A method and system for predicting driver fatigue accumulation degree in long and large highway tunnel and music intervention

Abstract

The present application is suitable for the technical field of driving fatigue intervention, and provides a long and large highway tunnel driver fatigue accumulation degree prediction and music intervention method and system. The present application determines the target vehicle entering the long and large highway tunnel, and obtains fatigue monitoring data; when the target driver is in a suspicious fatigue state, the fatigue accumulation degree is calculated to determine whether there is fatigue accumulation anomaly; when it is determined that there is fatigue accumulation anomaly and there is no following vehicle, fatigue intervention music is selected and sent to the target vehicle for fatigue prevention music intervention. Whether the target driver is in a suspicious fatigue state can be identified and judged, and when in a suspicious fatigue state, the fatigue accumulation degree is calculated, and when there is fatigue accumulation anomaly and there is no following vehicle, fatigue prevention music intervention is performed, so that timely and accurate intervention can be performed on the driver fatigue when there is no obvious driving fatigue sign, and the present application is effectively applicable to the application scene of long and large highway tunnels.

Description

Technical Field

[0001] This invention belongs to the field of driving fatigue intervention technology, and in particular relates to a method and system for predicting driver fatigue accumulation and using music intervention in long highway tunnels. Background Technology

[0002] Driver fatigue intervention is a safety measure that uses monitoring, analysis, early warning, and adjustment technologies to proactively intervene in and manage a driver's physiological and psychological state and driving behavior when the driver shows signs of fatigue, is at risk of fatigue, or has already entered a state of fatigued driving. This aims to reduce the adverse effects of fatigue on driving safety and prevent traffic accidents.

[0003] Because long highway tunnels are characterized by long travel distances, strong enclosure, and monotonous environments, drivers are prone to visual fatigue and decreased attention, which increases the risk of drowsy driving. Therefore, driver fatigue intervention in long highway tunnels is particularly important.

[0004] However, existing driver fatigue intervention methods in long highway tunnels typically employ standardized and fixed fatigue intervention reminder strategies, which are insufficient in their effectiveness. Furthermore, they can only provide fatigue intervention reminders when obvious signs of driver fatigue are detected, resulting in significant lag and making it difficult to achieve timely and accurate intervention for driver fatigue in the application scenario of long highway tunnels. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting driver fatigue accumulation and using music intervention in long highway tunnels, aiming to solve the technical problems existing in the prior art mentioned in the background.

[0006] The embodiments of the present invention are implemented as follows: A method for predicting cumulative driver fatigue in long highway tunnels and using music intervention, the method specifically includes the following steps: Identify target vehicles entering long highway tunnels, acquire fatigue monitoring data, and determine whether the target driver is in a state of suspected fatigue. When the target driver is in a state of suspected fatigue, the fatigue accumulation degree is calculated based on the fatigue monitoring data and compared with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation. When fatigue accumulation anomalies are detected, road vehicle monitoring data is acquired to identify and determine whether there are following vehicles. When a following vehicle is detected, assistance intervention control is applied to the following vehicle, and anti-fatigue assistance intervention is applied to the target vehicle by honking the horn and flashing the flashing light. When it is determined that there is no following vehicle, fatigue intervention music is selected and sent to the target vehicle to provide anti-fatigue music intervention for the target driver.

[0007] As a further limitation of the technical solution of this invention, the steps of determining the target vehicle entering the long highway tunnel, acquiring fatigue monitoring data, and identifying and judging whether the target driver is in a state of suspected fatigue specifically include the following steps: Identify the target vehicle entering the long highway tunnel; Establish a target communication connection with the target vehicle, and generate and send a fatigue monitoring request to the target vehicle; Receive fatigue monitoring data transmitted back from the target vehicle; Extract eye-closed monitoring data from the fatigue monitoring data; The closed-eye monitoring data is identified to determine whether the target driver in the target vehicle is in a state of suspected fatigue.

[0008] As a further limitation of the technical solution of this embodiment of the invention, the step of calculating the fatigue accumulation degree based on the fatigue monitoring data when the target driver is in a suspected fatigue state, and comparing it with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation, specifically includes the following steps: When the target driver is in a state of suspected fatigue, the cumulative fatigue level is calculated based on the fatigue monitoring data. The fatigue accumulation is compared with a preset accumulation threshold; When the fatigue accumulation degree is greater than the accumulation threshold, it is determined that there is a fatigue accumulation anomaly; When the fatigue accumulation degree is not greater than the accumulation threshold, it is determined that there is no fatigue accumulation abnormality.

[0009] As a further limitation of the technical solution of this embodiment of the invention, the formula for calculating the fatigue accumulation degree is as follows: ; Where F(t) is the fatigue accumulation degree; Fo is the preset cumulative correction coefficient; t b The start time of suspected fatigue state; t c E is the current time; E is the eye-closing coefficient, which is determined by the target driver's eye-closing frequency and duration; D is the lane deviation coefficient, which is determined by the number of times the target vehicle deviates from its lane.

[0010] As a further limitation of the technical solution of this invention, the step of acquiring road vehicle monitoring data and identifying and determining whether there is a following vehicle when fatigue accumulation abnormality is determined specifically includes the following steps: When fatigue accumulation anomalies are detected, road vehicle monitoring data is acquired; Vehicle identification is performed on the road vehicle monitoring data to determine the nearest vehicle following the target vehicle; Based on the road vehicle monitoring data, the vehicle distance between the target vehicle and the nearest vehicle is calculated; The vehicle distance is compared with a preset following threshold; When the distance to the vehicle is less than the following threshold, the nearest vehicle is determined to be the following vehicle; If the distance to the vehicle is not less than the following threshold, it is determined that there is no following vehicle.

[0011] As a further limitation of the technical solution of this invention, when it is determined that there is a following vehicle, the step of assisting the following vehicle with anti-fatigue assistance control by sounding the horn and flashing the flashing light to the target vehicle specifically includes the following steps: When a vehicle is detected as following, an intervention signal is generated. The assistance intervention signal is sent to the following vehicle; Based on the aforementioned assistance intervention signal, assistance intervention control is applied to the following vehicle, and anti-fatigue assistance intervention is performed by honking and flashing a horn towards the target vehicle.

[0012] As a further limitation of the technical solution of this invention, the step of selecting fatigue intervention music and sending it to the target vehicle when it is determined that there is no following vehicle, and performing anti-fatigue music intervention on the target driver, specifically includes the following steps: When it is determined that there is no following vehicle, a fatigue intervention level is matched according to the fatigue accumulation degree; Based on the fatigue intervention level, determine the BPM intervention range; Select fatigue intervention music based on the BPM intervention range; Send the fatigue intervention music to the target vehicle; The fatigue intervention music is played in the target vehicle to provide anti-fatigue music intervention for the target driver in the target vehicle.

[0013] A system for predicting driver fatigue accumulation and providing music intervention in long highway tunnels, the system comprising a suspected fatigue state identification module, a fatigue accumulation calculation module, a following vehicle judgment module, a fatigue prevention assistance intervention module, and a fatigue prevention music intervention module, wherein: The suspected fatigue state identification module is used to identify target vehicles entering long highway tunnels, acquire fatigue monitoring data, and identify and determine whether the target driver is in a suspected fatigue state. The fatigue accumulation calculation module is used to calculate the fatigue accumulation based on the fatigue monitoring data when the target driver is in a suspected fatigue state, and compare it with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation. The following vehicle detection module is used to acquire road vehicle monitoring data and identify and determine whether there is a following vehicle when fatigue accumulation abnormality is detected. The fatigue prevention assistance intervention module is used to assist and control the following vehicle when it is determined that there is a following vehicle, and to perform anti-fatigue assistance intervention by sounding the horn and flashing the flashing light to the target vehicle. The fatigue-prevention music intervention module is used to select fatigue intervention music and send it to the target vehicle when it is determined that there is no following vehicle, so as to provide fatigue-prevention music intervention for the target driver.

[0014] As a further limitation of the technical solution of this embodiment of the invention, the suspected fatigue state identification module specifically includes: The target vehicle identification unit is used to identify target vehicles entering long highway tunnels. The request sending unit is used to establish a target communication connection with the target vehicle, generate and send a fatigue monitoring request to the target vehicle. A data receiving unit is used to receive fatigue monitoring data transmitted back by the target vehicle; The data extraction unit is used to extract eye-closed monitoring data from the fatigue monitoring data; The data recognition unit is used to identify the closed-eye monitoring data and determine whether the target driver in the target vehicle is in a state of suspected fatigue.

[0015] As a further limitation of the technical solution of this embodiment of the invention, the anti-fatigue music intervention module specifically includes: A level matching unit is used to match a fatigue intervention level based on the fatigue accumulation degree when it is determined that there is no following vehicle. The interval determination unit is used to determine the BPM intervention interval based on the fatigue intervention level. The music selection unit is used to select fatigue intervention music according to the BPM intervention range; A music transmitting unit is used to transmit the fatigue intervention music to the target vehicle; The music intervention unit is used to play the fatigue intervention music in the target vehicle to provide anti-fatigue music intervention for the target driver in the target vehicle.

[0016] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention identifies and judges whether the target driver is in a state of suspected fatigue, calculates the fatigue accumulation degree when the driver is in a state of suspected fatigue, and performs anti-fatigue music intervention when there is an abnormal fatigue accumulation and there is no following vehicle. Thus, it can still intervene in the driver fatigue in a timely and accurate manner when there are no obvious signs of driver fatigue, and is effectively applicable to the application scenario of long highway tunnels. (2) This invention calculates the fatigue accumulation degree and compares it with a preset accumulation threshold. When it is determined that there is an abnormal fatigue accumulation, it acquires road vehicle monitoring data, identifies and judges whether there is a following vehicle, and then generates an assistance intervention signal when there is a following vehicle, sends it to the following vehicle, and then performs assistance intervention control on the following vehicle, and performs anti-fatigue assistance intervention by honking and flashing the horn to the target vehicle. In this way, it can achieve cross-vehicle linkage fatigue intervention through cooperation with other vehicles, and improve the overall driving safety level in the complex road scenario of long highway tunnels. Attached Figure Description

[0017] Figure 1 A flowchart of the method for predicting driver fatigue accumulation and music intervention in long highway tunnels provided by an embodiment of the present invention is shown; Figure 2 The diagram illustrates the application architecture of the long highway tunnel driver fatigue accumulation prediction and music intervention system provided in this embodiment of the invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Understandably, due to the characteristics of long highway tunnels—long travel distances, strong enclosure, and monotonous environments—drivers are prone to visual fatigue and decreased attention, thus increasing the risk of drowsy driving. Therefore, driver fatigue intervention in long highway tunnels is particularly important. However, existing driver fatigue intervention methods in long highway tunnels typically employ standardized and fixed fatigue warning strategies, which are insufficient in effectiveness. Furthermore, they only provide fatigue warnings when obvious signs of driver fatigue are detected, exhibiting significant lag and making it difficult to achieve timely and accurate intervention for driver fatigue in the application scenario of long highway tunnels.

[0020] To address the aforementioned issues, this invention discloses a method and system for predicting driver fatigue accumulation and providing music intervention in long highway tunnels. The method involves identifying a target vehicle entering the tunnel, acquiring fatigue monitoring data, and determining whether the target driver is in a state of suspected fatigue. If the driver is in a state of suspected fatigue, the method calculates the cumulative fatigue level based on the monitoring data and compares it with a preset accumulation threshold to determine if there is an abnormality in fatigue accumulation. If an abnormality is determined, the method acquires road vehicle monitoring data and identifies whether there is a following vehicle. If a following vehicle is identified, the method provides assistance and intervention control, including honking and flashing a horn to the target vehicle to provide fatigue prevention assistance. If no following vehicle is identified, fatigue intervention music is selected and sent to the target vehicle to provide fatigue prevention music intervention for the driver. This method can identify and determine whether the driver is in a state of suspected fatigue, calculate the cumulative fatigue level when the driver is in a state of suspected fatigue, and provide fatigue prevention music intervention when there is an abnormality in fatigue accumulation and no following vehicle. Therefore, it can provide timely and accurate intervention for driver fatigue even when there are no obvious signs of driving fatigue, making it effectively applicable to long highway tunnel scenarios.

[0021] Specifically, Figure 1 The flowchart illustrates the method for predicting driver fatigue accumulation and using music intervention in long highway tunnels provided by an embodiment of the present invention.

[0022] In a preferred embodiment of the present invention, a method for predicting cumulative driver fatigue in long highway tunnels and using music intervention is provided, the method specifically including the following steps: Step S101: Identify the target vehicle entering the Changda Highway Tunnel, obtain fatigue monitoring data, and identify and determine whether the target driver is in a state of suspected fatigue.

[0023] In this embodiment of the invention, after a vehicle enters a long highway tunnel, it is marked as a target vehicle. With prior authorization to intervene in the fatigue of the target vehicle, a temporary target communication connection is established. Simultaneously, a fatigue monitoring request is generated and sent to the target vehicle via the target communication connection. Upon receiving the fatigue monitoring request, the target vehicle transmits fatigue monitoring data. By receiving the fatigue monitoring data transmitted from the target vehicle, and extracting the target driver's eye-closing monitoring data from it, the system identifies whether the target driver is in a state of suspected fatigue. Specifically, by identifying the frequency and duration of eye closure in the eye-closing monitoring data, if neither the frequency nor the duration exceeds a corresponding preset threshold, the target driver is determined not to be in a state of suspected fatigue; if the frequency exceeds the preset threshold but the duration does not, the target driver is determined to be in a state of suspected fatigue; if the duration exceeds the preset threshold, the target driver is determined to be in a state of definite fatigue.

[0024] It is understood that in this embodiment of the invention, for vehicles entering the highway, an authorization request will be sent to the vehicle in advance. If the authorization request is approved, the vehicle's fatigue intervention authorization can be obtained, and the vehicle will be included in the highway driving fatigue collaborative intervention network. Then, when the vehicle is driving on the highway, a temporary communication connection can be established with the vehicle, and temporary control can be performed on non-safety-critical functions such as music playback, horn honking, and headlight flashing. However, it will not involve control directly related to driving safety such as vehicle steering, braking, acceleration, and lane changing.

[0025] It is understood that, in this embodiment of the invention, the fatigue monitoring data includes eye-closing monitoring data for the target driver in the target vehicle and lane departure monitoring data for the target vehicle itself.

[0026] Understandably, if the target driver is in a state of definite fatigue, there is no need to calculate and analyze the cumulative fatigue level; instead, fatigue prevention assistance intervention and / or fatigue prevention music intervention can be directly implemented.

[0027] Specifically, in another preferred embodiment provided by the present invention, the steps of determining the target vehicle entering the long highway tunnel, acquiring fatigue monitoring data, and identifying and judging whether the target driver is in a state of suspected fatigue include the following steps: Identify the target vehicle entering the long highway tunnel; Establish a target communication connection with the target vehicle, and generate and send a fatigue monitoring request to the target vehicle; Receive fatigue monitoring data transmitted back from the target vehicle; Extract eye-closed monitoring data from the fatigue monitoring data; The closed-eye monitoring data is identified to determine whether the target driver in the target vehicle is in a state of suspected fatigue.

[0028] Furthermore, the method for predicting cumulative driver fatigue in long highway tunnels and using music intervention also includes the following steps: Step S102: When the target driver is in a state of suspected fatigue, calculate the fatigue accumulation degree based on the fatigue monitoring data and compare it with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation.

[0029] In this embodiment of the invention, when it is determined that the target driver is in a state of suspected fatigue, fatigue monitoring data is analyzed to calculate the fatigue accumulation degree, and the fatigue accumulation degree is compared with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation. Specifically, if the fatigue accumulation degree is greater than the accumulation threshold, it is determined that there is an abnormality in fatigue accumulation; if the fatigue accumulation degree is not greater than the accumulation threshold, it is determined that there is no abnormality in fatigue accumulation. The formula for calculating the fatigue accumulation degree is: ; Where F(t) is the fatigue accumulation degree; Fo is the preset cumulative correction coefficient; t b The start time of suspected fatigue state; t c E is the current time; E is the eye-closing coefficient, which is determined by the target driver's eye-closing frequency and duration; D is the lane deviation coefficient, which is determined by the number of times the target vehicle deviates from its lane.

[0030] It is understood that E is the eye-closing coefficient, which is determined by the target driver's eye-closing frequency and duration. Specifically, the faster the target driver closes their eyes and the longer the duration, the larger the eye-closing coefficient, which can indicate the accelerated accumulation of fatigue. D is the lane deviation coefficient, which is determined by the number of times the target vehicle deviates from its lane. Specifically, the more times the target vehicle deviates from its lane, the smaller the lane deviation coefficient, which can indicate that the fatigue level is higher under the same driving time. In this embodiment of the invention, the value of the lane deviation coefficient D is > 0.

[0031] Specifically, in another preferred embodiment provided by the present invention, when the target driver is in a suspected state of fatigue, calculating the fatigue accumulation degree based on the fatigue monitoring data and comparing it with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation specifically includes the following steps: When the target driver is in a state of suspected fatigue, the cumulative fatigue level is calculated based on the fatigue monitoring data. The fatigue accumulation is compared with a preset accumulation threshold; When the fatigue accumulation degree is greater than the accumulation threshold, it is determined that there is a fatigue accumulation anomaly; When the fatigue accumulation degree is not greater than the accumulation threshold, it is determined that there is no fatigue accumulation abnormality.

[0032] Furthermore, the method for predicting cumulative driver fatigue in long highway tunnels and using music intervention also includes the following steps: Step S103: When fatigue accumulation abnormality is determined, obtain road vehicle monitoring data, identify and determine whether there is a following vehicle.

[0033] In this embodiment of the invention, when fatigue accumulation anomaly is determined, road vehicle monitoring data is acquired and vehicle identification is performed on the road vehicle monitoring data to determine the nearest vehicle that is traveling in the same direction as the target vehicle and is located behind the target vehicle. Then, based on the road vehicle monitoring data, the vehicle distance between the target vehicle and the nearest vehicle is calculated. By comparing the vehicle distance with a preset following threshold, it is determined whether there is a following vehicle. Specifically, when the vehicle distance is less than the following threshold, it is determined that there is a following vehicle, and at this time, the nearest vehicle is marked as a following vehicle; when the vehicle distance is not less than the following threshold, it is determined that there is no following vehicle.

[0034] Specifically, in another preferred embodiment provided by the present invention, the step of acquiring road vehicle monitoring data and identifying and determining whether there is a following vehicle when fatigue accumulation abnormality is determined specifically includes the following steps: When fatigue accumulation anomalies are detected, road vehicle monitoring data is acquired; Vehicle identification is performed on the road vehicle monitoring data to determine the nearest vehicle following the target vehicle; Based on the road vehicle monitoring data, the vehicle distance between the target vehicle and the nearest vehicle is calculated; The vehicle distance is compared with a preset following threshold; When the distance to the vehicle is less than the following threshold, the nearest vehicle is determined to be the following vehicle; If the distance to the vehicle is not less than the following threshold, it is determined that there is no following vehicle.

[0035] Furthermore, the method for predicting cumulative driver fatigue in long highway tunnels and using music intervention also includes the following steps: Step S104: When it is determined that there is a following vehicle, assist the following vehicle with intervention control, and use horn blasting and flashing to assist the target vehicle to prevent fatigue.

[0036] In this embodiment of the invention, when it is determined that there is a following vehicle, an assistance intervention signal is generated and sent to the following vehicle. At this time, based on the assistance intervention signal, the following vehicle can be controlled to perform assistance intervention, so that the following vehicle can honk its horn and flash its headlights, thereby realizing anti-fatigue assistance intervention by honking and flashing its horn towards the target vehicle.

[0037] It is understood that in this embodiment of the invention, fatigue intervention authority for following vehicles is pre-defined, and therefore, assisted intervention control of following vehicles can be performed by honking and flashing through an assisted intervention signal.

[0038] It is understood that, in this embodiment of the invention, a corresponding fatigue intervention level can be matched according to different levels of fatigue accumulation. Different fatigue intervention levels correspond to different intervention control durations, and corresponding anti-fatigue assistance interventions such as horn beeping and flashing can be performed according to the corresponding intervention control duration. For example: When the fatigue intervention level is mild fatigue, the corresponding intervention control duration is 1 second, and a 1 second anti-fatigue assistance intervention of horn + flashing is performed. When the fatigue intervention level is moderate fatigue, the corresponding intervention control duration is 3 seconds, then a 3-second horn-sounding + flashing anti-fatigue assistance intervention will be performed. When the fatigue intervention level is high fatigue, the corresponding intervention control duration is 6 seconds, and a 6-second horn-sounding and flashing anti-fatigue assistance intervention will be performed.

[0039] Specifically, in another preferred embodiment provided by the present invention, the step of assisting the following vehicle with anti-fatigue assistance control when it is determined that there is a following vehicle, and providing anti-fatigue assistance intervention by honking and flashing a horn to the target vehicle, specifically includes the following steps: When a vehicle is detected as following, an intervention signal is generated. The assistance intervention signal is sent to the following vehicle; Based on the aforementioned assistance intervention signal, assistance intervention control is applied to the following vehicle, and anti-fatigue assistance intervention is performed by honking and flashing a horn towards the target vehicle.

[0040] Furthermore, the method for predicting cumulative driver fatigue in long highway tunnels and using music intervention also includes the following steps: Step S105: When it is determined that there is no following vehicle, select fatigue intervention music and send it to the target vehicle to provide anti-fatigue music intervention for the target driver.

[0041] In this embodiment of the invention, when it is determined that there is no following vehicle, a fatigue intervention level is matched according to the fatigue accumulation level, and a BPM intervention range is determined according to the fatigue intervention level. Then, according to the BPM intervention range, fatigue intervention music is selected from a preset music database. The fatigue intervention music is sent to the target vehicle and then played in the target vehicle to achieve anti-fatigue music intervention for the target driver in the target vehicle.

[0042] It is understood that, in the embodiments of the present invention, different levels of fatigue accumulation are matched with different fatigue intervention levels, corresponding to different BPM intervention intervals, specifically: When the fatigue intervention level is mild fatigue, the BPM intervention range is 90~110 BPM, which is the BPM range of upbeat music; When the fatigue intervention level is moderate fatigue, the BPM intervention range is 110~130 BPM, which is within the BPM range of motivational music; When the fatigue intervention level is high fatigue, the BPM intervention range is 130~150 BPM, which belongs to the BPM range of high arousal music.

[0043] Specifically, in another preferred embodiment provided by the present invention, the step of selecting fatigue intervention music and sending it to the target vehicle when it is determined that there is no following vehicle, and performing anti-fatigue music intervention on the target driver, specifically includes the following steps: When it is determined that there is no following vehicle, a fatigue intervention level is matched according to the fatigue accumulation degree; Based on the fatigue intervention level, determine the BPM intervention range; Select fatigue intervention music based on the BPM intervention range; Send the fatigue intervention music to the target vehicle; The fatigue intervention music is played in the target vehicle to provide anti-fatigue music intervention for the target driver in the target vehicle.

[0044] Furthermore, Figure 2 The diagram illustrates the application architecture of the long highway tunnel driver fatigue accumulation prediction and music intervention system provided in this embodiment of the invention.

[0045] Specifically, in another preferred embodiment of the present invention, a system for predicting cumulative driver fatigue in long highway tunnels and providing music intervention includes: The suspected fatigue state identification module 101 is used to identify target vehicles entering the long highway tunnel, acquire fatigue monitoring data, and identify and determine whether the target driver is in a suspected fatigue state.

[0046] In this embodiment of the invention, after a vehicle enters a long highway tunnel, the suspected fatigue state identification module 101 marks it as a target vehicle. With pre-existing fatigue intervention permissions for the target vehicle, a temporary target communication connection is established with the target vehicle. Simultaneously, a fatigue monitoring request is generated and sent to the target vehicle via the target communication connection. Upon receiving the fatigue monitoring request, the target vehicle transmits fatigue monitoring data back. By receiving the fatigue monitoring data transmitted from the target vehicle, the module extracts the target driver's eye-closing monitoring data from the fatigue monitoring data. Then, it identifies the eye-closing monitoring data to determine whether the target driver is in a suspected fatigue state. Specifically, by identifying the eye-closing frequency and duration of each eye-closing event in the eye-closing monitoring data, if neither the frequency nor the duration exceeds a corresponding preset threshold, the target driver is determined not to be in a suspected fatigue state; if the eye-closing frequency exceeds the corresponding preset threshold, but the duration does not, the target driver is determined to be in a suspected fatigue state; if the duration exceeds the corresponding preset threshold, the target driver is determined to be in a confirmed fatigue state.

[0047] Specifically, in another preferred embodiment provided by the present invention, the suspected fatigue state identification module 101 specifically includes: The target vehicle identification unit is used to identify target vehicles entering long highway tunnels. The request sending unit is used to establish a target communication connection with the target vehicle, generate and send a fatigue monitoring request to the target vehicle. A data receiving unit is used to receive fatigue monitoring data transmitted back by the target vehicle; The data extraction unit is used to extract eye-closed monitoring data from the fatigue monitoring data; The data recognition unit is used to identify the closed-eye monitoring data and determine whether the target driver in the target vehicle is in a state of suspected fatigue.

[0048] Furthermore, the long highway tunnel driver fatigue accumulation prediction and music intervention system also includes: The fatigue accumulation calculation module 102 is used to calculate the fatigue accumulation based on the fatigue monitoring data when the target driver is in a suspected fatigue state, and compare it with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation.

[0049] In this embodiment of the invention, when the target driver is determined to be in a state of suspected fatigue, the fatigue accumulation calculation module 102 analyzes the fatigue monitoring data, calculates the fatigue accumulation degree, and compares the fatigue accumulation degree with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation. Specifically, if the fatigue accumulation degree is greater than the accumulation threshold, it is determined that there is an abnormality in fatigue accumulation; if the fatigue accumulation degree is not greater than the accumulation threshold, it is determined that there is no abnormality in fatigue accumulation. The formula for calculating the fatigue accumulation degree is: ; Where F(t) is the fatigue accumulation degree; Fo is the preset cumulative correction coefficient; t b The start time of suspected fatigue state; t c E is the current time; E is the eye-closing coefficient, which is determined by the target driver's eye-closing frequency and duration; D is the lane deviation coefficient, which is determined by the number of times the target vehicle deviates from its lane.

[0050] The following vehicle judgment module 103 is used to acquire road vehicle monitoring data and identify and judge whether there is a following vehicle when it is determined that there is an abnormality of fatigue accumulation.

[0051] In this embodiment of the invention, when fatigue accumulation anomaly is determined, the following vehicle judgment module 103 acquires road vehicle monitoring data and performs vehicle identification on the road vehicle monitoring data to determine the nearest vehicle that is in the same direction of travel as the target vehicle and is located behind the target vehicle. Then, based on the road vehicle monitoring data, it calculates the vehicle distance between the target vehicle and the nearest vehicle. By comparing the vehicle distance with a preset following threshold, it is determined whether there is a following vehicle. Specifically, when the vehicle distance is less than the following threshold, it is determined that there is a following vehicle, and at this time, the nearest vehicle is marked as a following vehicle; when the vehicle distance is not less than the following threshold, it is determined that there is no following vehicle.

[0052] The fatigue prevention assistance intervention module 104 is used to assist and control the following vehicle when it is determined that there is a following vehicle, and to perform fatigue prevention assistance intervention by sounding the horn and flashing the flashing light towards the target vehicle.

[0053] In this embodiment of the invention, when it is determined that there is a following vehicle, the fatigue prevention assistance intervention module 104 generates an assistance intervention signal and sends the assistance intervention signal to the following vehicle. At this time, based on the assistance intervention signal, the following vehicle can be controlled to perform assistance intervention, so that the following vehicle can honk its horn and flash its headlights, thereby realizing fatigue prevention assistance intervention by honking and flashing its horn towards the target vehicle.

[0054] The fatigue-prevention music intervention module 105 is used to select fatigue intervention music and send it to the target vehicle when it is determined that there is no following vehicle, so as to provide fatigue-prevention music intervention for the target driver.

[0055] In this embodiment of the invention, when it is determined that there is no following vehicle, the fatigue prevention music intervention module 105 matches the fatigue intervention level according to the fatigue accumulation, determines the BPM intervention range according to the fatigue intervention level, and then selects fatigue intervention music from the preset music database according to the BPM intervention range. By sending the fatigue intervention music to the target vehicle, the fatigue intervention music is played in the target vehicle, thereby realizing fatigue prevention music intervention for the target driver in the target vehicle.

[0056] Specifically, in another preferred embodiment provided by the present invention, the anti-fatigue music intervention module 105 specifically includes: A level matching unit is used to match a fatigue intervention level based on the fatigue accumulation degree when it is determined that there is no following vehicle. The interval determination unit is used to determine the BPM intervention interval based on the fatigue intervention level. The music selection unit is used to select fatigue intervention music according to the BPM intervention range; A music transmitting unit is used to transmit the fatigue intervention music to the target vehicle; The music intervention unit is used to play the fatigue intervention music in the target vehicle to provide anti-fatigue music intervention for the target driver in the target vehicle.

[0057] The above-described embodiments are merely examples of several implementations of the present invention, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the present invention. For those skilled in the art, various modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A method for predicting cumulative driver fatigue in long highway tunnels and using music intervention, characterized in that, The method specifically includes the following steps: Identify target vehicles entering long highway tunnels, acquire fatigue monitoring data, and determine whether the target driver is in a state of suspected fatigue. When the target driver is in a state of suspected fatigue, the fatigue accumulation degree is calculated based on the fatigue monitoring data and compared with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation. When fatigue accumulation anomalies are detected, road vehicle monitoring data is acquired to identify and determine whether there are following vehicles. When a following vehicle is detected, assistance intervention control is applied to the following vehicle, and anti-fatigue assistance intervention is applied to the target vehicle by honking the horn and flashing the flashing light. When it is determined that there is no following vehicle, fatigue intervention music is selected and sent to the target vehicle to provide anti-fatigue music intervention for the target driver.

2. The method for predicting cumulative driver fatigue in long highway tunnels and using music intervention as described in claim 1, characterized in that, The process of identifying target vehicles entering long highway tunnels, acquiring fatigue monitoring data, and determining whether the target driver is in a state of suspected fatigue includes the following steps: Identify the target vehicles entering the long highway tunnel; Establish a target communication connection with the target vehicle, and generate and send a fatigue monitoring request to the target vehicle; Receive fatigue monitoring data transmitted back from the target vehicle; Extract eye-closed monitoring data from the fatigue monitoring data; The closed-eye monitoring data is identified to determine whether the target driver in the target vehicle is in a state of suspected fatigue.

3. The method for predicting cumulative driver fatigue in long highway tunnels and using music intervention as described in claim 1, characterized in that, The process of calculating the cumulative fatigue level based on the fatigue monitoring data when the target driver is in a suspected state of fatigue, and comparing it with a preset cumulative threshold to determine whether there is an abnormality in fatigue accumulation, specifically includes the following steps: When the target driver is in a state of suspected fatigue, the cumulative fatigue level is calculated based on the fatigue monitoring data. The fatigue accumulation is compared with a preset accumulation threshold; When the fatigue accumulation degree is greater than the accumulation threshold, it is determined that there is a fatigue accumulation anomaly; When the fatigue accumulation degree is not greater than the accumulation threshold, it is determined that there is no fatigue accumulation abnormality.

4. The method for predicting cumulative driver fatigue in long highway tunnels and using music intervention as described in claim 2, characterized in that, The formula for calculating the cumulative fatigue degree is as follows: ； Where F(t) is the fatigue accumulation degree; Fo is the preset cumulative correction coefficient; t b The start time of suspected fatigue state; t c E is the current time; E is the eye-closing coefficient, which is determined by the target driver's eye-closing frequency and duration; D is the lane deviation coefficient, which is determined by the number of times the target vehicle deviates from its lane.

5. The method for predicting cumulative driver fatigue in long highway tunnels and using music intervention as described in claim 1, characterized in that, When an abnormality of fatigue accumulation is determined, acquiring road vehicle monitoring data and identifying and determining whether there is a following vehicle specifically includes the following steps: When fatigue accumulation anomalies are detected, road vehicle monitoring data is acquired; Vehicle identification is performed on the road vehicle monitoring data to determine the nearest vehicle following the target vehicle; Based on the road vehicle monitoring data, the vehicle distance between the target vehicle and the nearest vehicle is calculated; The vehicle distance is compared with a preset following threshold; When the distance to the vehicle is less than the following threshold, the nearest vehicle is determined to be the following vehicle; If the distance to the vehicle is not less than the following threshold, it is determined that there is no following vehicle.

6. The method for predicting cumulative driver fatigue in long highway tunnels and using music intervention as described in claim 1, characterized in that, When a following vehicle is detected, the step of providing assistance and intervention control to the following vehicle, including honking the horn and flashing the flashing light to the target vehicle to prevent fatigue, specifically includes the following steps: When a vehicle is detected as following, an intervention signal is generated. The assistance intervention signal is sent to the following vehicle; Based on the aforementioned assistance intervention signal, assistance intervention control is applied to the following vehicle, and anti-fatigue assistance intervention is performed by honking and flashing a horn towards the target vehicle.

7. The method for predicting cumulative driver fatigue in long highway tunnels and using music intervention as described in claim 1, characterized in that, When it is determined that there is no following vehicle, selecting fatigue intervention music and sending it to the target vehicle to provide fatigue prevention music intervention for the target driver specifically includes the following steps: When it is determined that there is no following vehicle, a fatigue intervention level is matched according to the fatigue accumulation degree; Based on the fatigue intervention level, determine the BPM intervention range; Select fatigue intervention music based on the BPM intervention range; Send the fatigue intervention music to the target vehicle; The fatigue intervention music is played in the target vehicle to provide anti-fatigue music intervention for the target driver in the target vehicle.

8. A system for predicting cumulative driver fatigue in long highway tunnels and incorporating music intervention, characterized in that: The system includes a suspected fatigue state identification module, a fatigue accumulation calculation module, a vehicle following judgment module, a fatigue prevention assistance intervention module, and a fatigue prevention music intervention module, wherein: The suspected fatigue state identification module is used to identify target vehicles entering long highway tunnels, acquire fatigue monitoring data, and identify and determine whether the target driver is in a suspected fatigue state. The fatigue accumulation calculation module is used to calculate the fatigue accumulation based on the fatigue monitoring data when the target driver is in a suspected fatigue state, and compare it with a preset accumulation threshold to determine whether there is an abnormality in fatigue accumulation. The following vehicle detection module is used to acquire road vehicle monitoring data and identify and determine whether there is a following vehicle when fatigue accumulation abnormality is detected. The fatigue prevention assistance intervention module is used to assist and control the following vehicle when it is determined that there is a following vehicle, and to perform anti-fatigue assistance intervention by sounding the horn and flashing the flashing light to the target vehicle. The fatigue-prevention music intervention module is used to select fatigue intervention music and send it to the target vehicle when it is determined that there is no following vehicle, so as to provide fatigue-prevention music intervention for the target driver.

9. The driver fatigue accumulation prediction and music intervention system for long highway tunnels according to claim 8, characterized in that, The suspected fatigue state identification module specifically includes: The target vehicle identification unit is used to identify target vehicles entering long highway tunnels. The request sending unit is used to establish a target communication connection with the target vehicle, generate and send a fatigue monitoring request to the target vehicle. A data receiving unit is used to receive fatigue monitoring data transmitted back by the target vehicle; The data extraction unit is used to extract eye-closed monitoring data from the fatigue monitoring data; The data recognition unit is used to identify the closed-eye monitoring data and determine whether the target driver in the target vehicle is in a state of suspected fatigue.

10. The driver fatigue accumulation prediction and music intervention system for long highway tunnels according to claim 8, characterized in that, The fatigue-prevention music intervention module specifically includes: A level matching unit is used to match a fatigue intervention level based on the fatigue accumulation degree when it is determined that there is no following vehicle. The interval determination unit is used to determine the BPM intervention interval based on the fatigue intervention level. The music selection unit is used to select fatigue intervention music according to the BPM intervention range; A music transmitting unit is used to transmit the fatigue intervention music to the target vehicle; The music intervention unit is used to play the fatigue intervention music in the target vehicle to provide anti-fatigue music intervention for the target driver in the target vehicle.

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