitably occupy road space, closing one or more lanes and complicating the driving environment. Furthermore, the deployment of traffic safety facilities in these work zones requires drivers to constantly adjust their driving posture, all of which contribute to variations in driver workload. Excessive workload increases the probability of driver misjudgment or delayed reaction, increasing driving risks. Therefore, accurately monitoring the workload of drivers in road maintenance work zones is crucial for driving safety in these areas. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for monitoring the workload of drivers in road maintenance work areas, so as to effectively monitor the workload of drivers in road maintenance work areas and improve driving safety in road maintenance work areas.

[0005] To achieve the above objectives, the present invention provides the following technical solutions.

[0006] On one hand, the present invention provides a method for monitoring the workload of drivers in road maintenance work areas, comprising: Obtain objective indicator information of drivers in the road maintenance work area; the objective indicator information includes: waveform energy values ​​of alpha and beta waves, pupil area and scan rate, and vehicle acceleration; Based on objective indicator information, the objective workload level information of drivers in the road maintenance work area is calculated; among them, the objective workload level is divided into five levels: low load, medium-low load, medium load, medium-high load and high load, and the five levels are respectively converted into objective workload values ​​of 20, 40, 60, 80 and 100. The subjective workload values ​​of drivers in road maintenance work areas were obtained using the NASA-TLX scale. The objective workload value and the subjective workload value are weighted by 0.5 to obtain the workload level information of drivers in the road maintenance work area, and the workload level information of drivers in the road maintenance work area is monitored.

[0007] Optionally, the waveform energy values ​​of the alpha and beta waves are obtained by collecting relevant data from an EEG device and processing it in Matlab; the pupil area and scan rate are obtained by collecting relevant data from an eye tracker and processing it in terminal software.

[0008] Optionally, the vehicle acceleration is obtained via dynamic GPS.

[0009] Optionally, the calculation of the objective workload level information of drivers in the road maintenance work area based on objective indicator information specifically includes: The K-means clustering method is used to classify the five objective indicators, namely the waveform energy values ​​of α-wave and β-wave, pupil area, scanning rate, and vehicle acceleration, according to the five levels. The correlation degree function is used to calculate the correlation between objective indicator information and different levels of objective workload, thereby obtaining objective workload level information of drivers in road maintenance work areas.

[0010] Optionally, the correlation degree is calculated as follows: ;in, For the object to be evaluated, the first element Level, Number The correlation between the indicators; For the first The weights of each objective indicator data are calculated using the entropy weight method as follows: α wave waveform energy value weight 0.147, β wave waveform energy value weight 0.163, pupil diameter weight 0.175, saccade count weight 0.228, and vehicle acceleration weight 0.287.

[0011] Optionally, the step of using the NASA-TLX scale to obtain the subjective workload value of drivers in the road maintenance work area specifically includes: In a real-world context, the status of the six factors of the NASA-TLX scale was assessed. In the first part of the scale, the six dimensions of mental demand, physical demand, time demand, performance level, effort level, and frustration level were scored out of 100. In the second part of the NASA-TLX scale, the six dimensions were compared pairwise, and the more important dimension was selected. There were 15 pairs in total. The second part of the NASA-TLX scale was used to determine the weight of each dimension in the scale. The method for calculating subjective workload is as follows: ;in, This represents the subjective workload value of drivers in the road maintenance work area. For the first The workload score for each project is out of 100. For the first The weight of the project, the weight value is the first one. The ratio of the number of times each item was selected in Part 2 of the scale to the total number of pairs.

[0012] Optionally, the mental effort required refers to the amount of mental activity required to complete the task, and whether the task is easy or difficult, simple or complex, or demanding or not demanding from a mental perspective. The physical demands refer to the amount of physical effort required to complete the work, whether the task is easy or difficult for you in terms of physical strength, whether it is slow or fast, whether your muscles feel relaxed or tense, and whether the movements are easy or strenuous. The time requirement refers to the speed or pace at which the task is completed, whether the pace is slow or fast, whether it makes people feel calm or flustered. The performance level refers to how well you have achieved your goals and how satisfied you are with those achievements. The level of effort refers to whether the effort required to complete your task is small or large. The degree of frustration refers to whether you feel a little or a lot of frustration and annoyance at work.

[0013] Optionally, except for performance level, which is scored from best to worst, the other five dimensions are scored from low to high.

[0014] Optionally, in the second part of the NASA-TLX scale, 15 pairs of comparisons are made between the six dimensions, including: mental and physical demands, mental and frustration, time and effort, performance and physical demands, time and frustration, mental and effort, time and performance, effort and frustration, effort and performance, mental and performance, physical and frustration, performance and frustration, mental and time, time and physical demands, and physical demands and effort.

[0015] On the other hand, the present invention provides a workload monitoring system for drivers in road maintenance work areas, used to implement the aforementioned method for monitoring the workload of drivers in road maintenance work areas. The workload monitoring system for drivers in road maintenance work areas includes: a data acquisition module, a data analysis module, and a comprehensive processing module; the data acquisition module and the data analysis module are respectively connected to the comprehensive processing module. The data acquisition module is used to acquire objective indicator information of drivers in the road maintenance work area; the objective indicator information includes: waveform energy values ​​of alpha and beta waves, pupil area and scan rate, and vehicle acceleration; The data analysis module is used to obtain the subjective workload values ​​of drivers in the road maintenance work area using the NASA-TLX scale. The integrated processing module is used to calculate the objective workload level information of drivers in the road maintenance work area based on objective indicator information; wherein, the objective workload level is divided into five levels: low load, medium-low load, medium load, medium-high load and high load, and the five levels are respectively converted into objective workload values ​​of 20, 40, 60, 80 and 100. The integrated processing module is also used to perform weighted calculations on the objective workload value and the subjective workload value, both with a weight of 0.5, to obtain the workload level information of drivers in the road maintenance work area and to monitor the workload level information of drivers in the road maintenance work area.

[0016] The present invention achieves the following technical effects: This invention detects the workload of drivers in road maintenance work areas through a combination of subjective and objective measurement methods. Objective indicators include the waveform energy values ​​of alpha and beta waves, pupil area, scan rate, and vehicle acceleration of the drivers in the road maintenance work areas. Objective measurements are used to calculate the objective workload level of drivers in the road maintenance work areas. Subjective measurements include measuring the subjective workload value of drivers in the road maintenance work areas using the NASA-TLX scale. Based on this, the objective and subjective workload values ​​are weighted at 0.5 to obtain the workload level information of drivers in the road maintenance work areas, thus monitoring the workload level of drivers in these areas. This invention visualizes driver workload through a combination of subjective and objective monitoring, effectively monitoring the workload of drivers in road maintenance work areas and improving driving safety in these areas. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0018] Figure 1 This is a flowchart illustrating the method for monitoring the workload of drivers in road maintenance work areas according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the workload monitoring system for drivers in road maintenance work areas, according to an embodiment of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0021] Example 1 like Figure 1 As shown, this embodiment provides a method for monitoring the workload of drivers in road maintenance work areas, including: Physiological and driving behavior data of the test subjects (drivers in the road maintenance work area) are collected and used together as objective data of the test subjects. The objective workload level of the driver is obtained by calculating the objective index data of the test subjects based on the correlation function; The subjective workload level of the test subjects was obtained using a subjective questionnaire. The driver's workload is obtained by combining the objective workload level with the subjective workload level.

[0022] Specifically, the physiological indicators include electroencephalogram (EEG) indicators and eye movement (EMG) indicators, and the driving behavior indicators include vehicle acceleration. The EEG indicators include the waveform energy values ​​of alpha and beta waves, and the EMG indicators include pupil area and saccade rate.

[0023] The acquisition of the subject's physiological data includes: acquiring the waveform energy values ​​of alpha and beta waves, pupil area, and saccade rate. Specifically, the waveform energy values ​​of the subject's alpha and beta waves are obtained by collecting relevant data with an electroencephalogram (EEG) device and processing it in Matlab. The subject's pupil area and saccade rate are obtained by collecting relevant data with an eye tracker and processing it in terminal software.

[0024] The acquisition of the test subject's driving behavior index data includes: acquiring the test subject's vehicle acceleration via dynamic GPS.

[0025] Furthermore, the calculation of the driver's objective workload level includes the following steps (1) to (2).

[0026] (1) The objective workload level of the driver is defined as 5 levels, namely {low workload, medium-low workload, medium workload, medium-high workload, high workload}. In this embodiment, the K-means clustering method is used to divide the five objective index data into five levels according to the idea of ​​clustering, and divide them into different levels of objective workload.

[0027] (2) Calculate the correlation degree between objective index information and different levels of objective workload based on the correlation function, and obtain the objective workload level information of the test subjects.

[0028] The correlation function is: ; in, ; .

[0029] in, The first matter element to be evaluated Individual values; For point With finite interval The distance; For point With finite interval The distance; For the object to be evaluated, the first element Level, Number The correlation between the indicators. For the classical domain object element The range of values ​​for each indicator For the first element of the domain The range of values ​​for each indicator; For the classical domain object element The lower limit of the value range of each indicator. For the classical domain object element The upper limit of the value range for each indicator; For the first element of the domain The lower limit of the value range of each indicator. For the first element of the domain The upper limit of the range of values ​​for each indicator.

[0030] The method for calculating the correlation degree is as follows: ; in, Weights for indicator data; For the object to be evaluated, the first element Level, Number The correlation between the indicators; For correlation degree.

[0031] Furthermore, the entropy weight method was used to calculate the weights of each physiological indicator data. .

[0032] Weight determination is a fundamental step in the decision-making process. This embodiment uses the entropy weight method to calculate the weights of each indicator based on the degree of information dispersion, which can reflect the evaluation effect more objectively and realistically. For example, Table 1 shows the weights of each objective indicator data calculated using the entropy weight method.

[0033] Table 1 .

[0034] Specifically, the subjective workload level of the test subjects is obtained using a subjective questionnaire, which includes the NASA-TLX scale, as shown in Tables 2, 3, and 4. The NASA-TLX scale is used as follows: For a given real-world situation, the test subjects rate the status of each of the six factors (dimensions) of the NASA-TLX scale. The NASA-TLX evaluation method requires test subjects to rate the six dimensions—mental, physical, time, performance, effort, and frustration—in the first part of the scale after completing a task, with a maximum score of 100. The first part of the scale is used to measure the driver's workload rating for each dimension, as shown in Tables 2 and 3. In the second part of the NASA-TLX scale, the six dimensions are compared pairwise. The driver needs to select the more important dimension from the two pairs, as shown in Table 4, with 15 pairs in total. The second part of the scale is used to determine the weight of each dimension in the scale; the sum of the weights of the six NASA-TLX factors equals 1.

[0035] Table 2 .

[0036] Table 3 .

[0037] Table 4 .

[0038] Furthermore, the method of obtaining the subject's subjective workload level using a subjective questionnaire also includes: The method for calculating subjective workload is as follows: ; in, This represents the driver's subjective workload. For the first The workload score for each project is out of 100. For the first The weight of the project, the weight value is the first one. The ratio of the number of times each item was selected in Part 2 of the scale to the total number of pairs (out of 15).

[0039] Furthermore, the calculation of the driver's workload level includes the following steps 1) to 2).

[0040] 1) Convert the objective workload level information of the test subject into corresponding objective workload values. The five levels of the driver's objective workload level {low load, low-medium load, medium load, medium-high load, high load} correspond to objective workload values ​​{20, 40, 60, 80, 100} respectively.

[0041] 2) Combining the objective workload level information and the subjective workload level information, the workload level information of the subject is obtained by weighted calculation.

[0042] The weights of both the objective load level information and the subjective load level information are 0.5.

[0043] Example 2 like Figure 2 As shown in the figure, this embodiment provides a workload monitoring system for drivers in road maintenance work areas, including: a data acquisition module, a data analysis module, and a comprehensive processing module. The data acquisition module and the data analysis module are respectively connected to the comprehensive processing module.

[0044] The data acquisition module is used to acquire the subject's EEG index data, eye movement index data, and driving behavior data, which together serve as the subject's objective index data.

[0045] The data analysis module uses a subjective questionnaire to evaluate the work fatigue of the test subjects and obtain their subjective workload level.

[0046] The integrated processing module uses a correlation function to calculate the objective index data of the test subjects to obtain the driver's objective workload level, and combines the objective workload level with the subjective workload level to obtain the driver's workload.

[0047] Furthermore, the data acquisition module includes a first sub-acquisition module and a second sub-acquisition module. The EEG index data includes the waveform energy values ​​of alpha and beta waves, the eye movement index data includes pupil area and scan rate, and the driving behavior data includes the subject's vehicle acceleration.

[0048] The first sub-acquisition module is used to acquire the waveform energy values ​​of the alpha and beta waves, pupil area, and scan rate of the subject.

[0049] The second sub-acquisition module is used to acquire the vehicle acceleration of the test subject.

[0050] Furthermore, the data analysis module utilizes information from the subjective questionnaires completed by the test subjects to determine their subjective workload level based on the NASA-TLX scale.

[0051] The method for calculating subjective workload is as follows: ; in, This represents the driver's subjective workload. For the first The workload score for each project is out of 100. For the first The weight of the project, the weight value is the first one. The ratio of the number of times each item was selected in Part 2 of the scale to the total number of pairs (out of 15).

[0052] Furthermore, the integrated processing module includes a first sub-processing module, a second sub-processing module, and a third sub-processing module; the first sub-processing module is connected to the second sub-processing module, and the second sub-processing module is connected to the third sub-processing module.

[0053] The first sub-processing module is used to calculate the correlation function: ; in, ; .

[0054] in, The first matter element to be evaluated Individual values; For point With finite interval The distance; For point With finite interval The distance; For the object to be evaluated, the first element Level, Number The correlation between the indicators. For the classical domain object element The range of values ​​for each indicator For the first element of the domain The range of values ​​for each indicator; For the classical domain object element The lower limit of the value range of each indicator. For the classical domain object element The upper limit of the value range for each indicator; For the first element of the domain The lower limit of the value range of each indicator. For the first element of the domain The upper limit of the range of values ​​for each indicator.

[0055] The second sub-processing module is used to calculate the correlation degree based on the correlation degree function: To obtain objective workload level information of the test subjects.

[0056] The third sub-processing module is used to convert the objective workload level information of the test subject into the corresponding objective workload value, and to calculate the driver's workload by combining the objective workload level and the subjective workload level.

[0057] Furthermore, the weight of the correlation Determined in the following ways: Obtain the values ​​of each indicator when different personnel drive in the road maintenance work area; The data is normalized using the maximum-minimum method; The weight values ​​of each indicator are calculated using the entropy weight method.

[0058] This invention detects the workload of drivers in road maintenance work areas through a combination of subjective and objective measurements. Objective measurements include acquiring physiological and driving behavior data of the drivers, calculating the correlation between these data and workload levels using a correlation function, and obtaining the driver's objective workload level. Subjective measurements include measuring the driver's subjective workload level using the NASA-TLX scale. The objective and subjective workload levels are then combined to obtain the driver's actual workload. This invention visualizes driver workload through a combination of subjective and objective monitoring, effectively monitoring driver workload in road maintenance work areas and improving driving safety in these areas.

[0059] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring the workload of drivers in road maintenance work areas, characterized in that, include: Obtain objective indicator information of drivers in road maintenance work areas; The objective indicators include: the waveform energy values ​​of alpha and beta waves, pupil area and scan rate, and vehicle acceleration; Based on objective indicator information, the objective workload level information of drivers in the road maintenance work area is calculated; among them, the objective workload level is divided into five levels: low load, medium-low load, medium load, medium-high load and high load, and the five levels are respectively converted into objective workload values ​​of 20, 40, 60, 80 and 100. The subjective workload values ​​of drivers in road maintenance work areas were obtained using the NASA-TLX scale. The objective workload value and the subjective workload value are weighted by 0.5 to obtain the workload level information of drivers in the road maintenance work area, and the workload level information of drivers in the road maintenance work area is monitored.

2. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 1, characterized in that, The waveform energy values ​​of the α wave and β wave were obtained by collecting relevant data from an EEG device and processing it in Matlab; the pupil area and scan rate were obtained by collecting relevant data from an eye tracker and processing it in terminal software.

3. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 1, characterized in that, The vehicle acceleration is obtained via dynamic GPS.

4. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 1, characterized in that, The calculation of the objective workload level information of drivers in the road maintenance work area based on objective indicator information specifically includes: The K-means clustering method is used to classify the five objective indicators, namely the waveform energy values ​​of α-wave and β-wave, pupil area, scanning rate, and vehicle acceleration, according to the five levels. The correlation degree function is used to calculate the correlation between objective indicator information and different levels of objective workload, thereby obtaining the objective workload level information of drivers in the road maintenance work area.

5. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 4, characterized in that, The method for calculating the correlation degree is as follows: ;in, For the object to be evaluated, the first element Level, Number The correlation between the indicators; For the first The weights of each objective indicator data are calculated using the entropy weight method as follows: α wave waveform energy value weight 0.147, β wave waveform energy value weight 0.163, pupil diameter weight 0.175, saccade count weight 0.228, and vehicle acceleration weight 0.

287.

6. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 1, characterized in that, The method of obtaining the subjective workload values ​​of drivers in road maintenance work areas using the NASA-TLX scale specifically includes: In a real-world context, the status of the six factors of the NASA-TLX scale was assessed. In the first part of the scale, the six dimensions of mental demand, physical demand, time demand, performance level, effort level, and frustration level were scored out of 100. In the second part of the NASA-TLX scale, the six dimensions were compared pairwise, and the more important dimension was selected. There were 15 pairs in total. The second part of the NASA-TLX scale was used to determine the weight of each dimension in the scale. The method for calculating subjective workload is as follows: ;in, This represents the subjective workload value of drivers in the road maintenance work area. For the first The workload score for each project is out of 100. For the first The weight of the project, the weight value is the first one. The ratio of the number of times each item was selected in Part 2 of the scale to the total number of pairs.

7. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 6, characterized in that, The mental effort required refers to the amount of mental activity required to complete the task, and whether the task is easy or difficult, simple or complex, or demanding or not demanding from a mental perspective. The physical demands refer to the amount of physical effort required to complete the work, whether the task is easy or difficult for you in terms of physical strength, whether it is slow or fast, whether your muscles feel relaxed or tense, and whether the movements are easy or strenuous. The time requirement refers to the speed or pace at which the task is completed, whether the pace is slow or fast, whether it makes people feel calm or flustered. The performance level refers to how well you have achieved your goals and how satisfied you are with those achievements. The level of effort refers to whether the effort required to complete your task is small or large. The degree of frustration refers to whether you feel a little or a lot of frustration and annoyance at work.

8. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 6, characterized in that, Except for performance level, which is scored from best to worst, the other five dimensions are scored from lowest to highest.

9. The method for monitoring the workload of drivers in road maintenance work areas as described in claim 6, characterized in that, In the second part of the NASA-TLX scale, 15 pairs of comparisons are made between the six dimensions: mental and physical demands, mental demands and frustration levels, time demands and effort levels, performance levels and physical demands, time demands and frustration levels, mental demands and effort levels, time demands and performance levels, effort levels and frustration levels, effort levels and performance levels, mental demands and performance levels, physical demands and frustration levels, performance levels and frustration levels, mental demands and time demands, time demands and physical demands, and physical demands and effort levels.

10. A workload monitoring system for drivers in a road maintenance work area, characterized in that, To implement the method for monitoring the workload of drivers in road maintenance work areas as described in claim 1, the road maintenance work area driver workload monitoring system includes: a data acquisition module, a data analysis module, and a comprehensive processing module; the data acquisition module and the data analysis module are respectively connected to the comprehensive processing module; The data acquisition module is used to acquire objective indicator information of drivers in the road maintenance work area; the objective indicator information includes: waveform energy values ​​of alpha and beta waves, pupil area and scan rate, and vehicle acceleration; The data analysis module is used to obtain the subjective workload values ​​of drivers in the road maintenance work area using the NASA-TLX scale. The integrated processing module is used to calculate the objective workload level information of drivers in the road maintenance work area based on objective indicator information; wherein, the objective workload level is divided into five levels: low load, medium-low load, medium load, medium-high load and high load, and the five levels are respectively converted into objective workload values ​​of 20, 40, 60, 80 and 100. The integrated processing module is also used to perform weighted calculations on the objective workload value and the subjective workload value, both with a weight of 0.5, to obtain the workload level information of drivers in the road maintenance work area and to monitor the workload level information of drivers in the road maintenance work area.

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