Comprehensive guarantee method for traffic safety of dangerous road section under cooperation of vehicle cloud and road cloud

Abstract

The invention provides a dangerous road section traffic safety comprehensive guarantee method under vehicle and road cloud cooperation. The method comprises the following steps: S1, carrying out information acquisition; s2, carrying out risk level evaluation on the collected information; s3, carrying out early warning reminding; according to the method, all vehicles driving in the coverage area of the traffic safety cloud are dynamically monitored through the traffic safety cloud, and risk grade evaluation is carried out on the vehicles according to vehicle type evaluation parameters, meteorological evaluation parameters and road surface detection evaluation parameters; and corresponding early warning contents are matched based on different risk levels, and early warning reminding is carried out on a driver, so that overall traffic safety guarantee under vehicle-road cloud cooperation is realized. The accident rate can be effectively reduced, and particularly traffic accidents caused by severe weather or road slippage and the like are avoided; according to the risk levels of different vehicles, the system can match proper early warning content in real time, and excessive interference or wrong early warning on a driver is avoided.

Description

Technical Field

[0001] The present invention relates to the field of traffic safety assurance, and in particular to a comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration. Background Art

[0002] With the rapid development of intelligent connected vehicle technology, vehicle-road-cloud integrated collaboration as the infrastructure for the development of intelligent connected vehicles has also become an industry hotspot, playing a vital role in promoting the development of the industry; however, although the vehicle-road-cloud collaborative system has shown great potential in improving traffic convenience, travel efficiency, and reducing traffic congestion, it still has the following drawbacks in terms of road safety: Relying on a fixed risk assessment model, it is difficult to make detailed risk assessments for different vehicle types, different road conditions, and different weather conditions; 2. Traditional speed limit rules are usually static and cannot be dynamically adjusted according to actual weather, road conditions or traffic flow, which may cause safety hazards in extreme weather or special road conditions; Summary of the invention

[0003] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a comprehensive method for ensuring traffic safety on dangerous roads under the collaboration of vehicle, road and cloud.

[0004] To achieve the above object, the present invention adopts the following technical solutions: Based on a comprehensive traffic safety guarantee system for dangerous sections under the coordination of vehicle, road and cloud, it includes information collection module, edge computing module and early warning reminder module; The information collection module collects information through traffic intelligent equipment; The edge computing module receives the transmitted information and evaluates the risk level. The warning reminder module receives the warning content, transmits the warning content to the vehicle display screen, and gives the driver a warning reminder on the vehicle display screen.

[0005] A comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration includes the following steps: S1: collect information; The information collection module collects information through traffic intelligent equipment; Configure intelligent traffic equipment at road intersections in advance to collect weather information at intersections and information about vehicles passing through them; The intelligent devices include non-motor vehicle persuasion robot version of traffic safety piles, weather version of traffic safety piles, speedometers, bayonet capture cameras, etc.; The meteorological information includes rainfall conditions, visibility, temperature, etc.; The vehicle information passing through the intersection includes vehicle location, speed, vehicle model, license plate, etc.; Specifically, the non-motor vehicle persuasion robot version of the traffic safety pole collects information such as whether the non-motor vehicle rider wears a helmet, whether the non-motor vehicle carries passengers illegally, and whether the non-motor vehicle is equipped with shielding facilities; The meteorological version of the traffic safety pile collects information such as rainfall, visibility, temperature, wind speed, road ice thickness or water depth at the intersection; the speed, model, license plate and other information of vehicles passing through the intersection are collected through the speedometer; Collect traffic violation images in real time through camera capture devices; The information collection module transmits the collected information to the edge computing module; S2: Conduct risk level assessment on the collected information; The edge computing module receives the transmitted information and evaluates the risk level, and the edge computing module includes a dynamic speed limit model and a targeted warning information release content model; The following sub-steps are included: S21: Obtaining the actual speed limit value of the vehicle on the current road through a dynamic speed limit model; The dynamic speed limit model is as follows: ; in, is the standard speed limit value of the current road section under normal traffic conditions. known; is the meteorological assessment parameter, Evaluate parameters for vehicle types, Evaluate parameters for road surface detection, , ; Specifically, meteorological assessment parameters The value of is obtained by weighted calculation, and the calculation method is as follows: =w1*road ice thickness or water depth+w2*visibility+w3*rainfall+w4*temperature+w5*humidity+w6*wind speed; Among them, w1, w2, w3, w4, w5, and w6 are preset weight coefficients. Meteorological information includes but is not limited to road ice thickness or water depth, visibility, rainfall, temperature, humidity, and wind speed. Engineers adjust the weight coefficients accordingly based on actual meteorological information; Vehicle Type Assessment Parameters The value of corresponds to the vehicle type, which includes ordinary vehicles (such as sedans, etc.), large buses (such as long-distance passenger vehicles, etc.), large trucks (such as heavy trucks, trucks, etc.), and hazardous chemical vehicles (such as vehicles transporting hazardous chemicals, etc.); different vehicle types are set in advance according to actual vehicle standards. The value of The road surface detection evaluation parameters are related to the road intersection type, road congestion coefficient, and meteorological evaluation parameters corresponding to the road; =y1*road congestion coefficient+y2*intersection type coefficient+y3*meteorological assessment parameter ; The road congestion coefficient = current traffic flow / maximum road carrying capacity, wherein the current traffic flow is obtained by combining the passing vehicles collected by the camera at the traffic intersection with the video analysis technology, and the maximum road carrying capacity is known; the road intersection types include intersections, blind intersections, etc., and different coefficient values ​​are set in advance based on different road intersection types; y1, y2, y3 are preset weight coefficients; Based on the information collected in step S1, the corresponding , , value, and obtain the actual speed limit value of the vehicle on the current road through the dynamic speed limit model; S22: Determine the risk level and specific warning content according to the targeted warning information release content model function; The targeted warning information release content model is Function calculation risk level and warning content; The following sub-steps are included: S221: Passed The function determines the final risk value; Said ; Among them, a1, a2, and a3 are pre-set weight coefficients; The result obtained in step S21 , , Passing values ​​into functions The value obtained is the final risk value; S222: Determine the risk level and specific warning content based on the final risk value; pass Determine the level of risk; When , the risk level at this time is low risk; when When , the risk level is medium risk; when , the risk level at this time is high risk; in, , is a preset risk level classification threshold; The edge computing module matches the obtained risk level with the specific warning content in the warning content library; The warning content library contains different risk levels and warning contents; The edge computing module transmits the matched warning content to the warning reminder module; S3: Provide early warning reminder; The warning reminder module receives the warning content, transmits the warning content to the vehicle display screen, and gives the driver a warning reminder in the form of text or voice on the vehicle display screen.

[0006] Compared with the prior art, the present invention has the following beneficial effects: The method of the present invention uses the traffic safety cloud to dynamically monitor all vehicles traveling within its coverage area, and evaluates the risk level of the vehicle based on vehicle type evaluation parameters, meteorological evaluation parameters, and road surface detection evaluation parameters; and based on the matching of different risk levels with corresponding warning content, the driver is given a warning reminder, thereby achieving overall traffic safety protection under the coordination of vehicle, road, and cloud; The method of the present invention can effectively reduce the accident rate, especially avoid traffic accidents caused by bad weather or road slippage; according to the risk level of different vehicles, the system can match the appropriate warning content in real time to avoid excessive interference or false warnings to the driver. The traditional speed limit value is usually fixed. This method takes into account the real-time meteorological, road and traffic conditions through a dynamic speed limit model, so that the speed limit can be adjusted according to the actual situation, reducing the risk of accidents caused by weather changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flowchart of the steps of a comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration of the present invention. DETAILED DESCRIPTION

[0007] In order to provide a further understanding of the purpose, structure, features, and functions of the present invention, the following detailed description is given in conjunction with the embodiments.

[0008] Based on a comprehensive traffic safety guarantee system for dangerous sections under the coordination of vehicle, road and cloud, it includes information collection module, edge computing module and early warning reminder module; The information collection module collects information through traffic intelligent equipment; The edge computing module receives the transmitted information and evaluates the risk level. The warning reminder module receives the warning content, transmits the warning content to the vehicle display screen, and gives the driver a warning reminder on the vehicle display screen.

[0009] A comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration includes the following steps: S1: collect information; The information collection module collects information through traffic intelligent equipment; Configure intelligent traffic equipment at road intersections in advance to collect weather information at intersections and information about vehicles passing through them; The intelligent devices include non-motor vehicle persuasion robot version of traffic safety piles, weather version of traffic safety piles, speedometers, bayonet capture cameras, etc.; The meteorological information includes rainfall conditions, visibility, temperature, etc.; The vehicle information passing through the intersection includes vehicle location, speed, vehicle model, license plate, etc.; Specifically, the non-motor vehicle persuasion robot version of the traffic safety pole collects information such as whether the non-motor vehicle rider wears a helmet, whether the non-motor vehicle carries passengers illegally, and whether the non-motor vehicle is equipped with shielding facilities; For illegal behaviors such as speeding, not giving way to pedestrians, not wearing seat belts, talking on the phone, not wearing helmets on non-motor vehicles, installing awnings, carrying people, etc., the photos are captured and uploaded to the integrated command platform for non-site punishment; For example: if the vehicle speed is too fast, it will prompt you to slow down; if a large vehicle occupies the left lane, it will prompt you to drive to the right, etc., and the speeding vehicle will be photographed; non-motor vehicle violations such as not wearing a helmet, carrying passengers, and making phone calls will be photographed and voice and text reminders will be given on the spot.

[0010] Monitor the behavior of non-motor vehicles and discover their illegal behaviors in time. All captured and monitored information are uploaded, stored and analyzed through the integrated command platform, which helps the traffic management department to establish a complete traffic violation database while providing early warning prompts, which can effectively prevent the occurrence of traffic accidents.

[0011] The meteorological version of the traffic safety pile collects information such as rainfall, visibility, temperature, wind speed, road ice thickness or water depth at the intersection; the speed, model, license plate and other information of vehicles passing through the intersection are collected through the speedometer; Collect traffic violation images in real time through camera cameras; The information collection module transmits the collected information to the edge computing module; S2: Conduct risk level assessment on the collected information; The edge computing module receives the transmitted information and evaluates the risk level, and the edge computing module includes a dynamic speed limit model and a targeted warning information release content model; The following sub-steps are included: S21: Obtaining the actual speed limit value of the vehicle on the current road through a dynamic speed limit model; The dynamic speed limit model is as follows: ; in, is the standard speed limit value of the current road section under normal traffic conditions. known; is the meteorological assessment parameter, Evaluate parameters for vehicle types, Evaluate parameters for road surface detection, , ; Specifically, meteorological assessment parameters The value of is obtained by weighted calculation, and the calculation method is as follows: =w1*road ice thickness or water depth+w2*visibility+w3*rainfall+w4*temperature+w5*humidity+w6*wind speed; Among them, w1, w2, w3, w4, w5, and w6 are preset weight coefficients. Meteorological information includes but is not limited to road ice thickness or water depth, visibility, rainfall, temperature, humidity, and wind speed. Engineers adjust the weight coefficients accordingly based on actual meteorological information; Vehicle Type Assessment Parameters The value of corresponds to the vehicle type, which includes ordinary vehicles (such as sedans, etc.), large buses (such as long-distance passenger vehicles, etc.), large trucks (such as heavy trucks, trucks, etc.), and hazardous chemical vehicles (such as vehicles transporting hazardous chemicals, etc.); different vehicle types are set in advance according to actual vehicle standards. The value of The road surface detection evaluation parameters are related to the road intersection type, road congestion coefficient, and meteorological evaluation parameters corresponding to the road; =y1*road congestion coefficient+y2*intersection type coefficient+y3*meteorological assessment parameter ; The road congestion coefficient = current traffic flow / maximum road carrying capacity, wherein the current traffic flow is obtained by combining the passing vehicles collected by the camera at the traffic intersection with the video analysis technology, and the maximum road carrying capacity is known; the road intersection types include intersections, blind intersections, etc., and different coefficient values ​​are set in advance based on different road intersection types; y1, y2, y3 are preset weight coefficients; Based on the information collected in step S1, the corresponding , , value, and obtain the actual speed limit value of the vehicle on the current road through the dynamic speed limit model; For example: Under normal circumstances, the speed limit on a certain national or provincial highway is The current weather condition is heavy rain without water accumulation, which is the weather assessment parameter. is 0.2, the vehicle that passed is a large passenger car, i.e. the vehicle type assessment parameter 0.15, road detection evaluation parameters with more vehicles on the road is 0.12; The dynamic speed limit values ​​are: ; After rounding, the dynamic speed limit value is: 50 km / h; S22: Determine the risk level and specific warning content according to the targeted warning information release content model function; The targeted warning information release content model is Function calculation risk level and warning content; The following sub-steps are included: S221: Passed The function determines the final risk value; Said ; Among them, a1, a2, and a3 are pre-set weight coefficients; The result obtained in step S21 , , Passing values ​​into functions The value obtained is the final risk value; S222: Determine the risk level and specific warning content based on the final risk value; pass Determine the level of risk; When , the risk level at this time is low risk; when When , the risk level is medium risk; when , the risk level at this time is high risk; in, , is a preset risk level classification threshold; The edge computing module matches the obtained risk level with the specific warning content in the warning content library; The warning content library contains different risk levels and warning contents; The edge computing module transmits the matched warning content to the warning reminder module; Preferably, the warning content library contains different risk levels, warning contents, and warning prompt forms; the warning contents contain different warning types, including speed limit type, weather type, road condition type, specific vehicle type type, etc.; The warning contents corresponding to different risk levels all include warning types, and the warning contents corresponding to different risk levels are different; Match the specific warning type according to the set weight and the calculated parameter value; the parameter value includes the meteorological assessment parameter , Vehicle type evaluation parameters , Road surface detection evaluation parameters wait; Specifically, the priority of warning types is: speed limit type > weather type = road condition type = specific vehicle type type; If the speed of the vehicle is greater than the actual speed limit of the vehicle on the current road obtained in step S21, the speed limit warning information in the warning content corresponding to the specific risk level is selected for reminder; if the speed of the vehicle is less than the actual speed limit of the vehicle on the current road obtained in step S21, the warning type information in the warning content corresponding to the specific risk level is determined in combination with the parameter value obtained in step S21 and the set weight value for reminder; The parameter values ​​include meteorological assessment parameters , vehicle type assessment parameters , Road surface detection evaluation parameters wait.

[0012] Data is collected in real time through sensors and processed locally through edge computing modules, reducing delays and dependence on central servers and ensuring the system's response speed. The dynamic speed limit and targeted warning models take into account multiple factors such as road weather, traffic flow, and road conditions, making risk assessment more accurate, issuing warnings in a timely manner, and improving the efficiency of responding to emergencies.

[0013] S3: Provide early warning reminder; The warning reminder module receives the warning content, transmits the warning content to the vehicle computer display screen, and gives the driver a warning reminder on the vehicle computer display screen.

[0014] Preferably, the driver is warned by warning reminders such as text, voice, active intervention, sound and light alarms and other enhanced warning devices; The warning content is displayed in text form on the vehicle display screen to inform the driver of the current risk level and corresponding warning information; The warning content is broadcast in voice form through the vehicle voice system, so that the driver can receive the warning information in time while focusing on driving; In the case of high danger levels, active intervention measures such as deceleration and turning on safety warning lights are used to remind the driver; Based on the risk level of different road sections, the system will decide whether to enable enhanced warning equipment, such as sound and light alarms. For some high-risk sections or emergency situations, the system will automatically trigger sound and light alarms or other appropriate warning forms based on the specific road environment and the number and type of warning equipment.

[0015] By providing early warning information in a timely manner, drivers can react before risks occur and effectively avoid accidents. For sections of road with higher danger levels, interventions such as active deceleration and turning on warning lights can alert drivers to potential dangers, avoid sudden braking or misoperation, and thus reduce the occurrence of traffic accidents.

[0016] Through the combination of text, voice, active intervention and other forms, it can provide the most suitable warning method for different driving environments and drivers' needs; for example, in a noisy urban environment, sound and light alarms are more effective than text and voice prompts.

[0017] The present invention has been described by the above-mentioned relevant embodiments, however, the above-mentioned embodiments are only examples for implementing the present invention. It must be pointed out that the disclosed embodiments do not limit the scope of the present invention. On the contrary, changes and modifications made without departing from the spirit and scope of the present invention are all within the scope of patent protection of the present invention.

Claims

1. A comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration, characterized by: The following steps are involved: S1: collect information; The information collection module collects information through traffic intelligent equipment; Configure intelligent traffic equipment at road intersections in advance to collect weather information at intersections and information about vehicles passing through them; The information collection module transmits the collected information to the edge computing module; S2: Conduct risk level assessment on the collected information; The edge computing module receives the transmitted information and evaluates the risk level, and the edge computing module includes a dynamic speed limit model and a targeted warning information release content model; The following sub-steps are included: S21: Obtaining the actual speed limit value of the vehicle on the current road through a dynamic speed limit model; The dynamic speed limit model is as follows: ； in, is the standard speed limit value of the current road section under normal traffic conditions. known; is the meteorological assessment parameter, Evaluate parameters for vehicle types, Evaluate parameters for road surface detection, , ; Based on the information collected in step S1, the corresponding , , value, and obtain the actual speed limit value of the vehicle on the current road through the dynamic speed limit model; S22: Determine the risk level and specific warning content according to the targeted warning information release content model function; The targeted warning information release content model is Function calculation risk level and warning content; The following sub-steps are included: S221: Passed The function determines the final risk value; Said ; Among them, a1, a2, and a3 are pre-set weight coefficients; The result obtained in step S21 , , Passing values ​​into functions The value obtained is the final risk value; S222: Determine the risk level and specific warning content based on the final risk value; pass Determine the level of risk; The edge computing module matches the obtained risk level with the specific warning content in the warning content library; The warning content library contains different risk levels and warning contents; The edge computing module transmits the matched warning content to the warning reminder module; S3: Provide early warning reminder; The warning reminder module receives the warning content, transmits the warning content to the vehicle computer display screen, and gives the driver a warning reminder on the vehicle computer display screen.

2. The comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration as claimed in claim 1, characterized in that: In step S1, the intelligent devices include a non-motor vehicle persuasion robot version of a traffic safety post, a weather version of a traffic safety post, a speedometer, and a bayonet capture camera; The meteorological information includes rainfall conditions, visibility, and temperature; The vehicle information passing through the intersection includes vehicle location, speed, vehicle model, and license plate; Specifically, the non-motor vehicle persuasion robot version of the traffic safety pole collects information on whether non-motor vehicle riders wear helmets, whether non-motor vehicles carry passengers illegally, and whether non-motor vehicles are equipped with shielding facilities; Collect information on intersection rainfall, visibility, temperature, wind speed, road ice thickness or water depth through weather-related traffic safety posts; The speed, model and license plate information of vehicles passing through the intersection are collected through the speedometer; Traffic violation images are collected in real time through the camera.

3. The comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration as claimed in claim 1, characterized in that: In step S21, the meteorological assessment parameters The value of is obtained by weighted calculation, and the calculation method is as follows: =w1*road ice thickness or water depth+w2*visibility+w3*rainfall+w4*temperature+w5*humidity+w6*wind speed; Among them, w1, w2, w3, w4, w5, and w6 are preset weight coefficients. Meteorological information includes but is not limited to road ice thickness or water depth, visibility, rainfall, temperature, humidity, and wind speed. Engineers adjust the weight coefficients accordingly based on actual meteorological information; Vehicle Type Assessment Parameters The value of corresponds to the vehicle type, which includes ordinary vehicles, large passenger vehicles, large trucks, and hazardous chemicals vehicles; different vehicle types are set in advance according to actual vehicle standards. The value of The road surface detection evaluation parameters are related to the road intersection type, road congestion coefficient, and meteorological evaluation parameters corresponding to the road; =y1*road congestion coefficient+y2*intersection type coefficient+y3*meteorological assessment parameter ; The road congestion coefficient = current traffic flow / maximum road carrying capacity, wherein the current traffic flow is obtained by combining the passing vehicles collected by the camera at the traffic intersection with the video analysis technology, and the maximum road carrying capacity is known; the road intersection types include intersections and blind spot intersections, and different coefficient values ​​are set in advance based on different road intersection types; y1, y2, and y3 are preset weight coefficients.

4. The comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration as claimed in claim 1, characterized in that: In step S222, when , the risk level at this time is low risk; when , the risk level at this time is medium risk; when , the risk level at this time is high risk; in, , It is the preset risk level classification threshold.

5. The comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration as claimed in claim 4, characterized in that: The warning content library contains different risk levels, warning contents, and warning prompt forms; the warning contents contain different warning types, including speed limit, weather, road condition, and specific vehicle type; The warning contents corresponding to different risk levels all include warning types, and the warning contents corresponding to different risk levels are different; Match the specific warning type according to the set weight and the calculated parameter value; specifically, the warning type priority is: speed limit type > weather type = road condition type = specific vehicle type type; If the speed of the vehicle is greater than the actual speed limit of the vehicle on the current road obtained in step S21, the speed limit warning information in the warning content corresponding to the specific risk level is selected for reminder; if the speed of the vehicle is less than the actual speed limit of the vehicle on the current road obtained in step S21, the warning type information in the warning content corresponding to the specific risk level is determined in combination with the parameter value obtained in step S21 and the set weight value for reminder.

6. The comprehensive method for ensuring traffic safety on dangerous roads under vehicle-road-cloud collaboration as claimed in claim 1, characterized in that: In step S3, the driver is given a warning reminder in the form of text, voice, active intervention, and warning reminders of sound and light alarm enhanced warning equipment; The warning content is displayed in text form on the vehicle display screen to inform the driver of the current risk level and corresponding warning information; The warning content is broadcast in voice form through the vehicle voice system, so that the driver can receive the warning information in time while focusing on driving; In the case of high danger level, active intervention measures such as slowing down and turning on the safety warning lights are used to remind the driver; Based on the risk level of different road sections, the system will decide whether to enable enhanced warning devices; for some high-risk sections or emergency situations, the system will automatically trigger sound and light alarms or other appropriate warning forms based on the specific road environment and the number and type of warning devices.

7. A comprehensive traffic safety guarantee system for dangerous sections under the coordination of vehicle, road and cloud, used to implement the comprehensive traffic safety guarantee method for dangerous sections under the coordination of vehicle, road and cloud as described in any one of claims 1 to 6, characterized in that: Contains information collection module, edge computing module, and early warning reminder module; The information collection module collects information through traffic intelligent equipment; The edge computing module receives the transmitted information and evaluates the risk level. The warning reminder module receives the warning content, transmits the warning content to the vehicle display screen, and gives the driver a warning reminder on the vehicle display screen.

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