Intelligent network connection automobile traffic road safety risk assessment method

Abstract

The invention, which belongs to the technical field of intelligent traffic, discloses a safety risk assessment method for a traffic road of an intelligent networked automobile, and the method comprises the following steps: constructing a road traffic safety risk assessment index system; obtaining index data based on the road traffic safety risk evaluation index system; calculating an evaluation factor of each index in a road traffic safety risk evaluation index system based on the index data; and calculating the traffic safety risk level of the road intelligent network connection vehicle based on the evaluation factor of each index. According to the invention, the potential safety risk of the road can be identified in advance, and scientific, reasonable and comprehensive technical support is provided for intelligent network connection automobile operation management.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to a method for assessing road safety risks of intelligent networked vehicles. Background Art

[0002] As ICVs gradually complete the testing of test sites, major cities across the country have gradually carried out on-road testing of ICVs in order to better test and verify their performance. However, urban roads contain many complex scenarios, and there is currently a lack of scientific and standardized guiding documents for the selection, evaluation and grading of test roads. During on-road testing, ICVs pose a great deal of unknown risks to urban roads, and it is difficult for city managers and testing companies to monitor on-road testing of ICVs in a more targeted manner. Therefore, it is particularly important to propose an evaluation method and system that can grade urban roads. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method for assessing road safety risks of intelligent connected vehicles to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above object, the present invention provides a method for assessing road safety risks of intelligent connected vehicles, comprising the following steps:

[0005] Construct a road traffic safety risk assessment indicator system;

[0006] Acquiring index data based on the road traffic safety risk assessment index system;

[0007] Calculate the evaluation factors of each indicator in the road traffic safety risk evaluation indicator system based on the indicator data;

[0008] The road safety risk level of intelligent connected vehicles is calculated based on the evaluation factors of each indicator.

[0009] Optionally, the road traffic safety risk assessment index system includes traffic operation characteristics, road safety management facilities, and road traffic organization;

[0010] The traffic operation characteristics include: traffic accidents, congestion index, average vehicle speed during morning and evening peak hours, and vehicle flow;

[0011] The road safety management facilities include: special-shaped intersection density, intersection level difference, access level difference, traffic signs and markings, and off-site capture;

[0012] The road traffic organization includes: on-road parking setting rate and intersection channelization.

[0013] Optionally, the calculation formula for the evaluation factor of the average vehicle speed during the morning and evening peak hours is:

[0014]

[0015] Where, L represents the length of the road to be tested or the road to be used; t i It represents the time when vehicle i passes through the planned test section or the pass road; n represents the number of vehicles passing through the road section of the planned test section or the pass road.

[0016] Optionally, the calculation formula of the evaluation factor of the motor vehicle flow is:

[0017]

[0018] In the formula, A r represents the daytime traffic volume coefficient; T represents the number of daytime hours; Q represents the 24-hour traffic volume in the predicted year.

[0019] Optionally, the calculation formula of the evaluation factor of the on-street parking setting rate is:

[0020]

[0021] In the formula, n represents the number of on-street parking spaces set up on the road section to be tested; It represents the average length of parking spaces on the road; L represents the road section to be tested or the mileage of the road.

[0022] Optionally, before calculating the road intelligent connected vehicle traffic safety risk level, the evaluation factors of each indicator are normalized, wherein the calculation formula for the normalized evaluation factor is:

[0023]

[0024] Where P ij represents the normalized result of the jth evaluation object indicator of the i-th indicator; ω ij represents the calculated value of the jth evaluation object indicator of the i-th indicator; ω imax Indicates the maximum index calculation value of the i-th index; ω imin Indicates the minimum calculated value of the ith indicator.

[0025] Optionally, a road safety risk score is calculated based on the normalized evaluation factors and the recommended values ​​of each indicator; wherein the calculation formula is:

[0026]

[0027] In the formula, α i represents the weight calculated for the ith indicator; P i Represents the normalized result of the i-th indicator.

[0028] Optionally, obtaining a road safety risk level for intelligent connected vehicles based on the road safety risk score;

[0029] Among them, the road intelligent connected vehicle traffic safety risk levels are divided into:

[0030] When the road safety risk score is greater than 9, the road intelligent connected vehicle traffic safety risk level is level one;

[0031] When the road safety risk score is greater than 7 and less than or equal to 9, the road intelligent connected vehicle traffic safety risk level is level 2;

[0032] When the road safety risk score is less than or equal to 7, the road intelligent connected vehicle traffic safety risk level is level three.

[0033] Compared with the prior art, the present invention has the following advantages and technical effects:

[0034] The present invention can identify potential road safety risks in advance and provide scientific, reasonable and comprehensive technical support for the operation and management of intelligent connected vehicles. Evaluation from three dimensions, namely traffic operation characteristics, road safety management facilities and road traffic organization, can help identify high-risk time periods and sections, provide a scientific basis for traffic management and decision-making; timely discover the damage or improper setting of facilities, and promote the intelligent upgrade of road safety management facilities; at the same time, the present invention can promote the improvement of road traffic safety level and provide strong support for the healthy development of the intelligent connected vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 This is a flow chart of the risk assessment of road traffic safety for intelligent connected vehicles tested in an embodiment of the present invention;

[0037] Figure 2 This is a road traffic safety risk assessment index system for testing intelligent connected vehicles in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0040] Embodiment 1

[0041] like Figure 1 As shown, in this embodiment, a method for assessing the safety risk of intelligent connected vehicle traffic roads is provided, which aims to classify urban roads and provide guidance for open road testing of intelligent connected vehicles, including the following steps:

[0042] Construct a road traffic safety risk evaluation index system; obtain index data based on the road traffic safety risk evaluation index system; calculate the evaluation factor of each index in the road traffic safety risk evaluation index system based on the index data; calculate the road traffic safety risk level of intelligent networked vehicles based on the evaluation factor of each index. The specific implementation process includes:

[0043] Step 1: Based on the index data obtained through electronic maps, remote sensing satellite images, video images, the public security traffic safety comprehensive service management platform, the public security traffic management big data analysis and judgment platform, field surveys, etc., calculate the evaluation factors of each index according to formulas (1) to (11).

[0044] Furthermore, if Figure 2 As shown, this embodiment includes the following evaluation factors:

[0045] Factor ω is evaluated by road traffic accidents acc Assess the safety risk of accidents on the road sections or roads to be tested by intelligent connected vehicles; Traffic accident assessment factor ω on the road sections or roads to be tested acc The calculation method is as follows:

[0046]

[0047] Where: m d ——Number of deaths caused by traffic accidents on the road section to be tested in the past three years; m r ——The number of injured people caused by traffic accidents in the proposed test section in the past three years.

[0048] Data sources: Public Security Traffic Safety Comprehensive Service Management Platform, Public Security Traffic Management Big Data Analysis and Judgment Platform.

[0049] Evaluating factor ω through road traffic signs and markings sig Evaluate the traffic safety facilities of the road section or road through which the intelligent networked vehicle is to be tested; evaluate the traffic sign and marking factors of the road section or road through which the intelligent networked vehicle is to be tested sig The calculation method is as follows:

[0050]

[0051] Where: m s ——The number of missing traffic signs and markings or irregularly set points per kilometer on the road section to be tested or on the through road.

[0052] Data sources: electronic maps, visual interpretation of remote sensing images, video image recognition, and field surveys.

[0053] Evaluation factor ω through road congestion index con Evaluate the congestion condition of the road section or road to be tested for intelligent connected vehicles; the congestion index evaluation factor of the road section or road to be tested ω con The calculation method is as follows:

[0054]

[0055] Where: m c ——Congestion index of the road section or road to be tested.

[0056] Data source: Amap or Baidu Map.

[0057] Evaluating factor ω by density of road irregular intersections het Evaluate the topological structure of the road section or road through which the intelligent networked vehicle is to be tested; evaluate the density factor of the special-shaped intersection of the road section or road through which the intelligent networked vehicle is to be tested het The calculation method is as follows:

[0058]

[0059] Where: m h ——The number of special-shaped intersections per kilometer of the road section to be tested. Special-shaped intersections refer to intersections with an angle of <75° or an angle of >105°.

[0060] Data sources: electronic maps, visual interpretation of remote sensing images, video image recognition, and field surveys.

[0061] Evaluate the factor ω by using the road intersection level difference iet Evaluate the topological structure of the road section or road through which the intelligent networked vehicle is to be tested; evaluate the level difference factor ω of the intersection of the road section or road through which the intelligent networked vehicle is to be tested iet The calculation method is as follows:

[0062]

[0063] Where: m i ——The number of intersections whose intersection order difference P1 is greater than 2 in the road section to be tested; the intersection order difference P1 = max|ab|, where a and b represent the order values ​​of the two roads intersecting at the intersection.

[0064] Data sources: electronic maps, visual interpretation of remote sensing images, video image recognition, and field surveys.

[0065] The factor ω is evaluated by the road access level difference acp Evaluate the topological structure of the road section or road to be tested for intelligent connected vehicles; evaluate the level difference factor of the access point of the road section or road to be tested ω acp The calculation method is as follows:

[0066]

[0067] Where: m a ——The number of access points whose access point level difference P2 of the road section to be tested is greater than 2; the access point level difference P2 = |uv|, u represents the level value of the access road, and v represents the level value of the urban road.

[0068] Data sources: electronic maps, visual interpretation of remote sensing images, video image recognition, and field surveys.

[0069] Evaluation factor ω of the on-street parking rate par Evaluate the traffic organization of the road section or road to be tested for intelligent connected vehicles; evaluate the parking rate of the road section or road to be tested ω par The calculation method is as follows:

[0070]

[0071] Where: n is the number of on-street parking spaces set up on the road section to be tested; ——The average length of parking spaces on the road section to be tested or the road to be used, in kilometers; L——The mileage of the road section to be tested or the road to be used, in kilometers.

[0072] Data sources: electronic maps, visual interpretation of remote sensing images, video image recognition, and field surveys.

[0073] Evaluation factor of average vehicle speed during morning and evening rush hours on the passing road ω vel Evaluate the running characteristics of motor vehicles during morning and evening peak hours on the road section or road to be tested for intelligent connected vehicles; the average vehicle speed evaluation factor ω during morning and evening peak hours on the road section or road to be tested vel The calculation method is as follows:

[0074]

[0075] Where: L is the length of the road section or road to be tested, in kilometers; t i ——The time it takes for vehicle i to pass through the planned test section or the pass road, in hours; n——The number of vehicles on the road section that pass through the planned test section or the pass road.

[0076] Data source: data collected from field surveys and road inspections.

[0077] Evaluate factor ω by taking non-site snapshots on the road off Evaluate the degree of illegal supervision on the road sections or roads to be tested by intelligent connected vehicles; take off-site photos of the road sections or roads to be tested off The evaluation factor is calculated as follows:

[0078]

[0079] Where: n is the number of fixed traffic technology monitoring equipment on the road section to be tested; R i ——Monitoring range of the ith fixed traffic technical monitoring equipment; L——Length of the road section to be tested.

[0080] Data sources: electronic maps, visual interpretation of remote sensing images, video image recognition, and field surveys.

[0081] Factor ω is evaluated by motor vehicle flow flo Evaluate the impact of the planned test section or road on the operation of the connected vehicle; evaluate the motor vehicle flow rate on the planned test section or road ω flo The calculation method is as follows:

[0082]

[0083] Where: A r ——Daytime traffic coefficient, with a value of approximately 0.80; T——number of daytime hours; Q——24-hour traffic volume in each forecast year.

[0084] Data sources: electronic maps and field surveys.

[0085] Evaluate the factor ω through intersection channelization hett Evaluate the smoothness of the intersection of the planned test section or the road for intelligent connected vehicles; channelization evaluation factor of the planned test section or the road intersection ω hett The calculation method is as follows:

[0086]

[0087] Where: m h ——The number of test sections or road lanes where the number of lanes suddenly narrows and road bottlenecks occur.

[0088] Data sources: electronic maps, visual interpretation of remote sensing images, video image recognition, and field surveys.

[0089] Step 2: Based on the evaluation factors of traffic operation characteristics (traffic accidents, congestion index, average speed during morning and evening peak hours, motor vehicle flow), road safety management facilities (number of special-shaped intersections, intersection level difference, access level difference, traffic signs and markings, off-site snapshot), and road traffic organization (on-street parking setting rate, intersection channelization) calculated in step 1, normalize them according to formula (12).

[0090]

[0091] Where: P ij ——The normalized result of the jth evaluation object indicator of the ith indicator; ω ij ——the calculated value of the jth evaluation object indicator of the ith indicator; ω imax ——The maximum calculated value of the ith indicator; ω imin ——The minimum calculated value of the ith indicator;

[0092] Step 3: Calculate the final score according to formula (13) based on the normalized values ​​of the indicators calculated in step 2 and the recommended weight values ​​of each indicator in Table 1.

[0093]

[0094] Where: α i ——The weight calculated for the ith indicator; P i ——Normalized result of the ith indicator;

[0095] Table 1

[0096]

[0097] The final output evaluation results are:

[0098] According to the calculation results of formula (13), the road safety risk level of intelligent connected vehicles is confirmed by referring to Table 2, and corresponding decisions are made according to the evaluation suggestions.

[0099] Table 2

[0100]

[0101] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for assessing road safety risks of intelligent connected vehicles, characterized in that: The following steps are involved: Construct a road traffic safety risk assessment indicator system; Acquiring index data based on the road traffic safety risk assessment index system; Calculate the evaluation factors of each indicator in the road traffic safety risk evaluation indicator system based on the indicator data; The road safety risk level of intelligent connected vehicles is calculated based on the evaluation factors of each indicator.

2. The method for assessing road safety risks of intelligent connected vehicles according to claim 1, characterized in that: The road traffic safety risk assessment index system includes traffic operation characteristics, road safety management facilities, and road traffic organization; The traffic operation characteristics include: traffic accidents, congestion index, average vehicle speed during morning and evening peak hours, and vehicle flow; The road safety management facilities include: special-shaped intersection density, intersection level difference, access level difference, traffic signs and markings, and off-site capture; The road traffic organization includes: on-road parking setting rate and intersection channelization.

3. The method for assessing road safety risks of intelligent connected vehicles according to claim 2, characterized in that: The calculation formula for the evaluation factor of the average vehicle speed during the morning and evening peak hours is: Where, L represents the length of the road to be tested or the road to be travelled; t i It represents the time when vehicle i passes through the planned test section or the pass road; n represents the number of vehicles passing through the road section of the planned test section or the pass road.

4. The method for assessing road safety risks of intelligent connected vehicles according to claim 2, characterized in that: The calculation formula of the evaluation factor of the motor vehicle flow is: In the formula, A r represents the daytime traffic volume coefficient; T represents the number of daytime hours; Q represents the 24-hour traffic volume in the predicted year.

5. The method for assessing road safety risks of intelligent connected vehicles according to claim 2, characterized in that: The calculation formula for the evaluation factor of the on-street parking setting rate is: In the formula, n represents the number of on-street parking spaces set up on the road section to be tested; It represents the average length of parking spaces on the road; L represents the road section to be tested or the mileage of the road.

6. The method for assessing road safety risks of intelligent connected vehicles according to claim 1, characterized in that: Before calculating the road safety risk level of intelligent connected vehicles, the evaluation factors of each indicator are normalized. The calculation formula for the normalized evaluation factors is: Where P ij represents the normalized result of the jth evaluation object indicator of the i-th indicator; ω ij represents the calculated value of the jth evaluation object indicator of the i-th indicator; ω imax Indicates the maximum index calculation value of the i-th index; ω imin Indicates the minimum calculated value of the ith indicator.

7. The method for assessing road safety risks of intelligent networked vehicles according to claim 6, characterized in that: The road safety risk score is calculated based on the normalized evaluation factors and the recommended values ​​of each indicator; the calculation formula is: In the formula, α i represents the weight calculated for the ith indicator; P i Represents the normalized result of the i-th indicator.

8. The method for assessing road safety risks of intelligent networked vehicles according to claim 7, characterized in that: Obtaining a road safety risk level for intelligent connected vehicles based on the road safety risk score; Among them, the road intelligent connected vehicle traffic safety risk levels are divided into: When the road safety risk score is greater than 9, the road intelligent connected vehicle traffic safety risk level is level one; When the road safety risk score is greater than 7 and less than or equal to 9, the road intelligent connected vehicle traffic safety risk level is level 2; When the road safety risk score is less than or equal to 7, the road intelligent connected vehicle traffic safety risk level is level three.

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