Driving early warning method and system based on dynamic balance beam barrel risk control model

Abstract

The invention discloses a driving early warning method and system based on a dynamic balance wooden barrel risk control model. The method comprises the following steps: respectively quantifying each driving defense risk factor and each driving environment risk factor into a wooden plate length; selecting the minimum value of the length of each driving defense risk factor as a driving defense DD board index value Ldd; selecting the minimum length value of each driving environment risk factor as a driving environment risk DE board index value Lde; and calculating a driving safety risk value V according to the Ldd, the Lde, the vehicle information comprehensive factor index value R and a safety redundancy ring adjustment angle theta determined according to the vehicle driving speed, finally comparing the V with a set risk level early warning value, and performing early warning according to a set early warning rule. According to the method, accurate judgment and accurate prediction of the driving risk are realized by comprehensively analyzing the changes of the driving behavior of the driver, the traffic environment, the road condition and the natural environment, the driving safety traffic risk can be reduced, and traffic accidents are actively prevented.

Description

Technical Field

[0001] The present invention belongs to the field of road traffic safety and relates to a driving warning method and system based on a dynamic balancing barrel risk control model, which is suitable for accident risk assessment in intelligent transportation systems, vehicle-road collaborative intelligent driving, vehicle-road-cloud integration, Internet of Vehicles, and the insurance industry. Background Art

[0002] In the field of traffic safety, driving warning systems are one of the important technologies to improve driving safety. Existing driving warning systems usually rely on a single data source or algorithm, such as warnings based on vehicle speed or distance. These systems may be effective in specific situations, but often lack comprehensive consideration of complex traffic environments. For example, some systems may not fully consider the driver's fatigue state, changes in road conditions, fluctuations in traffic flow, and the impact of bad weather. These factors may have a significant impact on driving safety, but existing technologies often do not provide a comprehensive and dynamic risk assessment method.

[0003] The theory of the short board of the wooden barrel is a classic management concept. Its core idea is that the amount of water a wooden barrel can hold does not depend on the longest board on the barrel, but on the shortest board. In other words, the capacity of the barrel is determined by its shortest part. This theory is often used to illustrate the impact of weak links in a team, organization or system on the overall performance. The theory of the long board of the wooden barrel is a supplement or alternative to the traditional theory of the short board of the wooden barrel. It emphasizes that in an organization, team or system, more attention should be paid to and developed on its advantages or strengths (i.e., long boards), rather than focusing too much on making up for weaknesses or short boards.

[0004] At present, there are three main defects in the algorithms of vehicle-side and road-side intelligent perception devices and systems: First, there are too many key factors, the algorithms are too complicated, and the algorithm design is based on artificially set thresholds and numerical weightings, which often deviates greatly from the actual situation and ignores the barrel principle of systemic risk - that is, the risk of accidents depends on the shortest board of the barrel. Secondly, existing models often ignore the laws of driving behavior, including the laws of the physiological and psychological characteristics of drivers; the key factor in determining the driving risk control process is people, people have different personalities, and people's stability is greatly affected by the environment; thus, the assessment of driving risks is not comprehensive enough. Finally, the judgment of driving risks is often isolated, and the multi-source data such as driving behavior, road scenes and vehicle operation status cannot be effectively fused and calculated, resulting in problems such as misreporting, omissions and false alarms in the active safety intelligent equipment system of the vehicle. Summary of the invention

[0005] Purpose of the invention: In view of the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a driving warning method and system based on the dynamic balance barrel wind control model, which aims to comprehensively analyze the changes in the driver's driving behavior, traffic environment, road conditions and natural environment, to achieve accurate judgment of driving risks, reduce the risk of driving safety passage, and actively prevent traffic accidents.

[0006] Technical solution: To achieve the above-mentioned invention object, the present invention adopts the following technical solution:

[0007] A driving warning method based on a dynamic balancing barrel wind control model, comprising:

[0008] Each driving defense risk factor and each driving environment risk factor are quantified as the length of the wooden board;

[0009] The minimum length of each driving defense risk factor is selected as the driving defense DD board index value Ldd;

[0010] The minimum length of each driving environment risk factor is selected as the driving environment risk DE board index value Lde;

[0011] The driving safety risk value V is calculated based on the DD plate index value, the DE plate index value, the vehicle information comprehensive factor index value R, and the safety redundancy loop adjustment angle θ determined according to the vehicle driving speed:

[0012]

[0013] Compare V with the set risk level warning value and issue a warning based on the set warning rules.

[0014] Preferably, the driving defense risk factors include one or more of the driver's following habits, lane changing habits, intersection habits, fatigue, and distraction; the driving environment risk factors include one or more of traffic weather, traffic roads, traffic flow, traffic control level, and traffic vehicle-road coordination level.

[0015] Preferably, the comprehensive vehicle information factor index value is obtained by quantifying and weighting multiple factors including vehicle size, fault, vehicle model, load and mileage.

[0016] Preferably, the relationship between the vehicle speed v and the safety redundancy loop adjustment angle θ is:

[0017]

[0018] Among them, v0 is the vehicle speed limit under the shortest board risk factor.

[0019] As a preference, each risk factor is quantified into wooden board lengths L0, L1 and L2 representing high risk, medium risk and low risk respectively according to their respective risk levels; based on L0, L1 and L2, the corresponding risk level warning values ​​V0, V1 and V2 are calculated, where V0 = L0 × πRs 2 , V1=L1×πRs 2 , V2=L2×πRs 2 , Rs is the comprehensive factor index value of vehicle information when the vehicle is in good condition.

[0020] Furthermore, the method further includes determining the high, medium and low risk level index values ​​R0, R1 and R2 corresponding to the vehicle information comprehensive factor according to the minimum value L of Ldd and Lde, wherein

[0021] Preferably, when Ldd, Lde and R are all good, no warning is issued. When Ldd, Lde or R reaches a high, medium or low risk level indicator value, the driving safety risk value V is calculated and a driving risk reminder is issued based on the calculation result.

[0022] A computer system includes a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor. When the computer program / instruction is executed by the processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model are implemented.

[0023] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model are implemented.

[0024] A computer program product includes a computer program / instruction, and when the computer program / instruction is executed by a processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model are implemented.

[0025] Beneficial effects: The present invention establishes a key safety short-term warning dynamic balance barrel risk control model for a road traffic system with a binary structure of driving behavior capability (DD) and driving behavior constraint conditions (DE), which can take into account a variety of different driving risk factors, including road traffic environment risk factors, driving defense risk factors and vehicle-related risk factors, to identify safety risks and take early warning measures. Compared with the prior art, the present invention can reduce false alarms and missed alarms, and accurate risk assessment and timely early warning reminders can help drivers take preventive measures, thereby reducing the incidence of traffic accidents; at the same time, the risk control model of the present invention can reduce unnecessary warnings, which helps to reduce the energy consumption and operating costs of the system. On the basis of the Road Traffic Safety Law, the present invention adjusts safety redundant warnings according to the speed limit of each risk factor to avoid the impact of a single factor that is too low or too high on driving safety. Compared with the current safety driving system, which is generally a single factor warning in a specific environment, the present invention is unique and provides an effective method path for achieving the goals of "zero fatalities" and "continuous reduction of accidents" in road traffic during driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is an overall flow chart of an embodiment of the present invention.

[0027] Figure 2 The present invention is a detailed flow chart of applying the method of the present invention to provide real-time driving warning.

[0028] Figure 3 Schematic diagram of a wooden barrel in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments.

[0030] like Figure 1 As shown, an embodiment of the present invention discloses a driving warning method based on a dynamic balance wooden barrel risk control model. First, each driving defense risk factor and each driving environment risk factor are quantified as a wooden board length; then the minimum length of each driving defense risk factor is selected as the driving defense DD (Driving Defense) board index value Ldd, and the minimum length of each driving environment risk factor is selected as the driving environment risk DE (Driving Environment) board index value Lde; then the driving safety risk value V is calculated according to the DD board index value, DE board index value, vehicle information comprehensive factor index value R, and the safety redundancy loop adjustment angle θ determined according to the vehicle speed; finally, V is compared with the set risk level warning value, and a warning is issued according to the set warning rules. The V value calculation formula is expressed as:

[0031]

[0032] Specifically, firstly, according to the relevant definitions of factors such as traffic weather (visibility / visible distance), traffic roads (road type / road environment), traffic flow, traffic control level, and traffic vehicle-road coordination level, and in combination with national and local traffic safety regulations on roads, each factor is quantified into the environmental risk factor wooden board length Len according to the risk level, and the relevant data of each environmental risk factor in the driving environment is obtained through an intelligent terminal or a big data platform and quantified into the wooden board length Len, and the driving environment risk index value Lde is calculated according to the short board theory; and according to the traffic macro data, the driver's following habits, intersection habits, lane changing habits, and driving behavior data such as fatigue and distraction are statistically analyzed, and the risk levels are divided according to appropriate proportions and the corresponding level values ​​are quantified into the driving defense risk factor wooden board length Ldm, and the current driver's driving behavior data is obtained in real time through an intelligent terminal or a big data platform, and the wooden board length Ldm corresponding to the current driving defense risk factors is calculated, and the driving defense risk factor index Ldd is calculated according to the short board theory; in addition, the vehicle information comprehensive index R is calculated according to the weight ratio according to factors such as vehicle size, fault, model, load (overload / overweight), mileage, etc. Through the use of road traffic macro data to collect warning values ​​of drivers' risk levels during driving, drivers can be given graded warnings of traffic risks in real time and at the right time in various scenarios.

[0033] The dynamic balancing wooden barrel risk control model in the embodiment of the present invention is a key control model of short-term risks in road traffic based on the "golden section wooden barrel dynamic balancing principle". During road traffic driving, the golden section method is used to analyze various factors of environmental risks that cause accidents (DE board) and defense capabilities to prevent accidents (DD board), and then the vehicle condition risks are comprehensively considered. The ASLM (Active / Safety / Logic / algorithm / Module) risk control safety adjustment loop is used to control the safe vehicle speed and calculate whether the driving risk reaches the corresponding risk warning level.

[0034] The embodiments of the present invention determine the safe driving status based on national and local road traffic safety laws and regulations, combined with the driving environment and people's (statistically significant) general driving behavior habits, to achieve real-time and timely warnings, and adjust the warning value through the safe vehicle speed, which has the system viscosity that is widely used for social drivers and passengers.

[0035] Combine the following Figure 2 , the process of performing real-time driving warning in an embodiment of the present invention is described in detail.

[0036] First, according to the risk factors in the driving process, the driving defense risk factors and environmental risk factors in the driving process are quantified into the lengths of the wooden board Ldm and Len respectively. Specifically, the driving defense force DD board index value Ldd is calculated according to the driving defense risk factors such as following habits, lane changing habits, intersection habits, fatigue, and distraction. Ldd is the minimum index value of each defense risk factor in the driving environment, that is,

[0037] Ldd=min(Ld1, Ld2, Ld3…, Ldm). The driving environment risk DE board index value Lde is calculated based on environmental risk factors such as low visibility / low sight distance (sky), road environment (ground), traffic congestion / sudden changes / interference (flow), road control level of the traffic management bureau (control), and vehicle / road intelligent coordination level (intelligence). Lde is the minimum index value of each environmental risk factor corresponding to the driving environment, that is, Lde=min(Le1, Le2, Le3…, Len).

[0038] During driving, when the driving environment risk increases, that is, the length of the environmental risk factor board Lde becomes smaller, the driving defense risk also increases, that is, the length of the driving defense risk factor board Ldd also becomes smaller; according to the statistical analysis of Lde and Ldd after quantification of traffic big data, it is found that when the driving defense ability is good, the environmental risk (DE board) index value is the golden section point of the maximum index value of the driving defense DD board, that is, (Ldd-Lde) / Ldd=0.618, which means that even if the driving risk can be reduced through the safety redundancy loop, its adjustment angle is not less than arctan2.

[0039] Then, the comprehensive factor index value R of vehicle information is calculated based on factors such as vehicle size, fault (high-risk fault), vehicle model, load (overload / overweight), mileage, etc., which is obtained by weighting and quantifying the information of each factor, R=k1H1+k2H2+k3H3+…+kiHi, ki is the weight of each factor, and Hi is the quantified value of the corresponding factor.

[0040] like Figure 3 As shown, in the embodiment of the present invention, the driving risk is reduced by adjusting the safe vehicle speed through the safety redundancy loop. The safety redundancy loop is adjusted by the angle θ between the bottom of the wooden barrel and the horizontal plane, and its value range is 0-arctan2. The relationship between the vehicle speed v and θ is:

[0041]

[0042] Where v0 is the vehicle speed limit under the shortest board risk factor.

[0043] According to the formula Substitute Ldd, Lde, R and θ to calculate the driving safety risk value V.

[0044] Let the constants V0, V1, and V2 be the warning values for high, medium, and low risk levels corresponding to the driving risk V during driving. When V < V0, an accident may occur during driving; when V0 <= V <= V1, the vehicle is in a high-risk driving state and a manual intervention warning is issued; when V1 < V <= V2, the vehicle is in a medium-risk driving state and a voice reminder warning is issued; when V > V2, the vehicle is in a low-risk driving state, and risks are everywhere during driving.

[0045] When θ = 0°, when the vehicle condition R is defined as good and takes the value Rs, L0, L1, and L2 are the risk level index values corresponding to each risk factor during driving. The relationship between the risk level warning value and the risk level index value is: V0 = L0 × πRs 2 ; V1 = L1 × πRs 2 ; V2 = L2 × πRs 2 .

[0046] When θ = 0°, in the case where each risk factor is good, let L be the minimum value of Ldd and Lde. R0, R1, and R2 are the risk level index values corresponding to the vehicle information comprehensive factor during driving. The relationship between them and the risk level warning value is:

[0047] During the real-time warning process, when Ldd, Lde, and R are all good, no warning is issued. When Ldd, Lde, or R reaches the high, medium, or low risk level index value, the driving safety risk value V is calculated according to the foregoing formula, and a driving risk reminder is given according to the calculation result.

[0048] The following takes visibility and fatigue as examples to give an exemplary description of the driving warning method based on the dynamic balance barrel risk control model.

[0049] In an example, assume that during driving, visibility is the shortest board, that is, the index values of each risk factor except visibility are higher than the index value (board length) corresponding to visibility.

[0050] Let the index of visibility be Le1. According to the requirements of national and local road traffic safety regulations for the corresponding speed limits on high-speed roads when the low visibility of road traffic in bad weather is less than 200, 100, and 50 meters, the values corresponding to visibility Le1 are shown in Table 1.

[0051] Table 1 Visibility Quantification Table

[0052] visibility Index value (Le1) >＝200 LS 100-200 L2 50-100 L1 <50 L0

[0053] In the above table, Ls represents good visibility, L0 < L1 < L2 < Ls, and the specific values can be incremental point values such as 1, 2, 3, 4 or 100, 200, 300, 400, etc.; they can also be adjusted according to the big data statistics.

[0054] When other environmental factors are good, then Lde = min(Le1, … Len) = Le1. According to the classification of defense risk factors such as drivers' following - distance habits, lane - changing habits, intersection habits, fatigue level, distraction level, etc. in big - data statistics, calculate the corresponding index values of each risk factor Ldm, and then calculate the current driving defense force Ldd according to Ldd = min(Ld1, Ld2, Ld3…, Ldm).

[0055] When the visibility decreases, the safe driving speed can be adjusted through the safety redundancy loop to reduce driving risks. The relationship between the current vehicle speed v and θ is as follows:

[0056]

[0057] Through big - data statistical analysis, in the high - speed environment, v0 corresponding to the visibility is 60 km / h.

[0058] Calculate the corresponding driving risk V according to the following driving risk index value calculation formula:

[0059]

[0060] Compare the values of V with those of V0, V1, and V2, and give early warnings according to the early - warning rules.

[0061] In another example, let the index of fatigue level be Ld1. According to the safety warning reminder for overtime driving in the national standard "GBT19056 - 2021", the index values of the risk levels corresponding to the fatigue - driving risk factor in driving defense force are defined as shown in Table 2.

[0062] Table 2 Quantification table of fatigue level

[0063] Driving time T (hours) Index value Ld1 T<3 LS 3-3.5 L2 3.5-4 L1 T>＝4 L0

[0064] In the above table, Ls indicates not being fatigued, and the values of L0 < L1 < L2 < Ls are consistent with the visibility.

[0065] When the fatigue factor is the smallest among the index values of driving defense risk factors, then Ldd = min(Ld1, Ld2, Ld3…, Ldm) = Ld1. According to the current driving environment, calculate the value of Lde for each driving environment, and obtain the corresponding Lde value according to Lde = min(Le1, … Len).

[0066] When Ldd <= Lde after calculation, the driving risk index value V = πR 2Ldd, then the driving warning during driving is fatigue warning. Compare V with V0, V1, V2 values, and issue warnings based on the index values. When T>=4, if the driving stay time is monitored to be >20min, it will be restored to Ls, otherwise when driving again, it will remind you "Exceeded driving, you have been driving continuously for XX hours and XX minutes!"

[0067] A computer system disclosed in an embodiment of the present invention includes a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor. When the computer program / instruction is executed by the processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model are implemented.

[0068] A computer-readable storage medium is disclosed in an embodiment of the present invention. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model are implemented.

[0069] A computer program product disclosed in an embodiment of the present invention includes a computer program / instruction, and when the computer program / instruction is executed by a processor, the steps of the driving warning method based on the dynamic balancing barrel wind control model are implemented.

Claims

1. A driving warning method based on a dynamic balancing barrel wind control model, characterized in that: include: Each driving defense risk factor and each driving environment risk factor are quantified as the length of the wooden board; The minimum length of each driving defense risk factor is selected as the driving defense DD board index value Ldd; The minimum length of each driving environment risk factor is selected as the driving environment risk DE board index value Lde; The driving safety risk value V is calculated based on the DD plate index value, the DE plate index value, the vehicle information comprehensive factor index value R, and the safety redundancy loop adjustment angle θ determined according to the vehicle driving speed: Compare V with the set risk level warning value and issue a warning based on the set warning rules.

2. The driving warning method based on the dynamic balance barrel wind control model according to claim 1 is characterized in that: The driving defense risk factors include one or more of the driver's following habits, lane changing habits, intersection habits, fatigue, and distraction; the driving environment risk factors include one or more of traffic weather, traffic roads, traffic flow, traffic control level, and traffic vehicle-road coordination level.

3. The driving warning method based on the dynamic balance barrel wind control model according to claim 1 is characterized in that: The vehicle information comprehensive factor index value is obtained by quantifying and weighting multiple factors including vehicle size, fault, model, load, and mileage.

4. The driving warning method based on the dynamic balance barrel wind control model according to claim 1 is characterized in that: The relationship between the vehicle speed v and the safety redundancy loop adjustment angle θ is: Among them, v0 is the vehicle speed limit under the shortest board risk factor.

5. The driving warning method based on the dynamic balance barrel wind control model according to claim 1 is characterized in that: Each risk factor is quantified into wooden board lengths L0, L1 and L2 representing high risk, medium risk and low risk respectively according to their respective risk levels; Based on L0, L1 and L2, calculate the corresponding risk level warning values ​​V0, V1 and V2, where V0 = L0 × πRs 2 , V1=L1×πRs 2 , V2=L2×πRs 2 , Rs is the comprehensive factor index value of vehicle information when the vehicle is in good condition.

6. The driving warning method based on the dynamic balance barrel wind control model according to claim 5 is characterized in that: It also includes determining the high, medium and low risk level index values ​​R0, R1 and R2 corresponding to the vehicle information comprehensive factor according to the minimum value L of Ldd and Lde, where 7. The driving warning method based on the dynamic balance barrel wind control model according to claim 1 is characterized in that: When Ldd, Lde and R are all good, no warning is issued. When Ldd, Lde or R reaches a high, medium or low risk level indicator value, the driving safety risk value V is calculated and a driving risk reminder is issued based on the calculation result.

8. A computer system comprising a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor, characterized in that: When the computer program / instructions are executed by the processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by the processor, the steps of a driving warning method based on a dynamic balancing barrel wind control model according to any one of claims 1 to 7 are implemented.

Images