A data processing method and system for vehicle-road cooperation early warning

Abstract

A kind of data processing method and system for vehicle-road cooperation early warning, comprising the following steps: target vehicle obtains vehicle end data;Road test monitoring unit obtains road test monitoring data;Road test monitoring data is divided into hierarchical data and basic data;According to hierarchical condition, part of basic data is transmitted to target vehicle;Target vehicle carries out risk identification and early warning according to vehicle end data and the part of basic data obtained.The application obtains part of basic data by vehicle end data and road test monitoring unit, ensures the timeliness of data acquisition, so as to provide data basis for the risk identification and early warning of target vehicle to obtain data more timely.

Description

Technical Field

[0001] This application relates to a data processing method and system for vehicle-road cooperative early warning. Background Technology

[0002] Vehicle-to-infrastructure (V2I) communication utilizes advanced wireless communication and next-generation internet technologies to implement comprehensive, real-time dynamic information exchange between vehicles and infrastructure. Based on the collection and fusion of dynamic traffic information across all times and spaces, it enables active vehicle safety control and cooperative road management, fully realizing effective collaboration between people, vehicles, and roads to ensure traffic safety, improve traffic efficiency, and ultimately form a safe, efficient, and environmentally friendly road traffic system. The sources of V2I communication are mainly twofold: road testing and the vehicles themselves. Currently, V2I communication is primarily used in driver assistance systems. During driver assistance, in the event of a hazard, the driver needs to be promptly alerted and control transferred to them. At present, the issuance of warnings is relatively mechanical, primarily based on collision time, which leads to a high incidence of false alarms. Existing data processing strategies cannot solve this problem. Summary of the Invention

[0003] To address the aforementioned problems, this application discloses a data processing method for vehicle-road cooperative early warning, comprising the following steps:

[0004] Acquire vehicle-side data for the target vehicle;

[0005] The road test monitoring unit acquires road test monitoring data;

[0006] The road test monitoring data is divided into graded data and basic data;

[0007] Based on the classification, some basic data will be transmitted to the target vehicle;

[0008] The target vehicle performs risk identification and early warning based on vehicle-side data and some basic data obtained. This application ensures the timeliness of data acquisition by using vehicle-side data and some basic data obtained by road test detection units, thus providing a data foundation for more timely data acquisition for risk identification and early warning of the target vehicle.

[0009] Preferably, the basic data is allocated according to the hierarchical data, and the basic data is divided into primary data and secondary data;

[0010] The primary data is transmitted to the target vehicle, and the secondary data is processed according to the basic data to obtain edge analysis data, which is then transmitted to the target vehicle. This application obtains tiered data through the road test monitoring unit itself, and then tiers the basic data. This method allows for the tiering of basic data to obtain relatively urgent primary data, and the use of secondary data to obtain edge analysis data, thus completing data tiering according to different levels of urgency.

[0011] Preferably, the grading data includes evaluation values ​​and emergency measurement values;

[0012] The hierarchical data is obtained in the following manner:

[0013] The monitoring data are ranked according to importance (Z). i Assign a value, and combine it with the size B of the data packet corresponding to the monitoring data. i The evaluation value P was calculated. i =Z i / B i ;

[0014] Based on road congestion level Y i And the corresponding vehicle speed V i The emergency measurement value J was obtained. i =Y i *V i ;

[0015] Based on the evaluation value P i The monitoring data are sorted, and then ranked according to the magnitude of the emergency measurement value, based on the evaluation value P. i The sorting is used to allocate hierarchical and basic data.

[0016] Preferably, the road congestion level Y i =Predicted travel time for the current road segment / Predicted travel time for the road segment under smooth traffic conditions.

[0017] Preferably, based on the size of the emergency measurement value, the values ​​are sorted in (J) i The monitoring data up to 5%) is used as primary data, and the remaining data is used as secondary data. The evaluation values ​​in this application take into account the degree of importance and the size of the data packet, balancing the data load and importance. For emergency measurements, the degree of road congestion and the speed of individual vehicles are taken into account, so that as much effective data as possible can be obtained in the shortest possible time under relatively urgent conditions.

[0018] Preferably, after acquiring the first-level data for the target vehicle, if automatic optimization of the driving strategy is not possible or the optimization effect is poor, then during the next splitting of the first-level and second-level data, the data will be sorted in (2J). iThe monitoring data before 5%) is used as primary data, and the remaining data is used as secondary data.

[0019] Preferably, if the monitoring data is video data, it will be automatically classified as secondary data.

[0020] Preferably, the secondary data processing obtains edge analysis data as follows: for monitoring data that is video or image, emergency data analysis and extraction are performed to obtain edge analysis data. This application addresses the issue that while videos and images generally have large bit values, they may be the only source of data in certain situations. Therefore, edge analysis data is extracted by setting up emergency data analysis and extraction to meet the timeliness requirement of data integrity when decision-making is needed.

[0021] Preferably, the emergency data includes the following data: vehicle information, distance between vehicles, distance between vehicles and the roadside, vehicle speed, vehicle acceleration, and the time, location, and probability of the vehicle risk occurring.

[0022] Preferably, the time, location, and probability of vehicle risks are calculated and predicted while keeping the target vehicle's driving strategy unchanged and the surrounding environment unchanged.

[0023] On the other hand, this application also discloses a data processing system for vehicle-road cooperative early warning, including the following modules:

[0024] The target vehicle is used to acquire vehicle-side data and to identify and warn risks based on the vehicle-side data and some basic data obtained.

[0025] The road test monitoring unit is used to acquire road test monitoring data;

[0026] The data classification and transmission unit is used to classify drive-test monitoring data into graded data and basic data; and

[0027] Based on the classification, some basic data is transmitted to the target vehicle.

[0028] This application can bring the following beneficial effects:

[0029] 1. This application obtains some basic data through vehicle-side data and road test detection units, ensuring the timeliness of data acquisition, thereby providing a data foundation for more timely data acquisition for risk identification and early warning of target vehicles.

[0030] 2. This application obtains graded data through the road test monitoring unit itself, and then grades the basic data. This method can grade the basic data to obtain the relatively urgent first-level data and use the second-level data to obtain edge analysis data, thereby completing the data grading according to different urgency levels.

[0031] 3. The evaluation values ​​in this application take into account the degree of importance and the size of the data packets, balancing the data load and importance. For emergency measurement values, the degree of road congestion and the speed of individual vehicles are taken into account, so that as much effective data as possible can be obtained in the shortest possible time under relatively urgent conditions. Attached Figure Description

[0032] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0033] Figure 1 This is a schematic diagram of Example 1;

[0034] Figure 2 This is a schematic diagram of Example 2. Detailed Implementation

[0035] To clearly illustrate the technical features of this solution, the following detailed description of specific implementation methods will be provided.

[0036] In the first embodiment, such as Figure 1 As shown, a data processing method for vehicle-road cooperative early warning includes the following steps:

[0037] S101 Target vehicle acquires vehicle-side data;

[0038] The S102 road test monitoring unit acquires road test monitoring data;

[0039] S103 classifies road test monitoring data into different levels;

[0040] Based on the hierarchical data, the basic data is allocated into primary data and secondary data;

[0041] The grading data includes evaluation values ​​and emergency measurement values;

[0042] The hierarchical data is obtained in the following manner:

[0043] The monitoring data are ranked according to importance (Z). i Assign a value, and combine it with the size B of the data packet corresponding to the monitoring data. i The evaluation value P was calculated. i =Z i / B iRegarding the importance level, the current system assigns values ​​between 50 and 100. Currently, the importance level is set at 100 for vehicle speed, and 90-100 for the importance of various entities or between entities and road tests, with values ​​assigned according to road conditions. For vehicle type, the value is 50, and other aspects are also assigned values ​​within this range.

[0044] For B i Generally, the unit of measurement is kb. Since the comparison here is essentially a horizontal comparison, the difference in the unit of measurement will not affect the sorting. As long as it remains consistent during use, the requirements can be met.

[0045] Based on road congestion level Y i And the corresponding vehicle speed V i The emergency measurement value J was obtained. i =Y i *V i Travel speed V i The unit of measurement is km / h;

[0046] Based on the evaluation value P i The monitoring data are sorted, and then ranked according to the magnitude of the emergency measurement value, based on the evaluation value P. i The sorting is used to allocate hierarchical and basic data.

[0047] The road congestion level Y i =Predicted travel time for the current road segment / Predicted travel time for the road segment under smooth traffic conditions.

[0048] Based on the size of the emergency measurement value, they will be ranked in (J) i The monitoring data before 5%) is used as primary data, and the remaining data is used as secondary data. i The absolute value used here is the value after removing the km / h unit. If automatic optimization of the driving strategy is not possible or the optimization effect is poor after acquiring the first-level data for the target vehicle, then in the next splitting of the first-level and second-level data, it will be sorted in (2J) i The monitoring data before ( / 5)% is used as primary data, and the remaining data is used as secondary data. (2J) i / 5) No more than 1 / 3.

[0049] If the monitoring data is video data, it will be automatically classified as secondary data.

[0050] The secondary data processing is performed as follows to obtain edge analysis data: for monitoring data that is video or image, emergency data analysis and extraction are performed to obtain edge analysis data.

[0051] The emergency data includes the following: vehicle information, distance between vehicles, distance between vehicles and the roadside, vehicle speed, vehicle acceleration, and the time, location, and likelihood of vehicle risks occurring.

[0052] The timing, location, and probability of vehicle risks are calculated and predicted while keeping the target vehicle's driving strategy and the surrounding environment constant.

[0053] S104 transmits some basic data to the target vehicle according to the classification.

[0054] The primary data is transmitted to the target vehicle, and the secondary data is processed according to the basic data to obtain edge analysis data. Then, the edge analysis data is transmitted to the target vehicle.

[0055] S105 targets vehicles for risk identification and early warning based on vehicle-side data and some basic data obtained.

[0056] In the second embodiment, as Figure 2 As shown, a data processing system for vehicle-road cooperative early warning includes the following modules:

[0057] Target vehicle 201 is used to acquire vehicle-side data and to identify and warn risks based on the vehicle-side data and some basic data obtained.

[0058] The road test monitoring unit 202 is used to acquire road test monitoring data;

[0059] The data classification and transmission unit 203 is used to divide the road test monitoring data into classified data and basic data; and to transmit part of the basic data to the target vehicle according to the classification.

[0060] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A data processing method for vehicle-road cooperative early warning, characterized in that: The steps include the following: Acquire vehicle-side data for the target vehicle; The road test monitoring unit acquires road test monitoring data; The road test monitoring data is divided into graded data and basic data; Based on the classification, some basic data will be transmitted to the target vehicle; The target vehicle is used for risk identification and early warning based on vehicle-side data and some basic data obtained; Based on the hierarchical data, the basic data is allocated into primary data and secondary data; The primary data is transmitted to the target vehicle, and the secondary data is processed according to the basic data to obtain edge analysis data. Then, the edge analysis data is transmitted to the target vehicle. The grading data includes evaluation values ​​and emergency measurement values; The hierarchical data is obtained in the following manner: The monitoring data are ranked according to importance (Z). i Assign a value, and combine it with the size B of the data packet corresponding to the monitoring data. i The evaluation value P was calculated. i =Z i / B i ; Based on road congestion level Y i And the corresponding vehicle speed V i The emergency measurement value J was obtained. i =Y i *V i ; Based on the evaluation value P i The monitoring data are sorted, and then ranked according to the magnitude of the emergency measurement value, based on the evaluation value P. i The sorting is used to allocate hierarchical and basic data.

2. The data processing method for vehicle-road cooperative early warning according to claim 1, characterized in that: The road congestion level Y i =Predicted travel time for the current road segment / Predicted travel time for the road segment under smooth traffic conditions.

3. The data processing method for vehicle-road cooperative early warning according to claim 1, characterized in that: Based on the size of the emergency measurement value, they will be sorted in (J) i The monitoring data before 5%) is used as primary data, and the remaining data is used as secondary data.

4. The data processing method for vehicle-road cooperative early warning according to claim 3, characterized in that: If the monitoring data is video data, it will be automatically classified as secondary data.

5. The data processing method for vehicle-road cooperative early warning according to claim 3, characterized in that: If, after acquiring Level 1 data for the target vehicle, automatic optimization of the driving strategy is not possible or the optimization effect is poor, then during the next splitting of Level 1 and Level 2 data, the data will be sorted in (2J). i The monitoring data before 5%) is used as primary data, and the remaining data is used as secondary data.

6. The data processing method for vehicle-road cooperative early warning according to claim 1, characterized in that: The secondary data processing is performed as follows to obtain edge analysis data: for monitoring data that is video or image, emergency data is analyzed and extracted to obtain edge analysis data; the emergency data includes the following data: vehicle information, distance between vehicles, distance between vehicles and the roadside, vehicle speed, vehicle acceleration, and the time, location, and probability of vehicle risks.

7. The data processing method for vehicle-road cooperative early warning according to claim 6, characterized in that: The timing, location, and probability of vehicle risks are calculated and predicted while keeping the target vehicle's driving strategy and the surrounding environment constant.

8. A vehicle-road cooperative early warning data processing system for implementing the vehicle-road cooperative early warning data processing method according to any one of claims 1-7, characterized in that: Includes the following modules: The target vehicle is used to acquire vehicle-side data and to identify and warn risks based on the vehicle-side data and some basic data obtained. The road test monitoring unit is used to acquire road test monitoring data; The data classification and transmission unit is used to divide road test monitoring data into classified data and basic data; and, according to the classification, transmits some basic data to the target vehicle.

Images