CN112070419B - Method for quantifying risk of potential dangerous situation of automobile driving - Google Patents
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Abstract
The invention discloses a method for quantifying risk degrees of potential dangerous situations of automobile driving, which comprises the steps of extracting primary, intermediate and final risk factors in a grading manner according to potential dangerous situation components, calculating the risk degree membership degree of the final risk factors, calculating weight values of the primary risk factors and the intermediate risk factors, and calculating the risk degree membership degree of the primary risk factors; and finally, calculating the risk degree of the potential dangerous situation of the automobile driving according to the weight value of the primary risk element and the membership degree of the risk degree, the driving speed and the potential dangerous movement speed. The method can measure risk situations by using the quantization index, and has the advantages of quantization, simplicity and high quantization precision.
Description
Technical Field
The invention belongs to the technical field of driver safety risk management, and particularly relates to a method for quantifying risk of a potential dangerous situation of automobile driving.
Background
The risk identification of the driver is a special risk management behavior, is a driving behavior for the driver to identify and evaluate the risk degree of the traffic environment and adjust the running state of the vehicle according to the risk degree to ensure safe running, and is closely related to road traffic safety. At present, students at home and abroad research various methods for evaluating the dangerous recognition capability of drivers, but the accuracy and the effectiveness of evaluation results obtained by the evaluation methods are questioned. The risk of a potentially dangerous situation in driving an automobile is one of the important factors affecting the reliability of the evaluation results. Different potential dangerous situations of automobile driving have different risk degrees, so that different identification difficulties can be generated. If different drivers adopt potential dangerous situations with different risk degrees to evaluate, and the potential dangerous situations are not distinguished in an evaluation model, the reliability of an evaluation result is influenced. Therefore, quantification of the risk degree of the potential dangerous situations of the automobile driving is achieved, unified judging standards of the risk degree are obtained, and the reliability of the evaluation result of the potential dangerous identification capability of the driver can be further improved.
Disclosure of Invention
Therefore, the invention provides a method for quantifying risk of a potential dangerous situation of automobile driving, which is used for solving the problem of accuracy of the identification capability evaluation of the potential dangerous situation of automobile driving. The invention has the characteristics of simple quantization method and high quantization precision.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for quantifying risk of a potential dangerous situation of automobile driving comprises the following steps:
s1: extracting elements which influence the risk degree of the potential dangerous situations of the automobile driving as first-level risk elements according to the components of the potential dangerous situations of the automobile driving;
s2: extracting elements which influence the risk degree of the primary risk elements as secondary risk elements according to the composition components of the primary risk elements;
s3: extracting elements which influence the risk degree of the secondary risk factors as tertiary risk elements according to the composition components of the secondary risk factors;
definition: all elements participating in further extraction in the primary risk elements are called primary risk elements, all elements participating in further extraction in the secondary risk elements are called intermediate risk elements, and elements not participating in further extraction in the secondary risk elements and all tertiary risk elements are called final risk elements;
s4: according to the influence degree of the final-stage risk elements on the risk degree of the potential dangerous situations of the automobile driving, calculating the risk degree membership degree of the final-stage risk elements by adopting a method combining a grade evaluation system and a fuzzy statistical method;
s5: according to the influence degree of the primary risk elements and the intermediate risk elements on the risk degree of the potential dangerous situations of the automobile driving, respectively calculating the weight values of the primary risk elements and the intermediate risk elements by adopting a method combining scoring and analytic hierarchy process;
s6: calculating the risk degree membership of the corresponding primary risk element by adopting a fuzzy comprehensive evaluation method according to the weight value of the intermediate risk element and the risk degree membership of the corresponding final risk element;
s7: and calculating the risk degree of the potential dangerous situation of the automobile driving by adopting a fuzzy comprehensive evaluation method according to the weight value of the primary risk element and the membership degree of the risk degree and the ratio of the driving speed to the potential dangerous movement speed of the automobile driving.
The first-level risk element comprises: 1) driving environment characteristics, 2) potential danger prompt information characteristics, 3) potential danger characteristics, 4) correlation between potential danger prompt information and potential danger, and 5) driving speed; wherein the driving speed does not participate in the further extraction.
The secondary risk element comprises: 1) Traffic environment, road line type and traffic environment illuminance under driving environment characteristics; 2) The motion state, the position, the color and the size of the potential danger prompt information under the characteristics of the potential danger prompt information; 3) Motion state, position, color and size under potentially dangerous characteristics; 4) The correlation degree of the potential danger prompt information and the potential danger correlation degree is strong and weak; the correlation degree of the potential danger prompt information and the potential danger is not involved in further extraction.
The three-level risk element comprises: 1) Urban roads, highways, mountain roads and suburban roads in traffic environment; 2) Straight roads, slopes, curves and intersections under the road line type; 3) Cloudy and cloudy days, light rain, light snow, light fog, heavy rain, heavy snow, heavy fog and night under the illumination of the traffic environment; 4) The potential danger prompt information and the potential danger are static, slow moving, medium moving and high moving in the motion state; 5) The potential danger prompt information and the right front, the left front, the right front and the rear of the position where the potential danger is located; 6) The potential danger prompt information and the color of the potential danger are vivid, bright, dark and close to the environment; 7) The potential danger prompt information and the size of the potential danger are large, medium, small and tiny.
In step S4, 5-level evaluation systems including high, medium, low and low are adopted; according to a 5-level evaluation system, firstly, evaluating final-level risk elements according to the influence degree of the risk elements on the risk degree of the potential dangerous situations of the automobile driving by evaluation experts, and then, carrying out statistical analysis on the evaluation results of all the evaluation experts by adopting a fuzzy statistical method to calculate the duty ratio of the number of experts in each evaluation level, namely the risk degree membership degree of the risk elements in the evaluation level.
In step S5, scoring is adopted, and each sub-element contained in each type of primary risk element and each type of intermediate risk element are compared and scored pairwise according to the influence degree of the sub-elements on the risk degree of the potential dangerous situation of the automobile driving; the scoring scale is:
the scoring method comprises the following steps: and compared with the risk element B, the influence degree of the risk element A on the risk degree of the potential dangerous situation of the automobile driving is closer to the type in the scoring scale, the corresponding value is selected for the A, and then the weight values of the primary risk element and the intermediate risk element are calculated by adopting a hierarchical analysis method according to the scoring result of an expert.
In step S6, the risk degree membership of the corresponding primary risk element is obtained by multiplying the weight value of the intermediate risk element and the risk degree membership of the corresponding final risk element.
In step S7, taking the maximum value of the product of the weighted values of all the primary risk factors and the membership degree of the risk degree, and multiplying the maximum value by the ratio of the running speed to the movement speed of the potential danger of the automobile driving, thereby obtaining the risk degree of the potential danger situation of the automobile driving.
The beneficial effects of the invention are as follows: the method can be used for measuring the risk of the potential dangerous situation of the automobile driving by using the quantization index, and has the advantages of quantization, simplicity and high quantization precision. Other features and advantages of the invention will be apparent from the description that follows.
Drawings
Fig. 1 is a flowchart of a method for quantifying risk of a potentially dangerous scenario in driving an automobile according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples, but it should be understood by those skilled in the art that the following examples are not to be construed as limiting the technical scope of the present invention, and any equivalent transformation or modification made within the spirit of the technical scope of the present invention should be considered as falling within the scope of the present invention.
Referring to fig. 1, the invention provides a method for quantifying risk of a potential dangerous situation of automobile driving, comprising the following steps:
s1: and extracting primary risk elements of the potential dangerous scene of the automobile driving. According to the composition of the potential dangerous scene of the automobile driving, the elements which can influence the risk degree of the potential dangerous scene of the automobile driving are mainly analyzed: the method comprises the following steps of (1) driving environment characteristics, (2) potential danger prompt information characteristics, (3) potential danger characteristics, (4) correlation degree of potential danger prompt information and potential danger and (5) driving speed, and therefore the elements are extracted to be used as primary risk elements of the automobile driving potential danger scene, namely the elements are expressed as follows:
R＝{R 1 ,R 2 ,R 3 ,R 4 ,R 5 the vehicle is characterized by } = { driving environment characteristic, potential danger prompt information characteristic, potential danger prompt information and potential danger relativity, driving speed }.
S2: and extracting secondary risk factors of the potential dangerous situations of automobile driving. Further analysis includes: (1) The factors influencing the risk of the driving environment characteristics mainly include: traffic environment, road line type and traffic environment illuminance; (2) The factors that influence the risk of the potential hazard prompt information characteristics mainly include: the motion state, the position, the color and the size; (3) The factors that influence the risk of potentially dangerous characteristics are mainly: the motion state, the position, the color and the size; (4) The factors influencing the risk degree of the correlation degree of the potential danger prompt information and the potential danger are mainly the intensity of the correlation degree of the potential danger prompt information and the potential danger; (5) No risk factor affects the risk of the driving speed, so that the driving speed does not need to be subdivided into two-stage risk factors. Therefore, the elements are extracted as secondary risk factors of the automobile driving potential dangerous situations, namely the following expression is carried out:
R 1 ＝{R 11 ,R 12 ,R 13 "traffic environment, road line shape, traffic environment illuminance
R 2 ＝{R 21 ,R 22 ,R 23 ,R 24 The } = { motion state, position, color, size }
R 3 ＝{R 31 ,R 32 ,R 33 ,R 34 The } = { motion state, position, color, size }
R 4 ＝{R 41 ,R 42 ,R 43 ,R 44 The } = { strong, medium, weak, uncorrelated }
S3: and extracting three-level risk elements of the potential dangerous scene of the automobile driving. According to the composition of the secondary risk factors, the following further analysis is carried out: (1) The elements that influence the risk of the traffic environment are mainly: urban roads, highways, mountain roads, suburban roads; (2) The factors that influence the risk of road line type mainly include: straight roads, ramps, curves, intersections; (3) The elements that influence the risk of the traffic environment illuminance mainly include: cloudy and cloudy days, small rain, small snow, small fog, heavy rain, heavy snow, heavy fog and night; because the influence of sunny days, cloudy days and cloudy days on the risk degree is of the same level, the risk degree is commonly attributed to the same type of elements; similarly, the influence of light rain, light snow, light fog, heavy rain, heavy snow, heavy fog and night on the risk degree is divided into the same type of elements; (4) The elements that influence the risk of the potentially dangerous prompt information and the potentially dangerous movement state mainly include: stationary, slow moving, medium speed moving, high speed moving; (5) The elements that influence the risk of the potential danger prompt information and the position of the potential danger mainly include: front, left front, right front, rear; (6) The elements that influence the risk of the potential hazard prompt information and the color of the potential hazard are mainly: vivid, bright, dark, and close to the environment; (7) The factors that influence the risk degree of the potential hazard information and the potential hazard size mainly include: big, medium, small, tiny; (8) The method has the advantages that no risk factors influence the degree of correlation between the potential danger prompt information and the potential danger, so that the degree of correlation between the potential danger prompt information and the potential danger does not need to be subdivided into three levels of risk factors. The above elements are thus extracted as three-level risk elements of a potentially dangerous scenario for driving a car, namely:
R 11 ＝{R 111 ,R 112 ,R 113 ,R 114 "urban road, expressway, mountain road, suburban road
R 12 ＝{R 121 ,R 122 ,R 123 ,R 124 } = { straight road, ramp, curve, intersection }
R 13 ＝{R 131 ,R 132 ,R 133 ,R 134 The method comprises the steps of } = { cloudy and cloudy days on sunny days, light rain, light snow, light fog, heavy rain, heavy snow, heavy fog and night }
R 21 ＝{R 211 ,R 212 ,R 213 ,R 214 The device comprises a device body, a control system and a control system, wherein } = { stationary, slow-speed moving, medium-speed moving and high-speed movingMove }
R 22 ＝{R 221 ,R 222 ,R 223 ,R 224 "forward, left, right, forward, rear
R 23 ＝{R 231 ,R 232 ,R 233 ,R 234 "clear, bright, dark, and environment-friendly
R 24 ＝{R 241 ,R 242 ,R 243 ,R 244 } = { big, medium, small, tiny }
R 31 ＝{R 311 ,R 312 ,R 313 ,R 314 } = { stationary, slow moving, medium moving, high moving }
R 32 ＝{R 321 ,R 322 ,R 323 ,R 324 "forward, left, right, forward, rear
R 33 ＝{R 331 ,R 332 ,R 333 ,R 334 "clear, bright, dark, and environment-friendly
R 34 ＝{R 341 ,R 342 ,R 343 ,R 344 } = { big, medium, small, tiny }
The driving speed in the first-level risk elements, the degree of correlation between the potential danger prompt information and the potential danger in the second-level risk elements and all the third-level risk elements are the most intuitive characteristic expressions of the potential danger situations of automobile driving, so that the extraction cannot be continued. Therefore, all the elements participating in further extraction in the primary risk elements are called primary risk elements; all the elements participating in further extraction in the secondary risk element are called as intermediate risk elements; the elements of the secondary risk factors that cannot be further extracted and all the tertiary risk factors are referred to as final risk factors. This definition is described below.
S4: and (5) calculating the risk degree membership degree of the final risk element of the potential dangerous scene of the automobile driving. The 5-level evaluation system is adopted to evaluate the influence degree of the final-level risk factors on the risk degree of the potential dangerous situation of the automobile driving, and the evaluation level is expressed as follows:
M＝{m 1 ,m 2 ,m 3 ,m 4 ,m 5 } = { high, medium low, low }
According to the evaluation system, calculating the risk degree membership of the final risk element, wherein the method comprises the following steps: firstly, an evaluation group expert evaluates the final-stage risk elements according to the influence degree of the risk elements on the risk degree of the potential dangerous situations of the automobile driving, for example, a person considers that the influence degree of a certain risk element on the risk of the potential dangerous situations of the automobile driving is very large, the evaluation is high, and a person considers that the influence degree of the risk element is not large, and the evaluation is medium.
And then, carrying out statistical analysis on the evaluation results of all the evaluation experts by adopting a fuzzy statistical method, and calculating the duty ratio of the number of experts in each evaluation grade, namely the risk degree membership of the risk element in the evaluation grade. For example, the influence degree of the risk factor of the urban road on the risk degree of the potential dangerous situation of the automobile driving is evaluated, 10 evaluation experts are total, wherein 4 experts are regarded as high, 2 experts are regarded as medium and high, 1 expert is regarded as medium, 2 experts are regarded as medium and low, 1 expert is regarded as low, the risk degree membership degree of the risk factor belonging to the high is 4/10, the risk degree membership degree belonging to the medium and high is 2/10, the risk degree membership degree belonging to the medium is 1/10, the risk degree membership degree belonging to the medium is 2/10, and the risk degree membership degree belonging to the low is 1/10.
Counting according to the method, and recording:
(1) The risk degree membership degree of each three-level risk element in the traffic environment is as follows:
urban road: m is M 111 ＝[m 1111 ,m 1112 ,m 1113 ,m 1114 ,m 1115 ]
Expressway: m is M 112 ＝[m 1121 ,m 1122 ,m 1123 ,m 1124 ,m 1125 ]
Mountain highway: m is M 113 ＝[m 1131 ,m 1132 ,m 1133 ,m 1134 ,m 1135 ]
Suburban highway: m is M 114 ＝[m 1141 ,m 1142 ,m 1143 ,m 1144 ,m 1145 ]
(2) The risk degree membership degree of each three-level risk element in the road line type is as follows:
straight path: m is M 121 ＝[m 1211 ,m 1212 ,m 1213 ,m 1214 ,m 1215 ]
Ramp(s): m is M 122 ＝[m 1221 ,m 1222 ,m 1223 ,m 1224 ,m 1225 ]
And (3) bending: m is M 123 ＝[m 1231 ,m 1232 ,m 1233 ,m 1234 ,m 1235 ]
Intersection: m is M 124 ＝[m 1241 ,m 1242 ,m 1243 ,m 1244 ,m 1245 ]
(3) The risk degree membership degree of each three-level risk element in the traffic environment illuminance is as follows:
sunny and cloudy days: m is M 131 ＝[m 1311 ,m 1312 ,m 1313 ,m 1314 ,m 1315 ]
Small rain, small snow and fog: m is M 132 ＝[m 1321 ,m 1322 ,m 1323 ,m 1324 ,m 1325 ]
Heavy rain, heavy snow and fog: m is M 133 ＝[m 1331 ,m 1332 ,m 1333 ,m 1334 ,m 1335 ]
Night: m is M 134 ＝[m 1341 ,m 1342 ,m 1343 ,m 1344 ,m 1335 ]
(4) The risk degree membership degree of each three-level risk element in the motion state of the potential danger prompt information is as follows:
and (3) standing: m is M 211 ＝[m 2111 ,m 2112 ,m 2113 ,m 2114 ,m 2115 ]
And (3) moving slowly: m is M 212 ＝[m 2121 ,m 2122 ,m 2123 ,m 2124 ,m 2125 ]
And (3) medium-speed movement: m is M 213 ＝[m 2131 ,m 2132 ,m 2133 ,m 2134 ,m 2135 ]
High-speed movement: m is M 214 ＝[m 2141 ,m 2142 ,m 2143 ,m 2144 ,m 2145 ]
(5) The risk degree membership degree of each three-level risk element in the position of the potential danger prompt information is as follows:
directly in front of: m is M 221 ＝[m 2211 ,m 2212 ,m 2213 ,m 2214 ,m 2215 ]
Front left: m is M 222 ＝[m 2221 ,m 2222 ,m 2223 ,m 2224 ,m 2225 ]
Front right: m is M 223 ＝[m 2231 ,m 2232 ,m 2233 ,m 2234 ,m 2235 ]
The rear: m is M 224 ＝[m 2241 ,m 2242 ,m 2243 ,m 2244 ,m 2245 ]
(6) The risk degree membership degree of each three-level risk element in the color of the potential danger prompt information is as follows:
vivid: m is M 231 ＝[m 2311 ,m 2312 ,m 2313 ,m 2314 ,m 2315 ]
Color cast: m is M 232 ＝[m 2321 ,m 2322 ,m 2323 ,m 2324 ,m 2325 ]
Dark color: m is M 233 ＝[m 2331 ,m 2332 ,m 2333 ,m 2334 ,m 2335 ]
Close to the environment: m is M 234 ＝[m 2341 ,m 2342 ,m 2343 ,m 2344 ,m 2345 ]
(7) The risk degree membership degree of each three-level risk element in the potential risk prompt information is as follows:
large: m is M 241 ＝[m 2411 ,m 2412 ,m 2413 ,m 2414 ,m 2415 ]
In (a): m is M 242 ＝[m 2421 ,m 2422 ,m 2423 ,m 2424 ,m 2425 ]
The size is small: m is M 243 ＝[m 2431 ,m 2432 ,m 2433 ,m 2434 ,m 2435 ]
Tiny: m is M 244 ＝[m 2441 ,m 2442 ,m 2443 ,m 2444 ,m 2445 ]
(8) The risk degree membership of each three-level risk element in the potential dangerous exercise state is as follows:
and (3) standing: m is M 311 ＝[m 3111 ,m 3112 ,m 3113 ,m 3114 ,m 3115 ]
And (3) moving slowly: m is M 312 ＝[m 3121 ,m 3122 ,m 3123 ,m 3124 ,m 3125 ]
And (3) medium-speed movement: m is M 313 ＝[m 3131 ,m 3132 ,m 3133 ,m 3134 ,m 3135 ]
High-speed movement: m is M 314 ＝[m 3141 ,m 3142 ,m 3143 ,m 3144 ,m 3145 ]
(9) The risk degree membership of each tertiary risk element in the potential hazard location is:
directly in front of: m is M 321 ＝[m 3211 ,m 3212 ,m 3213 ,m 3214 ,m 3215 ]
Front left: m is M 322 ＝[m 3221 ,m 3222 ,m 3223 ,m 3224 ,m 3225 ]
Front right: m is M 323 ＝[m 3231 ,m 3232 ,m 3233 ,m 3234 ,m 3235 ]
The rear: m is M 324 ＝[m 3241 ,m 3242 ,m 3243 ,m 3244 ,m 3245 ]
(10) The risk degree membership of each tertiary risk element in the potential risk color is:
vivid: m is M 331 ＝[m 3311 ,m 3312 ,m 3313 ,m 3314 ,m 3315 ]
Color cast: m is M 332 ＝[m 3321 ,m 3322 ,m 3323 ,m 3324 ,m 3325 ]
Dark color: m is M 333 ＝[m 3331 ,m 3332 ,m 3333 ,m 3334 ,m 3335 ]
Close to the environment: m is M 334 ＝[m 3341 ,m 3342 ,m 3343 ,m 3344 ,m 3345 ]
(11) The risk degree membership degree of each three-level risk element in the potential risk size is as follows:
large: m is M 341 ＝[m 3411 ,m 3412 ,m 3413 ,m 3414 ,m 3415 ]
In (a): m is M 342 ＝[m 3421 ,m 3422 ,m 3423 ,m 3424 ,m 3425 ]
The size is small: m is M 343 ＝[m 3431 ,m 3432 ,m 3433 ,m 3434 ,m 3435 ]
Tiny: m is M 344 ＝[m 3441 ,m 3442 ,m 3443 ,m 3444 ,m 3445 ]
(12) The risk degree membership degree of each secondary risk element in the correlation degree of the potential risk prompt information and the potential risk is as follows:
strong: m is M 41 ＝[m 411 ,m 412 ,m 413 ,m 414 ,m 415 ]
In (a): m is M 42 ＝[m 421 ,m 422 ,m 423 ,m 424 ,m 425 ]
Weak: m is M 43 ＝[m 431 ,m 432 ,m 433 ,m 434 ,m 435 ]
No correlation: m is M 44 ＝[m 441 ,m 442 ,m 443 ,m 444 ,m 445 ]
S5: and calculating weight values of primary risk elements and intermediate risk elements of the potential dangerous scene of the automobile driving. The method comprises the following steps:
and scoring is adopted, and each sub-element contained in each class of primary risk elements and each sub-element contained in each class of intermediate risk elements are respectively compared and scored pairwise according to the influence degree of the sub-elements on the risk degree of the potential dangerous situation. The scoring scale is:
the scoring method comprises the following steps: and compared with the risk element B, the influence degree of the risk element A on the risk degree of the potential dangerous situation of the automobile driving is closer to the type of the scoring scale, and then the corresponding value is selected for the risk element A. For example, if risk element a has a significantly greater impact on the risk level of an automotive driving potential hazard scenario than risk element B, then scoring a as 3 points; if the degree of influence is significantly less than B, A is scored as 1/3.
And (3) adopting a plurality of evaluation experts, respectively scoring according to a scoring scale and a scoring method, and calculating weight values of the primary risk elements and the intermediate risk elements by adopting a hierarchical analysis method according to the scoring result of the experts, wherein the weight values are marked as follows:
(1) The weight value of each sub-element in the primary risk element is as follows:
w R ＝[w 1 ,w 2 ,w 3 ,w 4 ]
w 1 is the weight of the driving environment characteristic, w 2 Characteristic weight of potential danger prompt information, w 3 Weight of potentially dangerous characteristics, w 4 The relevance of the potential hazard cue information to the potential hazard is weighted.
(2) The weight value of the intermediate risk element under the driving environment characteristic is as follows:
w 11 is the weight of the traffic environment, w 12 Is the line weight of the road, w 13 Is the traffic environment illumination weight.
(3) The weight values of the intermediate risk elements under the characteristics of the potential danger prompt information are as follows:
w 21 is the motion state weight, w 22 Weight for the position of the position, w 23 Is the color weight, w 24 Is a size weight.
(4) The weight values of the medium-level risk factors under the potential dangerous characteristics are as follows:
w 31 is the motion state weight, w 32 Weight for the position of the position, w 33 Is the color weight, w 34 Is a size weight.
S6: and (5) calculating the risk degree membership degree of primary risk elements of the potential dangerous scene of the automobile driving. And calculating the risk degree membership of the primary risk elements of the potential risk scene of the automobile driving by adopting a fuzzy comprehensive evaluation method according to the weight value of the intermediate risk elements and the type and the risk degree membership of the final risk elements. The method comprises the following steps:
(1) The risk degree membership of the driving environment characteristics is as follows:
(2) The risk degree membership of the potential danger prompt information characteristic is as follows:
(3) The risk degree membership of the potentially dangerous characteristics is:
(4) The risk degree membership of the correlation degree of the potential danger prompt information and the potential danger is as follows:
M 4 ＝[m 41 ,m 42 ,m 43 ,m 44 ,m 45 ]＝[m 4n1 ，m 4n2 ，m 4n3 ，m 4n4 ，m 4n5 ]
wherein i, j, k, m, n is selected as one of 1, 2, 3 or 4 according to the type of the final risk element of the driving potential dangerous situation of the automobile. For example, if the traffic environment of a certain car driving potential dangerous scenario is an urban road, M 1 I of (1) is selected as 1.
S7: and calculating the risk degree of the potential dangerous situation of the automobile driving. And calculating the risk degree of the potential dangerous situation of the automobile driving by adopting a fuzzy comprehensive evaluation method according to the weight value of the primary risk element and the membership degree of the risk degree and the proportional relation between the driving speed and the potential dangerous movement speed of the automobile driving. Namely:
wherein v is the running speed of the automobile, v 0 And a potentially dangerous speed for driving the automobile.
The above-described embodiment is a preferred embodiment of the present invention, but the present invention is not limited to the above-described embodiment, and any obvious modifications, substitutions or variations that can be made by one skilled in the art without departing from the spirit of the present invention are within the scope of the present invention.
Claims (8)
1. The method for quantifying the risk of the potential dangerous situation of the automobile driving is characterized by comprising the following steps of:
s1: extracting elements which influence the risk degree of the potential dangerous situations of the automobile driving as first-level risk elements according to the components of the potential dangerous situations of the automobile driving;
s2: extracting elements which influence the risk degree of the primary risk elements as secondary risk elements according to the composition components of the primary risk elements;
s3: extracting elements which influence the risk degree of the secondary risk factors as tertiary risk elements according to the composition components of the secondary risk factors;
definition: all elements participating in further extraction in the primary risk elements are called primary risk elements, all elements participating in further extraction in the secondary risk elements are called intermediate risk elements, and elements not participating in further extraction in the secondary risk elements and all tertiary risk elements are called final risk elements;
s4: according to the influence degree of the final-stage risk elements on the risk degree of the potential dangerous situations of the automobile driving, calculating the risk degree membership degree of the final-stage risk elements by adopting a method combining a grade evaluation system and a fuzzy statistical method;
s5: according to the influence degree of the primary risk elements and the intermediate risk elements on the risk degree of the potential dangerous situations of the automobile driving, respectively calculating the weight values of the primary risk elements and the intermediate risk elements by adopting a method combining scoring and analytic hierarchy process;
s6: calculating the risk degree membership of the corresponding primary risk element by adopting a fuzzy comprehensive evaluation method according to the weight value of the intermediate risk element and the risk degree membership of the corresponding final risk element;
s7: and calculating the risk degree of the potential dangerous situation of the automobile driving by adopting a fuzzy comprehensive evaluation method according to the weight value of the primary risk element and the membership degree of the risk degree and the ratio of the driving speed to the potential dangerous movement speed of the automobile driving.
2. The method for quantifying risk of a potentially dangerous scenario for driving an automobile according to claim 1, wherein the primary risk elements include: 1) driving environment characteristics, 2) potential danger prompt information characteristics, 3) potential danger characteristics, 4) correlation between potential danger prompt information and potential danger, and 5) driving speed; wherein the driving speed does not participate in the further extraction.
3. The method for quantifying risk of a potentially dangerous scenario for driving an automobile according to claim 2, wherein the secondary risk factors include: 1) Traffic environment, road line type and traffic environment illuminance under driving environment characteristics; 2) The motion state, the position, the color and the size of the potential danger prompt information under the characteristics of the potential danger prompt information; 3) Motion state, position, color and size under potentially dangerous characteristics; 4) The correlation degree of the potential danger prompt information and the potential danger correlation degree is strong and weak; the correlation degree of the potential danger prompt information and the potential danger is not involved in further extraction.
4. A method for quantifying risk of a potentially dangerous situation for driving an automobile according to claim 3, wherein said three-level risk elements comprise: 1) Urban roads, highways, mountain roads and suburban roads in traffic environment; 2) Straight roads, slopes, curves and intersections under the road line type; 3) Cloudy and cloudy days, light rain, light snow, light fog, heavy rain, heavy snow, heavy fog and night under the illumination of the traffic environment; 4) The potential danger prompt information and the potential danger are static, slow moving, medium moving and high moving in the motion state; 5) The potential danger prompt information and the right front, the left front, the right front and the rear of the position where the potential danger is located; 6) The potential danger prompt information and the color of the potential danger are vivid, bright, dark and close to the environment; 7) The potential danger prompt information and the size of the potential danger are large, medium, small and tiny.
5. The method for quantifying risk of automotive driving potential dangerous situations according to claim 1, wherein in step S4, a 5-level evaluation system is adopted, including high, medium, low, and medium; according to a 5-level evaluation system, firstly, evaluating final-level risk elements according to the influence degree of the risk elements on the risk degree of the potential dangerous situations of the automobile driving by evaluation experts, and then, carrying out statistical analysis on the evaluation results of all the evaluation experts by adopting a fuzzy statistical method to calculate the duty ratio of the number of experts in each evaluation level, namely the risk degree membership degree of the risk elements in the evaluation level.
6. The method for quantifying the risk level of an automotive driving potential dangerous situation according to claim 1, wherein in step S5, scoring is adopted to score each sub-element included in each type of primary risk element and each type of intermediate risk element in a pairwise manner according to the influence degree of the sub-element on the risk level of the automotive driving potential dangerous situation;
the scoring scale is:
the scoring method comprises the following steps: and compared with the risk element B, the influence degree of the risk element A on the risk degree of the potential dangerous situation of the automobile driving is closer to the type in the scoring scale, the corresponding value is selected for the A, and then the weight values of the primary risk element and the intermediate risk element are calculated by adopting a hierarchical analysis method according to the scoring result of an expert.
7. The method for quantifying risk of an automotive driving potential dangerous situation according to claim 1, wherein in step S6, the risk degree membership of the intermediate risk element and the corresponding final risk element are multiplied to obtain the risk degree membership of the corresponding primary risk element.
8. The method for quantifying the risk level of the automobile driving potential dangerous situation according to claim 1, wherein in step S7, the risk level of the automobile driving potential dangerous situation is obtained by taking the maximum value of the product of the weight values of all primary risk elements and the risk level membership and multiplying the maximum value by the ratio of the driving speed to the movement speed of the automobile driving potential dangerous situation.
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