ETC vehicle-road cooperation device and system

Abstract

The application discloses an ETC vehicle-road cooperation device and system, relates to the technical field of vehicle-road cooperation, and aims to solve the technical problem that the existing vehicle-road cooperation device and system lack intelligent evaluation and active intervention mechanism for road wet and slippery risk under special weather, and comprises the following: a road test module, which is provided with a plurality of groups and is equidistantly distributed along the road; a central processing module, which is connected to a cloud processor, is used for receiving image, speed and distance information uploaded by the road test module, judging road wet and slippery condition, calculating wet and slippery road length, road wet and slippery degree quantitative value and skidding risk grade, and sending the skidding risk grade, wet and slippery road length information and vehicle and wet and slippery road distance information to an ETC vehicle-mounted device; and the ETC vehicle-mounted device, which is installed on a vehicle, is used for receiving information sent by the central processing module and performing voice broadcast. The application has the advantages of intelligent evaluation and active intervention for road wet and slippery risk under special weather.

Description

Technical Field

[0001] This invention relates to the field of vehicle-road cooperative technology, and more specifically, to an ETC vehicle-road cooperative device and system. Background Technology

[0002] ETC vehicle-road cooperative devices and systems are a new generation of intelligent transportation solutions that integrate electronic non-stop toll collection technology and vehicle-road cooperative technology. They aim to improve traffic efficiency, safety and service experience through information interaction between vehicles and road infrastructure.

[0003] In existing technologies, road slippery detection mainly relies on manual inspections or single-point sensor equipment, making it difficult to achieve real-time monitoring and dynamic early warning across the entire highway. Traditional vehicle anti-skid systems only judge local road conditions through onboard sensors and cannot predict slippery areas ahead. At the same time, existing vehicle-road cooperative technologies mostly focus on traffic flow management and toll collection, lacking intelligent assessment and proactive intervention mechanisms for road slippery risks under special weather conditions.

[0004] In view of this, we propose an ETC vehicle-road cooperative device and system. Summary of the Invention

[0005] The purpose of this invention is to provide an ETC vehicle-road cooperative device and system to solve the technical problem that existing vehicle-road cooperative devices and systems lack intelligent assessment and active intervention mechanisms for the risk of slippery roads under special weather conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an ETC vehicle-road cooperative device and system, comprising:

[0007] The road test module consists of several groups, equidistantly distributed along the road.

[0008] The central processing module, connected to the cloud processor, is used to receive images, speed and spacing information uploaded by the road test module, determine the road slipperiness, calculate the length of the slippery road, the quantitative value of the road slipperiness and the skid risk level, and send the skid risk level, slippery road length information and the distance information between the vehicle and the slippery road to the ETC on-board unit.

[0009] ETC (Electronic Toll Collection) devices are installed in vehicles to receive information from the central processing module and make voice announcements.

[0010] The drive test module includes:

[0011] The video acquisition module is used to acquire road image information and transmit it to the central processing module;

[0012] The speed acquisition module is used to detect the speed of passing vehicles and upload it to the central processing module;

[0013] The positioning module is used to determine the distance between the vehicle's position and the slippery road and send it to the central processing module.

[0014] Preferably, the video acquisition module is a high-resolution, low-light, and wide dynamic range HD camera with intelligent image stabilization. It also incorporates an image enhancement algorithm to automatically optimize image contrast, brightness, and color saturation. Based on a histogram equalization algorithm, it homogenizes the grayscale histogram of the image. Let the grayscale value of the original image be... Gray values ​​after histogram equalization The calculation formula is:

[0015] ;

[0016] in, This represents the total number of gray levels in the image. The total number of pixels in the image. The grayscale value is The number of pixels;

[0017] The speed acquisition module is a radar speedometer, based on the Doppler effect principle, which detects the speed of a vehicle traveling at a speed of... The frequency of the reflected wave received by the radar when in motion With the frequency of the transmitted wave The relationship between them is:

[0018] ;

[0019] in, At the speed of light, The angle between the radar wave and the vehicle's direction of motion is determined by measuring the frequency difference. The vehicle's speed can then be calculated. :

[0020] .

[0021] Preferably, the positioning module determines the vehicle's position coordinates when the vehicle travels to the roadside module, and the vehicle position coordinates determined by the positioning module are set as follows: The central processing module determines the road's slippery condition, and the road testing modules at both ends of the slippery road determine the starting coordinates of the slippery road. and endpoint coordinates And calculate the distance from the vehicle's position to the starting point of the slippery road. The calculation formula is as follows:

[0022] .

[0023] Preferably, after receiving image information from the video acquisition module, the central processing module uses a deep learning image recognition algorithm based on a neural network model to intelligently analyze the road surface texture, reflective properties, and water accumulation areas in the image to determine the road slipperiness. The specific method is as follows:

[0024] Let the input image be After processing by convolutional layers, pooling layers, and fully connected layers, the output slippery state judgment result is: The calculation formula is as follows:

[0025] ;

[0026] in, and The first Layer weight matrix and bias vector, For activation function, ;

[0027] When the road is determined to be slippery, the central processing module immediately uses the positioning module to determine the number of road testing modules located on the slippery road. The spacing between each road testing module is known to be a fixed value. The length of the slippery road It is calculated using the following formula:

[0028] ;

[0029] in, This represents the number of road test modules located on slippery roads.

[0030] Preferably, the quantification value of road slipperiness The calculation method is as follows:

[0031] First, the number of pixels in the wet / slippery area of ​​the image is identified using an image recognition algorithm. and total number of pixels in the image The percentage of slippery areas was obtained. ;

[0032] Meanwhile, based on the reflection intensity analysis of the image, the quantified value of reflection intensity is calculated by comparing the average brightness of the reflective area in the image with the average brightness of the normal road surface. ;

[0033] Let the average brightness of a normal road surface be... The average brightness of the reflective area is ,but ;

[0034] The quantitative value of road slipperiness The calculation formula is: ,in, and These are the weighting coefficients, and .

[0035] Preferably, the method for calculating the slippage risk level is as follows:

[0036] After receiving the vehicle's current speed information uploaded by the speed acquisition module, a skid risk assessment model is established, taking into account factors such as road slipperiness, vehicle speed, and vehicle type, to calculate the skid risk level of the vehicle when it subsequently travels on a slippery road surface.

[0037] Let the vehicle's speed be... The vehicle model coefficient is Slipping risk level The calculation formula is:

[0038] ;

[0039] in, , and These are the weighting coefficients. For constant terms;

[0040] The risk level of slippage is divided into three levels: low, medium and high, with each level corresponding to a different risk threshold range.

[0041] when The risk level was low at the time.

[0042] when The risk level was medium at the time.

[0043] when It was at a high-risk level at the time.

[0044] Preferably, after receiving the distance information between the vehicle position and the slippery road determined by the positioning module, the central processing module integrates and packages the slippery risk level, the length of the slippery road, and the distance information between the vehicle position and the slippery road, and sends them to the ETC on-board unit through a wireless communication link.

[0045] Preferably, after receiving information from the central processing module, the ETC on-board unit uses its built-in speech synthesis engine to broadcast information in a clear, loud voice with a moderate speaking speed. The broadcast content includes the skid risk level at the current vehicle speed, the length of the slippery road, and the distance between the vehicle's current position and the slippery road.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. This invention achieves continuous monitoring of the slippery condition of the entire road section by using multiple sets of road testing modules evenly distributed along the road, combined with video acquisition, speed detection, and positioning modules. The central processing module, based on a deep learning image recognition algorithm, can accurately determine the range of slippery areas, solving the problem of limited detection range in existing technologies.

[0048] 2. This invention innovatively constructs a quantitative value for the degree of slipperiness. Vehicle speed and vehicle model coefficient The skid risk assessment model achieves accurate calculation of risk level, thereby dynamically analyzing the skid risk at the current speed based on the vehicle's driving speed, and has higher predictive accuracy than traditional assessment methods that rely on only a single parameter.

[0049] 3. This invention also integrates and packages information on the risk level of slippage, the length of slippery road, and the distance between the vehicle and the slippery road by designing an ETC on-board unit. This information is then transmitted to the ETC on-board unit via a wireless communication link and broadcast through the built-in voice synthesis engine of the ETC on-board unit. This helps to provide drivers with information on slippery road surfaces and serves as an early warning. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0051] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0052] Example 1: As Figure 1 As shown, the present invention provides an ETC vehicle-road cooperative device and system, comprising:

[0053] The road test module consists of several groups, equidistantly distributed along the road.

[0054] The central processing module, connected to the cloud processor, is used to receive images, speed and spacing information uploaded by the road test module, determine the road slipperiness, calculate the length of the slippery road, the quantitative value of the road slipperiness and the skid risk level, and send the skid risk level, slippery road length information and the distance information between the vehicle and the slippery road to the ETC on-board unit.

[0055] ETC (Electronic Toll Collection) devices are installed in vehicles to receive information from the central processing module and make voice announcements.

[0056] The road test module includes:

[0057] The video acquisition module is used to acquire road image information and transmit it to the central processing module;

[0058] The speed acquisition module is used to detect the speed of passing vehicles and upload it to the central processing module;

[0059] The positioning module is used to determine the distance between the vehicle's position and the slippery road and send it to the central processing module.

[0060] In an embodiment of the present invention, the video acquisition module is a high-resolution, low-light, and wide dynamic range HD camera with intelligent image stabilization. It also incorporates an image enhancement algorithm to automatically optimize image contrast, brightness, and color saturation. Based on a histogram equalization algorithm, it homogenizes the grayscale histogram of the image. Let the grayscale value of the original image be... Gray values ​​after histogram equalization The calculation formula is:

[0061] ;

[0062] in, This represents the total number of gray levels in the image. The total number of pixels in the image. The grayscale value is The number of pixels;

[0063] The speed acquisition module is a radar speedometer, based on the Doppler effect principle, which detects the speed of a vehicle traveling at a speed of [missing information]. The frequency of the reflected wave received by the radar when in motion With the frequency of the transmitted wave The relationship between them is:

[0064] ;

[0065] in, At the speed of light, The angle between the radar wave and the vehicle's direction of motion is determined by measuring the frequency difference. The vehicle's speed can then be calculated. :

[0066] .

[0067] In an embodiment of the present invention, the positioning module determines the vehicle's position coordinates when the vehicle travels to the roadside module, and the vehicle position coordinates determined by the positioning module are set as follows: The central processing module determines the road's slippery condition, and the road testing modules at both ends of the slippery road determine the starting coordinates of the slippery road. and endpoint coordinates And calculate the distance from the vehicle's position to the starting point of the slippery road. and distance to the destination The calculation formula is as follows:

[0068] ;

[0069] .

[0070] In an embodiment of the present invention, after receiving image information from the video acquisition module, the central processing module uses a deep learning image recognition algorithm based on a neural network model to intelligently analyze the road surface texture, reflective properties, and water accumulation areas in the image to determine the road slipperiness. The specific method is as follows:

[0071] Let the input image be After processing by convolutional layers, pooling layers, and fully connected layers, the output slippery state judgment result is: The calculation formula is as follows:

[0072] ;

[0073] in, and The first Layer weight matrix and bias vector, For activation function, ;

[0074] When the road is determined to be slippery, the central processing module immediately uses the positioning module to determine the number of road testing modules located on the slippery road. The spacing between each road testing module is known to be a fixed value. The length of the slippery road It is calculated using the following formula:

[0075] ;

[0076] in, This represents the number of road test modules located on slippery roads.

[0077] In an embodiment of the present invention, the quantification value of road slipperiness is... The calculation method is as follows:

[0078] First, the number of pixels in the wet / slippery area of ​​the image is identified using an image recognition algorithm. and total number of pixels in the image The percentage of slippery areas was obtained. ;

[0079] Meanwhile, based on the reflection intensity analysis of the image, the quantified value of reflection intensity is calculated by comparing the average brightness of the reflective area in the image with the average brightness of the normal road surface. ;

[0080] Let the average brightness of a normal road surface be... The average brightness of the reflective area is ,but ;

[0081] The quantitative value of road slipperiness The calculation formula is: ,in, and These are the weighting coefficients, obtained through extensive experimental data and machine learning algorithm training. .

[0082] In an embodiment of the present invention, the method for calculating the slippage risk level is as follows:

[0083] After receiving the vehicle's current speed information uploaded by the speed acquisition module, a skid risk assessment model is established, taking into account factors such as road slipperiness, vehicle speed, and vehicle type, to calculate the skid risk level of the vehicle when it subsequently travels on a slippery road surface.

[0084] Let the vehicle's speed be... The vehicle model coefficient is Slipping risk level The calculation formula is:

[0085] ;

[0086] in, , and The weighting coefficients were obtained through extensive experimental data and training with machine learning algorithms. For constant terms;

[0087] The risk level of slippage is divided into three levels: low, medium and high, with each level corresponding to a different risk threshold range.

[0088] when The risk level was low at the time.

[0089] when The risk level was medium at the time.

[0090] when The risk level was high at the time.

[0091] and To determine the thresholds through actual accident data and simulation tests, the model uses machine learning technology to continuously optimize and adjust based on actual accident data and vehicle driving data, making the risk assessment results more in line with the actual situation. At the same time, the model also has an adaptive adjustment function, which can automatically adjust the weight coefficients according to the road characteristics and traffic conditions of different regions, thereby improving the accuracy of the assessment.

[0092] In an embodiment of the present invention, after receiving the distance information between the vehicle position and the slippery road determined by the positioning module, the central processing module integrates and packages the slippery risk level, the slippery road length information, and the distance information between the vehicle position and the slippery road, and sends them to the ETC on-board unit through a wireless communication link.

[0093] The wireless communication link employs channel coding and modulation techniques to divide the high-speed data stream into multiple low-speed sub-data streams, which are then modulated onto different subcarriers for transmission. Let the transmitted signal be... The modulated signal for:

[0094] ;

[0095] in, For symbol period, For subcarrier spacing, For the number of subcarriers, This is the start time.

[0096] In an embodiment of the present invention, after receiving information from the central processing module, the ETC on-board unit uses a built-in speech synthesis engine to broadcast information in a clear, loud and moderately paced voice. The broadcast content includes the skid risk level at the current vehicle speed, the length of the wet road, and the distance between the vehicle's current position and the wet road.

[0097] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. An ETC vehicle-road cooperative device and system, characterized in that, include: The road test module consists of several groups, equidistantly distributed along the road. The central processing module, connected to the cloud processor, is used to receive images, speed and spacing information uploaded by the road test module, determine the road slipperiness, calculate the length of the slippery road, the quantitative value of the road slipperiness and the skid risk level, and send the skid risk level, slippery road length information and the distance information between the vehicle and the slippery road to the ETC on-board unit. ETC (Electronic Toll Collection) devices are installed in vehicles to receive information from the central processing module and make voice announcements. The drive test module includes: The video acquisition module is used to acquire road image information and transmit it to the central processing module; The speed acquisition module is used to detect the speed of passing vehicles and upload it to the central processing module; The positioning module is used to determine the distance between the vehicle's position and the slippery road surface and send it to the central processing module. After receiving image information from the video acquisition module, the central processing module uses a deep learning image recognition algorithm based on a neural network model to intelligently analyze the road surface texture, reflective properties, and water accumulation areas in the image to determine the road slipperiness. The specific method is as follows: Let the input image be After processing by convolutional layers, pooling layers, and fully connected layers, the output slippery state judgment result is: The calculation formula is as follows: ； in, and The first The weight matrix and bias vector of the layer, For activation function, ; When the road is determined to be slippery, the central processing module immediately uses the positioning module to determine the number of road testing modules located on the slippery road. The spacing between each road testing module is known to be a fixed value. The length of the slippery road It is calculated using the following formula: ； in, The number of road test modules located on slippery roads; The quantitative value of road slipperiness The calculation method is as follows: First, the number of pixels in the wet / slippery area of ​​the image is identified using an image recognition algorithm. and total number of pixels in the image The percentage of slippery areas was obtained. ; Meanwhile, based on the reflection intensity analysis of the image, the quantified value of reflection intensity is calculated by comparing the average brightness of the reflective area in the image with the average brightness of the normal road surface. ; Let the average brightness of a normal road surface be... The average brightness of the reflective area is ,but ; The quantitative value of road slipperiness The calculation formula is: ,in, and These are the weighting coefficients, and ; The method for calculating the slippage risk level is as follows: After receiving the vehicle's current speed information uploaded by the speed acquisition module, a skid risk assessment model is established, taking into account factors such as road slipperiness, vehicle speed, and vehicle type, to calculate the skid risk level of the vehicle when it subsequently travels on a slippery road surface. Let the vehicle's speed be... The vehicle model coefficient is Slipping risk level The calculation formula is: ； in, , and These are the weighting coefficients. For constant terms; The risk level of slippage is divided into three levels: low, medium and high, with each level corresponding to a different risk threshold range. when The risk level was low at the time. when The risk level was medium at the time. when It was at a high-risk level at the time.

2. The ETC vehicle-road cooperative device and system according to claim 1, characterized in that, The video acquisition module is a high-resolution, low-light, and wide dynamic range HD camera with intelligent image stabilization. It also incorporates an image enhancement algorithm to automatically optimize image contrast, brightness, and color saturation. Based on a histogram equalization algorithm, it homogenizes the image's grayscale histogram. Let the original image's grayscale value be... Gray values ​​after histogram equalization The calculation formula is: ； in, This represents the total number of gray levels in the image. The total number of pixels in the image. The grayscale value is The number of pixels; The speed acquisition module is a radar speedometer, based on the Doppler effect principle, which detects the speed of a vehicle traveling at a speed of... The frequency of the reflected wave received by the radar when in motion With the frequency of the transmitted wave The relationship between them is: ； in, At the speed of light, The angle between the radar wave and the vehicle's direction of motion is determined by measuring the frequency difference. The vehicle's speed can then be calculated. : 。 3. The ETC vehicle-road cooperative device and system according to claim 2, characterized in that, The positioning module determines the vehicle's position coordinates when it approaches the roadside module. The vehicle's position coordinates determined by the positioning module are set as follows: The central processing module determines the road's slippery condition, and the road testing modules at both ends of the slippery road determine the starting coordinates of the slippery road. and endpoint coordinates And calculate the distance from the vehicle's position to the starting point of the slippery road. The calculation formula is as follows: 。 4. The ETC vehicle-road cooperative device and system according to claim 1, characterized in that, After receiving the distance information between the vehicle position and the slippery road determined by the positioning module, the central processing module integrates and packages the slippery risk level, the length of the slippery road, and the distance information between the vehicle position and the slippery road, and sends them to the ETC on-board unit through a wireless communication link.

5. An ETC vehicle-road cooperative device and system according to claim 4, characterized in that, After receiving information from the central processing module, the ETC on-board unit uses its built-in speech synthesis engine to broadcast information in a clear, loud voice at a moderate pace. The broadcast content includes the skid risk level at the current vehicle speed, the length of the slippery road, and the distance between the vehicle's current position and the slippery road.

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