Vehicle travel risk safety assessment system

Abstract

The invention, which relates to the technical field of vehicle travel risk assessment, discloses a vehicle travel risk safety assessment system comprising a driving data acquisition module, a risk coefficient calculation module, a comprehensive risk analysis module and a travel risk assessment module. The driving data acquisition module is used for acquiring driving data of a vehicle; the risk coefficient calculation module is used for analyzing a risk model of the driving data and analyzing a risk coefficient of a vehicle; the comprehensive risk analysis module is used for calculating a comprehensive risk index of the vehicle; the travel risk assessment module is used for assessing the travel risk of the vehicle; the method is used for solving the problem that a driver cannot be effectively helped to select a safe road for driving and cannot be helped to correct driving habits due to the fact that an existing vehicle travel risk assessment technology is insufficient in analysis of factors influencing vehicle travel safety and cannot predict the travel risk of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle travel risk assessment, and in particular to a vehicle travel risk safety assessment system. Background Art

[0002] Vehicle travel risk assessment technology refers to the technology that quantitatively predicts the level of safety risks that a vehicle may encounter during driving through multi-source data collection, real-time analysis and intelligent algorithms, and generates active intervention strategies. Its purpose is to convert dynamic factors such as the environment, vehicles, drivers and traffic flow into quantifiable risk indicators, and realize the perception-assessment-decision-making closed loop.

[0003] Existing vehicle travel risk assessment technology can usually only assess the risk and take countermeasures after the risk has occurred, such as activating active braking when an impending collision is detected ahead. However, this method cannot predict the vehicle travel risk, cannot effectively help the driver choose a safe road to drive on, or help the driver correct driving habits. At the same time, existing vehicle travel risk assessment technology still has shortcomings when considering factors that affect vehicle travel safety. For example, in the Chinese patent application publication number "CN118446510A", a "risk assessment method for vehicle driving navigation" is disclosed. This solution only assesses the risk of vehicle driving navigation based on traffic flow data and historical accident rate data, and the indicators and weights given in the subsequent calculation of the road safety risk index are not clear and specific. Existing vehicle travel risk assessment technology also lacks comprehensiveness in analyzing the factors affecting vehicle travel safety and is unable to predict vehicle travel risks, resulting in an inability to effectively help drivers choose safe roads to drive on or help drivers correct driving habits. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent, by collecting the vehicle's driving data with the help of the vehicle's built-in sensors, and then analyzing the amplification coefficient of different driving data on traffic accidents based on historical data, and then analyzing the risk model of the driving data, and then analyzing the vehicle's risk coefficient based on the risk model combined with the driving data, calculating the vehicle's comprehensive risk index based on the risk coefficient, and then analyzing the risk threshold of the historical data, and finally evaluating the vehicle's travel risk based on the comprehensive risk index and the risk threshold, so as to solve the problem that the existing vehicle travel risk assessment technology still has the problem of insufficient comprehensiveness in analyzing the factors affecting vehicle travel safety and the inability to predict the vehicle's travel risk, resulting in the inability to effectively help drivers choose safe roads to drive on and help drivers correct their driving habits.

[0005] To achieve the above objectives, in a first aspect, the present application provides a vehicle travel risk safety assessment system, comprising a driving data acquisition module, a risk coefficient calculation module, a comprehensive risk analysis module, and a travel risk assessment module; the driving data acquisition module, the risk coefficient calculation module, and the travel risk assessment module are respectively data-connected to the comprehensive risk analysis module; The driving data acquisition module is used to collect the vehicle's driving data with the help of the vehicle's built-in sensors; The risk coefficient calculation module is used to analyze risk models of different driving data based on historical driving data, and analyze the risk coefficient of the vehicle based on the risk model combined with the driving data; The comprehensive risk analysis module is used to calculate the comprehensive risk index of the vehicle based on the risk coefficient; The travel risk assessment module is used to assess the travel risk of a vehicle based on a comprehensive risk index.

[0006] Furthermore, the driving data collection module is configured with a driving data collection strategy, and the driving data collection strategy includes: Get the current driving section of the vehicle and the historical driving data when a traffic accident occurs on the driving section, which is named historical data; The driving data includes driving data, environmental data and traffic data. The driving data includes driving speed and following distance. The environmental data includes visibility and road friction coefficient. The traffic data includes regional accident rate within the driving section.

[0007] Furthermore, the risk coefficient calculation module includes a risk association analysis unit, a risk model analysis unit and a risk coefficient analysis unit; The risk association analysis unit is used to analyze the increase coefficient of different driving data on traffic accidents based on historical data; The risk model analysis unit is used to analyze the risk model of driving data; The risk coefficient analysis unit is used to analyze the risk coefficient of the vehicle based on the risk model in combination with driving data.

[0008] Furthermore, the risk association analysis unit is configured with a risk association analysis strategy, and the risk association analysis strategy includes: Collect historical data and calculate the regional accident rates corresponding to different visibility levels, named fog accident rates, calculate the regional accident rates corresponding to different road friction coefficients, named wet accident rates, and calculate the regional accident rates when both visibility and road friction coefficient are within the normal range, named normal accident rates; Mark the normal accident rate, fog accident rate, and slippery accident rate as NR, FR, and WR, respectively. Calculate FR / NR to get the fog amplification factor, and calculate WR / NR to get the slippery amplification factor. A two-dimensional coordinate system is established with visibility as the X-axis and the fog amplification coefficient as the Y-axis, named the fog amplification analysis chart. The fog amplification coefficient is recorded in the fog amplification analysis chart according to visibility. A two-dimensional coordinate system is established with the road friction coefficient as the X-axis and the wet slip amplification coefficient as the Y-axis, named the wet slip amplification analysis chart. The wet slip amplification coefficient is recorded in the wet slip amplification analysis chart according to the road friction coefficient. Discrete regression analysis was performed on the fog amplification analysis diagram and the slippery amplification analysis diagram respectively, and the fog amplification equation and the slippery amplification equation were obtained.

[0009] Furthermore, the risk model analysis unit is configured with a risk model analysis strategy, and the risk model analysis strategy includes: A two-dimensional coordinate system is established with the driving speed as the X-axis and the following vehicle distance as the Y-axis. This is named the speed-distance relationship diagram. The following vehicle distance in the historical data is entered into the speed-distance relationship diagram according to the driving speed. Obtain the recommended minimum safe distance at different driving speeds based on driving safety regulations, and enter the minimum safe distance into the speed-distance relationship diagram according to the driving speed; The coordinate points obtained by entering historical data are named accident coordinates, and the coordinate points formed by the minimum safe vehicle distance are named safety coordinates; Discrete regression analysis is performed on the accident coordinates and safety coordinates respectively to obtain the vehicle speed and distance accident relationship curve and the vehicle speed and distance safety relationship curve; A risk model is constructed and the vehicle speed and distance accident relationship curve and the vehicle speed and distance safety relationship curve are entered into the risk model.

[0010] Furthermore, the risk factor analysis unit is configured with a risk factor analysis strategy, and the risk factor analysis strategy includes: Obtain the vehicle's real-time speed and following vehicle distance, named real-time speed and real-time distance respectively, enter the real-time speed and real-time distance into the speed-distance relationship diagram, and name the obtained coordinate point as a dynamic risk point; Obtain the vertical distances between the dynamic risk point and the speed-vehicle-distance accident relationship curve and the speed-vehicle-distance safety relationship curve, which are named accident deviation distance and safety deviation distance, respectively, and are represented by symbols AD and SD; Calculate SD / (AD+SD) and name the result as risk factor.

[0011] Furthermore, the comprehensive risk analysis module is configured with a comprehensive risk analysis strategy, which includes: Obtain the real-time visibility and road friction coefficient of the driving section and substitute them into the fog amplification equation and the wet-slip amplification equation respectively, and solve to obtain the real-time fog amplification coefficient and wet-slip amplification coefficient, which are named as the fog real-time amplification coefficient and the wet-slip real-time amplification coefficient, and are represented by the symbols RW and WC respectively; Mark the risk coefficient as RK, calculate RK×RW×WC, and obtain the comprehensive risk index of the vehicle on the driving section.

[0012] Furthermore, the travel risk assessment module includes a risk threshold analysis unit and a travel risk assessment unit; The risk threshold analysis unit is used to analyze the risk threshold of historical data; The travel risk assessment unit is used to assess the travel risk of the vehicle based on a comprehensive risk index and a risk threshold.

[0013] Furthermore, the risk threshold analysis unit is configured with a risk threshold analysis strategy, and the risk threshold analysis strategy includes: Calculate the comprehensive risk index for each piece of historical data, named historical index; Get the minimum and maximum values ​​of visibility, marked as Vmin and Vmax respectively, and get the minimum and maximum values ​​of road friction coefficient, marked as Cmin and Cmax respectively; The visibility and road friction coefficient in the historical data are marked as HV and HC respectively; By formula Calculate the normalized index of visibility by the formula Calculate the normalized index of the road friction coefficient, where GV is the normalized index of visibility and GC is the normalized index of the road friction coefficient; Calculate GV+GC and name the result as weather impact index. Each historical index corresponds to a weather impact index. A two-dimensional coordinate system is established with the weather impact index as the horizontal axis and the historical index as the vertical axis, named the weather index impact map. The historical index is entered into the weather index impact map according to the weather impact index, and the obtained coordinate points are named weather index impact points. Perform cluster analysis on the weather index image to obtain different impact clusters. Use rectangles to select the impact clusters to obtain cluster rectangles. When selecting the cluster rectangles, ensure that there is at least one weather index impact point on each side of the cluster rectangle. Obtain the geometric center of the cluster rectangle and name it the cluster center. Perform discrete regression analysis on the cluster center to obtain the preliminary equation of weather index influence. Move the preliminary equation of weather index influence vertically downward along the opposite direction of the vertical axis until all weather index influence points are above the preliminary equation of weather index influence, and obtain the effective equation of weather index influence. The real-time weather impact index is calculated through real-time visibility and road friction coefficient, named as the real-time weather impact index. The real-time weather impact index is substituted into the weather index impact effective equation to obtain the risk threshold.

[0014] Furthermore, the travel risk assessment unit is configured with a travel risk assessment strategy, and the travel risk assessment strategy includes: Determine whether the comprehensive risk index is greater than or equal to the risk threshold. If so, output a driving risk signal; if not, output a driving safety signal. If a driving risk signal is output, the vehicle is marked as having a travel risk within the driving section. If a driving safety signal is output, the vehicle is marked as not having a travel risk within the driving section.

[0015] Beneficial effects of the present invention: The present invention collects driving data of the vehicle by means of built-in sensors in the vehicle, then analyzes the increase coefficient of traffic accidents caused by different driving data based on historical data, then analyzes the risk model of the driving data, and then analyzes the risk coefficient of the vehicle based on the risk model combined with the driving data, and calculates the comprehensive risk index of the vehicle based on the risk coefficient. The advantage is that different driving speeds correspond to different safe vehicle distances. By analyzing the driver's following distance, it can be determined whether the vehicle has a collision risk. On the basis of the collision risk, visibility and road friction coefficient will further increase this collision risk. Therefore, the comprehensive risk index of the vehicle is calculated based on the driving data analysis to reflect the safety of the vehicle within the driving section, thereby improving the accuracy and rationality of the vehicle travel risk assessment. The present invention analyzes the risk threshold of historical data and finally evaluates the travel risk of the vehicle based on the comprehensive risk index and the risk threshold. The advantage is that the comprehensive risk index is used to reflect the safety of the vehicle in the driving section, but there is no judgment standard. Therefore, the risk threshold is obtained through historical data analysis, and the judgment basis of the comprehensive risk index is obtained, so as to perform a predictive risk assessment of the safety of the vehicle in the driving section, thereby improving the accuracy and effectiveness of the vehicle travel risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a principle block diagram of the system of the present invention; Figure 2 This is a foggy day amplification analysis diagram of the present invention; Figure 3 This is a vehicle speed and distance relationship diagram of the present invention; Figure 4 is a schematic diagram of the dynamic risk points of the present invention; Figure 5 The weather index influence diagram of the present invention; Figure 6is a schematic diagram of the influence clustering of the present invention; Figure 7 is a schematic diagram of the clustering rectangle of the present invention; Figure 8 Schematic diagram of the cluster center of the present invention; Figure 9 It is a schematic diagram of a curve corresponding to the preliminary equation of the weather index influence of the present invention. DETAILED DESCRIPTION

[0017] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0018] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0019] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0020] Example 1, please refer to Figure 1 As shown, the present application provides a vehicle travel risk safety assessment system, including a driving data acquisition module, a risk coefficient calculation module, a comprehensive risk analysis module and a travel risk assessment module; the driving data acquisition module, the risk coefficient calculation module and the travel risk assessment module are respectively connected to the comprehensive risk analysis module data; The driving data acquisition module is used to collect the vehicle's driving data with the help of the vehicle's built-in sensors; The driving data collection module is equipped with a driving data collection strategy, which includes: Get the current driving section of the vehicle and the historical driving data when a traffic accident occurs on the driving section, which is named historical data; Driving data includes driving data, environmental data, and traffic data. Driving data includes driving speed and following distance, environmental data includes visibility and road friction coefficient, and traffic data includes regional accident rates within the driving section. In actual applications, when collecting driving data, vehicle speed, following distance, and road friction coefficient can be obtained through the vehicle's built-in sensors, visibility can be obtained through weather forecasts, and regional accident rates can be obtained from traffic management departments. The regional accident rates are the same in most driving sections, but there are a small number of sections that have higher regional accident rates than other driving sections due to design issues. Therefore, it is necessary to conduct independent analysis on driving sections with higher regional accident rates than other driving sections.

[0021] The risk coefficient calculation module is used to analyze different risk models of driving data based on historical driving data, and analyze the risk coefficient of the vehicle based on the risk model combined with the driving data; the risk coefficient calculation module includes a risk association analysis unit, a risk model analysis unit, and a risk coefficient analysis unit; The risk correlation analysis unit is used to analyze the increase coefficient of traffic accidents caused by different driving data based on historical data; The risk correlation analysis unit is configured with a risk correlation analysis strategy, which includes: Collect historical data and calculate the regional accident rates corresponding to different visibility levels, named fog accident rates, calculate the regional accident rates corresponding to different road friction coefficients, named wet accident rates, and calculate the regional accident rates when both visibility and road friction coefficient are within the normal range, named normal accident rates; Mark the normal accident rate, fog accident rate, and slippery accident rate as NR, FR, and WR, respectively. Calculate FR / NR to get the fog amplification factor, and calculate WR / NR to get the slippery amplification factor. See also Figure 2 As shown, a two-dimensional coordinate system is established with visibility as the X-axis and the fog amplification coefficient as the Y-axis, named the fog amplification analysis chart, and the fog amplification coefficient is recorded in the fog amplification analysis chart according to the visibility. A two-dimensional coordinate system is established with the road friction coefficient as the X-axis and the wet slip amplification coefficient as the Y-axis, named the wet slip amplification analysis chart, and the wet slip amplification coefficient is recorded in the wet slip amplification analysis chart according to the road friction coefficient. Discrete regression analysis was performed on the fog amplification analysis diagram and the slippery amplification analysis diagram to obtain the fog amplification equation and the slippery amplification equation; In practical applications, the foggy accident rate and the slippery accident rate can be obtained from the traffic management department. For example, when visibility is 50m, the foggy accident rate is 16.26 per million vehicle-kilometers. The normal accident rate is the regional accident rate when visibility and road friction coefficient are both within the normal range. In fact, it is the accident rate under conditions such as clear weather, no fog, and no water, snow, or ice on the road. The normal accident rate is 0.41 per million vehicle-kilometers. The foggy increase coefficient when visibility is 50m is calculated to be 39.66. The calculation result is rounded to two decimal places, and the foggy increase analysis chart is constructed as shown below. Figure 2 As shown in the figure, the fog amplification equation obtained through discrete regression analysis is YV=3986.2×XV -1.139 , where YV is the fog amplification coefficient and XV is visibility. Similarly, the slippery amplification equation is YR=0.4388×XR -1.976 , where YR is the wet slip amplification coefficient and XR is the road friction coefficient.

[0022] The risk model analysis unit is used to analyze the risk model of driving data; The risk model analysis unit is configured with a risk model analysis strategy, which includes: See also Figure 3 As shown in the figure, a two-dimensional coordinate system is established with the driving speed as the X-axis and the following vehicle distance as the Y-axis, which is named the speed-distance relationship diagram. The following vehicle distance in the historical data is entered into the speed-distance relationship diagram according to the driving speed; Obtain the recommended minimum safe distance at different driving speeds based on driving safety regulations, and enter the minimum safe distance into the speed-distance relationship diagram according to the driving speed; The coordinate points obtained by entering historical data are named accident coordinates, and the coordinate points formed by the minimum safe vehicle distance are named safety coordinates; Discrete regression analysis is performed on the accident coordinates and safety coordinates respectively to obtain the vehicle speed and distance accident relationship curve and the vehicle speed and distance safety relationship curve; Construct a risk model and enter the vehicle speed and distance accident relationship curve and the vehicle speed and distance safety relationship curve into the risk model; In practical applications, the vehicle speed and distance relationship diagram is constructed as follows: Figure 3 As shown, Figure 3 The speed-distance accident relationship curve and the speed-distance safety relationship curve are marked in the figure. The speed-distance safety relationship curve is the official recommended minimum safe distance at different speeds, which can be obtained through the Internet or traffic management codes. Different speeds require different distances to be maintained. Among the traffic accidents that occur, rear-end collisions account for the largest proportion, which are usually caused by failure to maintain a safe distance. The speed-distance accident relationship curve reflects the relationship between speed and distance in historical traffic accidents. The closer to the speed-distance accident relationship curve, the greater the probability of a traffic accident, and the closer to the speed-distance safety relationship curve, the smaller the probability of a traffic accident. The risk coefficient analysis unit is used to analyze the risk coefficient of the vehicle based on the risk model and driving data; The risk factor analysis unit is configured with a risk factor analysis strategy, which includes: See also Figure 4 As shown, the real-time speed of the vehicle and the distance between the following vehicles are obtained, which are named real-time speed and real-time distance respectively. The real-time speed and real-time distance are entered into the speed-distance relationship diagram, and the obtained coordinate points are named dynamic risk points; Obtain the vertical distances between the dynamic risk point and the speed-vehicle-distance accident relationship curve and the speed-vehicle-distance safety relationship curve, which are named accident deviation distance and safety deviation distance, respectively, and are represented by symbols AD and SD; Calculate SD / (AD+SD) and name the result as risk factor; In actual application, the position of the dynamic risk point is entered as follows Figure 4As shown, the AD is 19.68, the SD is 6.92, and the calculated risk coefficient is 0.26. The risk coefficient here is only the risk coefficient under normal conditions, that is, the risk coefficient when visibility and road friction coefficient are normal.

[0023] The comprehensive risk analysis module is used to calculate the comprehensive risk index of the vehicle based on the risk coefficient; The comprehensive risk analysis module is configured with a comprehensive risk analysis strategy, which includes: Obtain the real-time visibility and road friction coefficient of the driving section and substitute them into the fog amplification equation and the wet-slip amplification equation respectively, and solve to obtain the real-time fog amplification coefficient and wet-slip amplification coefficient, which are named as the fog real-time amplification coefficient and the wet-slip real-time amplification coefficient, and are represented by the symbols RW and WC respectively; Mark the risk coefficient as RK, calculate RK×RW×WC, and get the comprehensive risk index of the vehicle on the driving section; In actual applications, the real-time visibility and road friction coefficient of the driving section are obtained as 5000m and 0.5, respectively. Since visibility greater than 1km has basically no impact on the driving of motor vehicles, and the maximum visibility in the data obtained in this embodiment is 1500m, when the visibility exceeds 1500m, it is defaulted to 1500m, that is, the real-time amplification coefficient RW in foggy days defaults to 1. Substituting the road friction coefficient into the solution, the real-time amplification coefficient WC for wet and slippery conditions is 1.73, and the calculation result is rounded to two decimal places. Further calculation shows that the comprehensive risk index of the vehicle in the driving section is 0.45, and the calculation result is rounded to two decimal places.

[0024] The travel risk assessment module is used to assess the travel risk of the vehicle based on the comprehensive risk index; the travel risk assessment module includes a risk threshold analysis unit and a travel risk assessment unit; The risk threshold analysis unit is used to analyze the risk threshold of historical data; The risk threshold analysis unit is configured with a risk threshold analysis strategy, which includes: Calculate the comprehensive risk index for each piece of historical data, named historical index; Get the minimum and maximum values ​​of visibility, marked as Vmin and Vmax respectively, and get the minimum and maximum values ​​of road friction coefficient, marked as Cmin and Cmax respectively; The visibility and road friction coefficient in the historical data are marked as HV and HC respectively; By formula Calculate the normalized index of visibility by the formula Calculate the normalized index of the road friction coefficient, where GV is the normalized index of visibility and GC is the normalized index of the road friction coefficient; Calculate GV+GC and name the result as weather impact index. Each historical index corresponds to a weather impact index. In actual applications, Vmin and Vmax are obtained as 20m and 1500m, respectively, and Cmin and Cmax are 0.1 and 0.7, respectively. According to the aforementioned rules, when visibility HV exceeds 1500m, it is calculated as 1500m. For example, in a historical data entry, HV is 5000m, which is considered 1500m, and the final calculated GV is 1. The weather impact index affects driving safety. The smaller the weather impact index, the greater the impact of weather on driving safety. Visibility and road friction coefficient can be added after normalization to assess the degree of their combined impact on driving safety. For example, in one driving session, GV is 0.6 and GC is 1, resulting in a weather impact index of 1.6. In another driving session, GV is 0.8 and GC is 0.8, resulting in a weather impact index of 1.6. Although visibility is higher than the previous data, the road surface is more slippery, which also affects driving safety. Therefore, the weather impact index is the same in the two driving sessions after calculation. See also Figure 5 As shown, a two-dimensional coordinate system is established with the weather impact index as the horizontal axis and the historical index as the vertical axis, which is named the weather index impact map. The historical index is entered into the weather index impact map according to the weather impact index, and the obtained coordinate point is named the weather index impact point; In practical applications, the weather index impact diagram is constructed as follows Figure 5 As shown in the figure, the historical data is the driving data when traffic accidents occurred in the past, and the weather index influence map reflects the approximate distribution of the comprehensive risk index when traffic accidents occurred under different weather influence indicators in the historical data, so as to find the risk threshold. Since different weather influence indicators will increase the risk coefficient, the risk thresholds applicable in normal weather and bad weather are different. Therefore, further analysis is needed to adapt to different weather influence indicators. At the same time, since there are a few free weather index influence points in the historical indicators, such historical indicators are usually low-probability events, which account for a low proportion in the analysis and have too little impact on the analysis results. However, safety is no small matter, so we need to take all situations into consideration. Therefore, after cluster analysis, different influence clusters are regarded as a whole, so that the impact of low-probability events on the analysis results can be increased. See also Figures 6 and 7 As shown, cluster analysis is performed on the weather index image to obtain different impact clusters. The impact clusters are framed with rectangles to obtain cluster rectangles. When selecting the cluster rectangles, it is necessary to ensure that there is at least one weather index impact point on each side of the cluster rectangle. See also Figures 8 and 9As shown, the geometric center of the cluster rectangle is obtained and named as the cluster center. Discrete regression analysis is performed on the cluster center to obtain the preliminary equation of weather index influence. The preliminary equation of weather index influence is moved vertically downward along the opposite direction of the vertical axis until all weather index influence points are above the preliminary equation of weather index influence, and the effective equation of weather index influence is obtained. The real-time weather impact index is calculated based on real-time visibility and road friction coefficient. It is named the real-time weather impact index. The real-time weather impact index is substituted into the weather index impact effective equation to obtain the risk threshold. In practical applications, the influence clustering is obtained through analysis. Figure 6 As shown, Figure 6 Each hollow circle in the figure is an influence cluster, and the cluster rectangle is obtained by selecting it. Figure 7 As shown, the cluster centers are extracted as Figure 8 As shown, the curve corresponding to the preliminary equation of weather index influence is obtained through discrete regression analysis as shown in Figure 9 As shown, the preliminary equation of weather index influence reflects the trend of historical indicators changing with the weather influence indicators. However, it involves safety and needs special attention. Some weather index influence points are below the preliminary equation of weather index influence. Therefore, it is necessary to calibrate it and move the preliminary equation of weather index influence downward so that all weather index influence points are above the preliminary equation of weather index influence. In this way, the driving conditions corresponding to all historical indicators can be included and the trend of historical indicators changing with the weather influence indicators can be met. Finally, the effective equation of weather index influence is obtained as YT=3.92×X 2 -21.75×X+24.46. For example, if the weather real-time impact index calculated during this trip is 1.5, the risk threshold is 0.655. The travel risk assessment unit is used to assess the travel risk of the vehicle based on comprehensive risk indicators and risk thresholds; The travel risk assessment unit is equipped with a travel risk assessment strategy, which includes: Determine whether the comprehensive risk index is greater than or equal to the risk threshold. If so, output a driving risk signal; if not, output a driving safety signal. If a driving risk signal is output, the vehicle is marked as having a travel risk within the driving section; if a driving safety signal is output, the vehicle is marked as not having a travel risk within the driving section; In actual applications, the calculated comprehensive risk index of the vehicle in the driving section is 0.45, which is less than the risk index. A driving safety signal is output, marking that the vehicle does not have travel risks in the driving section.

[0025] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0026] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

Claims

1. A vehicle travel risk safety assessment system, characterized in that: It includes a driving data acquisition module, a risk coefficient calculation module, a comprehensive risk analysis module and a travel risk assessment module; the driving data acquisition module, the risk coefficient calculation module and the travel risk assessment module are respectively connected to the comprehensive risk analysis module data; The driving data acquisition module is used to collect the vehicle's driving data with the help of the vehicle's built-in sensors; The risk coefficient calculation module is used to analyze risk models of different driving data based on historical driving data, and analyze the risk coefficient of the vehicle based on the risk model combined with the driving data; The comprehensive risk analysis module is used to calculate the comprehensive risk index of the vehicle based on the risk coefficient; The travel risk assessment module is used to assess the travel risk of a vehicle based on a comprehensive risk index.

2. A vehicle travel risk safety assessment system according to claim 1, characterized in that: The driving data acquisition module is configured with a driving data acquisition strategy, which includes: Get the current driving section of the vehicle and the historical driving data when a traffic accident occurs on the driving section, which is named historical data; The driving data includes driving data, environmental data and traffic data. The driving data includes driving speed and following distance. The environmental data includes visibility and road friction coefficient. The traffic data includes regional accident rate within the driving section.

3. A vehicle travel risk safety assessment system according to claim 1, characterized in that: The risk coefficient calculation module includes a risk association analysis unit, a risk model analysis unit and a risk coefficient analysis unit; The risk association analysis unit is used to analyze the increase coefficient of different driving data on traffic accidents based on historical data; The risk model analysis unit is used to analyze the risk model of driving data; The risk coefficient analysis unit is used to analyze the risk coefficient of the vehicle based on the risk model in combination with driving data.

4. A vehicle travel risk safety assessment system according to claim 3, characterized in that: The risk association analysis unit is configured with a risk association analysis strategy, and the risk association analysis strategy includes: Collect historical data and calculate the regional accident rates corresponding to different visibility levels, named fog accident rates, calculate the regional accident rates corresponding to different road friction coefficients, named wet accident rates, and calculate the regional accident rates when both visibility and road friction coefficient are within the normal range, named normal accident rates; Mark the normal accident rate, fog accident rate, and slippery accident rate as NR, FR, and WR, respectively. Calculate FR / NR to get the fog amplification factor, and calculate WR / NR to get the slippery amplification factor. A two-dimensional coordinate system is established with visibility as the X-axis and the fog amplification coefficient as the Y-axis, named the fog amplification analysis chart. The fog amplification coefficient is recorded in the fog amplification analysis chart according to visibility. A two-dimensional coordinate system is established with the road friction coefficient as the X-axis and the wet slip amplification coefficient as the Y-axis, named the wet slip amplification analysis chart. The wet slip amplification coefficient is recorded in the wet slip amplification analysis chart according to the road friction coefficient. Discrete regression analysis was performed on the fog amplification analysis diagram and the slippery amplification analysis diagram respectively, and the fog amplification equation and the slippery amplification equation were obtained.

5. A vehicle travel risk safety assessment system according to claim 4, characterized in that: The risk model analysis unit is configured with a risk model analysis strategy, and the risk model analysis strategy includes: A two-dimensional coordinate system is established with the driving speed as the X-axis and the following vehicle distance as the Y-axis. This is named the speed-distance relationship diagram. The following vehicle distance in the historical data is entered into the speed-distance relationship diagram according to the driving speed. Obtain the recommended minimum safe distance at different driving speeds based on driving safety regulations, and enter the minimum safe distance into the speed-distance relationship diagram according to the driving speed; The coordinate points obtained by entering historical data are named accident coordinates, and the coordinate points formed by the minimum safe vehicle distance are named safety coordinates; Discrete regression analysis is performed on the accident coordinates and safety coordinates respectively to obtain the vehicle speed and distance accident relationship curve and the vehicle speed and distance safety relationship curve; A risk model is constructed and the vehicle speed and distance accident relationship curve and the vehicle speed and distance safety relationship curve are entered into the risk model.

6. A vehicle travel risk safety assessment system according to claim 5, characterized in that: The risk factor analysis unit is configured with a risk factor analysis strategy, which includes: Obtain the vehicle's real-time speed and following vehicle distance, named real-time speed and real-time distance respectively, enter the real-time speed and real-time distance into the speed-distance relationship diagram, and name the obtained coordinate point as a dynamic risk point; Obtain the vertical distances between the dynamic risk point and the speed-vehicle-distance accident relationship curve and the speed-vehicle-distance safety relationship curve, which are named accident deviation distance and safety deviation distance, respectively, and are represented by symbols AD and SD; Calculate SD / (AD+SD) and name the result as risk factor.

7. A vehicle travel risk safety assessment system according to claim 6, characterized in that: The comprehensive risk analysis module is configured with a comprehensive risk analysis strategy, which includes: Obtain the real-time visibility and road friction coefficient of the driving section and substitute them into the fog amplification equation and the wet-slip amplification equation respectively, and solve to obtain the real-time fog amplification coefficient and wet-slip amplification coefficient, which are named as the fog real-time amplification coefficient and the wet-slip real-time amplification coefficient, and are represented by the symbols RW and WC respectively; Mark the risk coefficient as RK, calculate RK×RW×WC, and obtain the comprehensive risk index of the vehicle on the driving section.

8. A vehicle travel risk safety assessment system according to claim 7, characterized in that: The travel risk assessment module includes a risk threshold analysis unit and a travel risk assessment unit; The risk threshold analysis unit is used to analyze the risk threshold of historical data; The travel risk assessment unit is used to assess the travel risk of the vehicle based on a comprehensive risk index and a risk threshold.

9. A vehicle travel risk safety assessment system according to claim 8, characterized in that: The risk threshold analysis unit is configured with a risk threshold analysis strategy, and the risk threshold analysis strategy includes: Calculate the comprehensive risk index for each piece of historical data, named historical index; Get the minimum and maximum values ​​of visibility, marked as Vmin and Vmax respectively, and get the minimum and maximum values ​​of road friction coefficient, marked as Cmin and Cmax respectively; The visibility and road friction coefficient in the historical data are marked as HV and HC respectively; By formula Calculate the normalized index of visibility by the formula Calculate the normalized index of the road friction coefficient, where GV is the normalized index of visibility and GC is the normalized index of the road friction coefficient; Calculate GV+GC and name the result as weather impact index. Each historical index corresponds to a weather impact index. A two-dimensional coordinate system is established with the weather impact index as the horizontal axis and the historical index as the vertical axis, named the weather index impact map. The historical index is entered into the weather index impact map according to the weather impact index, and the obtained coordinate points are named weather index impact points. Perform cluster analysis on the weather index image to obtain different impact clusters. Use rectangles to select the impact clusters to obtain cluster rectangles. When selecting the cluster rectangles, ensure that there is at least one weather index impact point on each side of the cluster rectangle. Obtain the geometric center of the cluster rectangle and name it the cluster center. Perform discrete regression analysis on the cluster center to obtain the preliminary equation of weather index influence. Move the preliminary equation of weather index influence vertically downward along the opposite direction of the vertical axis until all weather index influence points are above the preliminary equation of weather index influence, and obtain the effective equation of weather index influence. The real-time weather impact index is calculated through real-time visibility and road friction coefficient, named as the real-time weather impact index. The real-time weather impact index is substituted into the weather index impact effective equation to obtain the risk threshold.

10. A vehicle travel risk safety assessment system according to claim 9, characterized in that: The travel risk assessment unit is configured with a travel risk assessment strategy, which includes: Determine whether the comprehensive risk index is greater than or equal to the risk threshold. If so, output a driving risk signal; if not, output a driving safety signal. If a driving risk signal is output, the vehicle is marked as having a travel risk within the driving section. If a driving safety signal is output, the vehicle is marked as not having a travel risk within the driving section.

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