CN101819718B - Identifying and early warning method for traffic accidents - Google Patents
Identifying and early warning method for traffic accidents Download PDFInfo
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- CN101819718B CN101819718B CN 201010156105 CN201010156105A CN101819718B CN 101819718 B CN101819718 B CN 101819718B CN 201010156105 CN201010156105 CN 201010156105 CN 201010156105 A CN201010156105 A CN 201010156105A CN 101819718 B CN101819718 B CN 101819718B
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Abstract
The invention relates to an identifying and early warning method for traffic accidents. The method comprises the following steps of: setting a preset database, obtaining an early warning identification parameter group at a section where traffic accidents frequently happen, and judging the vehicle length types of a vehicle to be detected and adjacent vehicles ahead and behind; judging whether the vehicles are safe or not according to a safe stopping distance, and directly carrying out accident monitoring and error correction if a safety state is determined; otherwise, judging the type of an accident according to the safe stopping distance, the running speed and the vehicle length types, carrying out corresponding accident type alarm, starting a rescue response and carrying out the accident monitoring and error correction; monitoring whether the actual traffic safety status and a predicted status are consistent, if so, directly judging the traffic safety status of the next vehicle; and otherwise, updating the preset database, and judging the traffic safety status of the next vehicle. The invention has the remarkable advantages of strong engineering practicability, high detection precision, low accident error alarming rate, real-time detection, rapid response, low cost, easy collection and good self-adaptivity.
Description
Technical field
The invention belongs to the tunnel safety management domain, specifically a kind of management control method that utilizes the artificial immune system principle to carry out the early warning of tunnel traffic identification of accidental events.
Background technology
The tunnel traffic accident detects early warning technology as the important component part of the operational management system of highway, can be divided into non-automatic detection technique and Automatic Measurement Technique two classes.After the sixties in 20th century, many developed countries begin notice is concentrated in the operational management of highway in the world, dispose respectively the traffic surveillance and control system of the perfect in shape and function take electronic equipment as means at highway, carried out simultaneously the research to traffic accidents early warning detection algorithm.
On the basis of empirical algorithms, along with the development of the new and high technologies such as mathematical theory and artificial intelligence, a series of advanced event detection algorithms according to the artificial intelligence theory occur in succession in early days.Intelligent algorithm is to adopt some to have the algorithm of selfdiscipline or ability of self-teaching, carries out intelligently the traffic hazard state being judged.
The defective of existing traffic hazard detection technique: most of traffic hazard early warning detection algorithms belong to the theoretical research achievement, designed, designed research range and restriction, and with the mode verification algorithm of off-line, application to engineering practice is less, and because data acquisition cost height or acquisition mode are not easy to realize, detect so that vehicle supervision department is unwilling to adopt this theory or algorithm to carry out traffic hazard, and have the threshold value marked ratio shortcomings such as high, the portability of difficulty, the alert rate of mistake and adaptivity be relatively poor.
Summary of the invention
The purpose of this invention is to provide that a kind of engineering practicability is strong, accuracy of detection is high, low, the real-time detection of the alert rate of accident mistake, response rapidly, low, the easy collection of cost, identifying and early warning method for traffic accidents that adaptivity is good.
For achieving the above object, the technical solution used in the present invention is as follows: a kind of identifying and early warning method for traffic accidents, its key are, carry out according to following steps:
The minimal security braking distance comprises several like this situations, both vehicle in the lanes process with minimal security braking distance with car before and after the track, change trains in the driving process and minimal security braking distance with car before and after the track, and in the driving process of changing trains with adjacent track before and after the minimal security braking distance of car, can choose different minimal security braking distances according to different Vehicle Driving Cycle situations.With the prior art that is calculated as of minimal security braking distance in the lanes process, the minimum safe distance under four kinds of possibility collision situations the when Liao Chuanjin of University Of Chongqing and the Wang Wenxia of Jilin University also exchange the track has been made large quantity research, does not just do at this and does not give unnecessary details.
In addition, presetting database is provided with fine day, rainy day, snow sky and greasy weather four kinds of situations, should choose presetting database according to actual weather condition, makes detection more accurate, and the alert rate of mistake is lower.
Step 2 at black spot, is obtained early warning identification parameter group, and this early warning identification parameter group comprises the headstock distance L of i car and i-1 car, the travel speed V of an i car
i, an i car Vehicle length l
iVehicle length l with i-1 car
I-1This method adopts electronic equipment commonly used that the Dynamic Traffic Flow characteristic parameter data of traffic hazard multi-happening section are carried out Real-Time Monitoring, and engineering practicability is strong, and cost is low, and data easily gather, and has guaranteed the precision that detects.
Step 3 is judged the Vehicle length type, carries out according to following several steps:
The first step is judged the Vehicle length type of i car, if l
i〉=10m, then the Vehicle length type of i car is the cart length l
dIf 5m＜l
i＜10m, then the Vehicle length type of i car is middle vehicle commander's degree l
zIf l
i≤ 5m, then the Vehicle length type of i car is the dolly length l
x
Second step is judged the Vehicle length type of i-1 car, if l
I-1〉=10m, then the Vehicle length type of i-1 car is the cart length l
dIf 5m＜l
I-1＜10m, then the Vehicle length type of i-1 car is middle vehicle commander's degree l
zIf l
I-1≤ 5m, then the Vehicle length type of i-1 car is the dolly length l
x
Wherein, i-1 car is front truck, i car is rear car, to compare with the vehicle of i car as vehicle to be measured and its place ahead herein, monitor the security situation of vehicle to be measured and rear car such as need, then can obtain in step 2 the early warning identification parameter group of the rear car (both i+1 cars) of i car.The first step of step 3 and the determining step of second step order are interchangeable.
When the Vehicle length type of i car and i-1 car is respectively l
zAnd l
x, or be all l
xThe time, then belong to the situation in the described ordinary accident Vehicle length typelib; When the Vehicle length type of i car and i-1 car is respectively l
dAnd l
x, or be respectively l
dAnd l
z, or be all l
zThe time, then belong to the situation in the described emergent accident Vehicle length typelib; When the Vehicle length type of i car and i-1 car is respectively l
dAnd l
z, or be all l
dThe time, then belong to the situation in the described peril Vehicle length typelib.
The Vehicle length type can be done further to revise to the identification of traffic hazard as the aid identification parameter, guarantees accuracy of detection, the alert rate of reduction accident mistake.
Step 4 is according to ordinary accident minimal security braking distance L
S1Judge whether safety, if L 〉=L
S1, then be defined as safe condition; Otherwise accident pattern is judged;
Step 5, accident pattern is judged, if L
S2≤ L＜L
S1, then enter ordinary accident and judge flow process; If L
S3≤ L＜L
S2, then enter emergent accident and judge flow process; If L＜L
S3, then enter peril and judge flow process;
Braking distance is the main identification parameter of accident, can reduce thus many unnecessary determining steps, improves early warning efficient, shortens the response time;
Ordinary accident is judged flow process: judge V
iWhether less than or equal to V
s, otherwise carry out accident monitoring and error correction; If V
i≤ V
s, whether the Vehicle length type of then judging i car and i-1 car belongs to the situation in the ordinary accident Vehicle length typelib, otherwise carries out accident monitoring and error correction; Be, then be defined as ordinary accident, and slightly report to the police, start II level rescue response, carry out again accident monitoring and error correction;
Emergent accident is judged flow process: judge V
iWhether greater than V
sAnd less than or equal to V
q, otherwise carry out accident monitoring and error correction; If V
s＜V
i≤ V
q, whether the Vehicle length type of then judging i car and i-1 car belongs to the situation in the emergent accident Vehicle length typelib, otherwise carries out accident monitoring and error correction; Be, then be defined as emergent accident, and carry out moderate and report to the police, start I level rescue response, carry out again accident monitoring and error correction;
Peril is judged flow process: judge V
iWhether greater than V
q, otherwise carry out accident monitoring and error correction; If V
i＞V
q, whether the Vehicle length type of then judging i car and i-1 car belongs to the situation in the peril Vehicle length typelib, otherwise carries out accident monitoring and error correction; Be, then be defined as peril, and carry out severe and report to the police, start I level rescue response, carry out again accident monitoring and error correction;
Travel speed also as the main identification parameter of accident, makes early warning more accurate, greatly reduces the alert rate of mistake; The Vehicle length type can be done further to revise to the identification of traffic hazard as the aid identification parameter, guarantees accuracy of detection, the alert rate of reduction accident mistake.
Step 6, accident monitoring and error correction, carry out according to following several steps:
The first step, whether monitoring has an accident, if do not have an accident, then i=i+1 carries out the monitoring of next car, gets back to step 2; Have an accident if monitor, then carry out the database error correction;
Second step, the database error correction if monitor the generation ordinary accident, is then upgraded the default subdata base of ordinary accident, i=i+1, and get back to step 2; If monitor the generation emergent accident, then upgrade the default subdata base of emergent accident, i=i+1, and get back to step 2; Have an accident if monitor, then upgrade the default subdata base of peril, i=i+1, and get back to step 2;
Upgrade the default subdata base of ordinary accident: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described ordinary accident;
Upgrade the default subdata base of emergent accident: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described emergent accident;
Upgrade the default subdata base of peril: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described peril.
Utilize self study, the adaptive principle of artificial immune system, by learning training, casualty data is stored in the database, constantly update database, strengthen the adaptability of this method for early warning, accuracy and the response speed of the early warning of energy Effective Raise.
Described presetting database is divided into fine presetting database, rainy day presetting database, snow day presetting database and greasy weather presetting database, wherein, fine empirical data is housed in the described fine presetting database, the empirical data of rainy day is housed in the described rainy day presetting database, the empirical data in snow sky is housed in the described snow day presetting database, the empirical data in greasy weather is housed in the described greasy weather presetting database.
The shooting instrument is set in the highway section, obtain each default subdata base empirical data, according to different weather conditions, the empirical data of accident is packed in the default subdata base of each correspondence, calculate parameter preset in the presetting database by empirical data, such as expectation average overall travel speed V
q, actual average travel speed V
s, minimal security braking distance L
S1, L
S2, L
S3With the Vehicle length typelib.
Remarkable result of the present invention is: utilize self study, the adaptive principle of artificial immune system, constantly update database, strengthen the adaptability of method for early warning, the accuracy of Effective Raise early warning; With safe stopping distance and the travel speed main identification parameter as accident, reduce operation steps, improve response speed; The Vehicle length type is done further correction as the aid identification parameter, guarantee accuracy of detection, the alert rate of reduction accident mistake; Choose parameter preset in the different presetting database according to different weather conditions, greatly increased the accuracy rate that detects, reduced the rate of false alarm of early warning; And electronic equipment commonly used can be accomplished Real-Time Monitoring, and engineering practicability is strong, cost is low, data easily gather.
Description of drawings
Fig. 1 is main-process stream block diagram of the present invention;
Fig. 2 is the FB(flow block) that accident pattern is judged;
Fig. 3 is the FB(flow block) of accident monitoring and error correction.
Embodiment
Below in conjunction with the drawings and specific embodiments the present invention is described in further detail.
As shown in Figure 1, the present invention is a kind of identifying and early warning method for traffic accidents.
Presetting database is set, and this presetting database is equipped with expectation average overall travel speed V
q, actual average travel speed V
s, the default subdata base of ordinary accident, the default subdata base of emergent accident and the default subdata base of peril, in the default subdata base of described ordinary accident ordinary accident minimal security braking distance L is housed
S1With ordinary accident Vehicle length typelib, in the default subdata base of described emergent accident emergent accident minimal security braking distance L is housed
S2With emergent accident Vehicle length typelib, in the default subdata base of described peril peril minimal security braking distance L is housed
S3With peril Vehicle length typelib, wherein, L
S3＜L
S2＜L
S1
At black spot, obtain early warning identification parameter group, this early warning identification parameter group comprises the headstock distance L of i car and i-1 car, the travel speed V of an i car
i, an i car Vehicle length l
iVehicle length l with i-1 car
I-1
Judge the Vehicle length type, need to carry out length with car in its place ahead (both i-1 cars) to i car to be measured and judge:
Judge the Vehicle length type of i car, if l
i〉=10m, then the Vehicle length type of i car is the cart length l
dIf 5m＜l
i＜10m, then the Vehicle length type of i car is middle vehicle commander's degree l
zIf l
i≤ 5m, then the Vehicle length type of i car is the dolly length l
x
Judge the Vehicle length type of i-1 car, if l
I-1〉=10m, then the Vehicle length type of i-1 car is the cart length l
dIf 5m＜l
I-1＜10m, then the Vehicle length type of i-1 car is middle vehicle commander's degree l
zIf l
I-1≤ 5m, then the Vehicle length type of i-1 car is the dolly length l
x
According to safe stopping distance, judge whether safety, be safe condition if judge, then carry out accident monitoring and error correction; Otherwise accident pattern is judged;
Judge accident pattern according to safe stopping distance, travel speed and Vehicle length type, and report to the police according to corresponding accident pattern, start simultaneously the rescue response;
Accident monitoring and error correction, whether monitoring actual traffic safety case is consistent with predicted conditions, if inconsistent, then need upgrade presetting database;
I=i+1 obtains the early warning identification parameter group of next car again, detects the traffic safety status of next car.
As shown in Figure 2, judge traffic safety status:
According to ordinary accident minimal security braking distance L
S1Judge whether safety, if L 〉=L
S1, then be defined as safe condition; Otherwise accident pattern is judged;
Accident pattern is judged, if L
S2≤ L＜L
S1, then enter ordinary accident and judge flow process; If L
S3≤ L＜L
S2, then enter emergent accident and judge flow process; If L＜L
S3, then enter peril and judge flow process;
Ordinary accident is judged flow process: judge V
iWhether less than or equal to V
s, otherwise carry out accident monitoring and error correction; If V
i≤ V
s, judge then whether the Vehicle length type of i car and i-1 car is respectively l
zAnd l
x, or be all l
x, otherwise carry out accident monitoring and error correction; Be, then be defined as ordinary accident, and slightly report to the police, start II level rescue response, carry out again accident monitoring and error correction;
Emergent accident is judged flow process: judge V
iWhether greater than V
sAnd less than or equal to V
q, otherwise carry out accident monitoring and error correction; If V
s＜V
i≤ V
q, judge then whether the Vehicle length type of i car and i-1 car is respectively l
dAnd l
x, or be respectively l
dAnd l
z, or be all l
z, otherwise carry out accident monitoring and error correction; Be, then be defined as emergent accident, and carry out moderate and report to the police, start I level rescue response, carry out again accident monitoring and error correction;
Peril is judged flow process: judge V
iWhether greater than V
q, otherwise carry out accident monitoring and error correction; If V
i＞V
q, judge then whether the Vehicle length type of i car and i-1 car is respectively l
dAnd l
z, or be all l
d, otherwise carry out accident monitoring and error correction; Be, then be defined as peril, and carry out severe and report to the police, start I level rescue response, carry out again accident monitoring and error correction;
As shown in Figure 3, accident monitoring and error correction:
Whether monitoring has an accident, if do not have an accident, then i=i+1 carries out the monitoring of next car, gets back to step 2; Have an accident if monitor, then carry out the database error correction;
The database error correction if monitor the generation ordinary accident, is then upgraded the default subdata base of ordinary accident, i=i+1, and obtain the early warning identification parameter group of next car; If monitor the generation emergent accident, then upgrade the default subdata base of emergent accident, i=i+1, and obtain the early warning identification parameter group of next car; Have an accident if monitor, then upgrade the default subdata base of peril, i=i+1, and obtain the early warning identification parameter group of next car;
Upgrade the default subdata base of ordinary accident: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described ordinary accident;
Upgrade the default subdata base of emergent accident: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described emergent accident;
Upgrade the default subdata base of peril: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described peril.
Its working condition is as follows: presetting database is set, and obtains early warning identification parameter group at black spot; Judge the Vehicle length type of vehicle to be measured and its adjacent vehicle in front and back; Judge whether safety according to safe stopping distance, if safe condition then directly carries out accident monitoring and error correction; Otherwise, need according to safe stopping distance, travel speed and the Vehicle length type type of judging that accidents happened, and report to the police according to corresponding accident pattern, start simultaneously the rescue response, carry out again accident monitoring and error correction; Accident monitoring and error correction, whether monitoring actual traffic safety case is consistent with predicted conditions, if consistent, then directly carry out the traffic safety status of next car and judges; Otherwise, need to upgrade presetting database, again the traffic safety status of next car is judged.
Claims (2)
1. an identifying and early warning method for traffic accidents is characterized in that, carries out according to following steps:
Step 1 arranges presetting database, and this presetting database is equipped with expectation average overall travel speed V
q, actual average travel speed V
s, the default subdata base of ordinary accident, the default subdata base of emergent accident and the default subdata base of peril, in the default subdata base of described ordinary accident ordinary accident minimal security braking distance L is housed
S1With ordinary accident Vehicle length typelib, in the default subdata base of described emergent accident emergent accident minimal security braking distance L is housed
S2With emergent accident Vehicle length typelib, in the default subdata base of described peril peril minimal security braking distance L is housed
S3With peril Vehicle length typelib, wherein, L
S3＜L
S2＜L
S1
Step 2 at black spot, is obtained early warning identification parameter group, and this early warning identification parameter group comprises the headstock distance L of i car and i-1 car, the travel speed V of an i car
i, an i car Vehicle length l
iVehicle length l with i-1 car
I-1
Step 3 is judged the Vehicle length type, carries out according to following several steps:
The first step is judged the Vehicle length type of i car, if l
i〉=10m, then the Vehicle length type of i car is the cart length l
dIf 5m＜l
i＜10m, then the Vehicle length type of i car is middle vehicle commander's degree l
zIf l
i≤ 5m, then the Vehicle length type of i car is the dolly length l
x
Second step is judged the Vehicle length type of i-1 car, if l
I-1〉=10m, then the Vehicle length type of i-1 car is the cart length l
dIf 5m＜l
I-1＜10m, then the Vehicle length type of i-1 car is middle vehicle commander's degree l
zIf l
I-1≤ 5m, then the Vehicle length type of i-1 car is the dolly length l
x
Step 4 is according to ordinary accident minimal security braking distance L
S1Judge whether safety, if described L 〉=L
S1, then be defined as safe condition; Otherwise, carry out accident pattern and judge;
Step 5, accident pattern is judged, if described L
S2≤ L＜L
S1, then enter ordinary accident and judge flow process; If described L
S3≤ L＜L
S2, then enter emergent accident and judge flow process; If described L＜L
S3, then enter peril and judge flow process;
Ordinary accident is judged flow process: judge V
iWhether less than or equal to V
s, if not, then carry out accident monitoring and error correction; If V
i≤ V
s, whether the Vehicle length type of then judging i car and i-1 car belongs to the situation in the ordinary accident Vehicle length typelib: when the Vehicle length type of i car and i-1 car is respectively l
zAnd l
x, or be all l
xThe time belong to situation in the described ordinary accident Vehicle length typelib, if judge situation about not belonging in the ordinary accident Vehicle length typelib, then carry out accident monitoring and error correction; If judge situation about belonging in the ordinary accident Vehicle length typelib, then be defined as ordinary accident, and slightly report to the police, start II level rescue response, carry out again accident monitoring and error correction;
Emergent accident is judged flow process: judge V
iWhether greater than V
sAnd less than or equal to V
q, if not, then carry out accident monitoring and error correction; Whether the Vehicle length type of if so, then judging i car and i-1 car belongs to the situation in the emergent accident Vehicle length typelib: when the Vehicle length type of i car and i-1 car is respectively l
dAnd l
x, or be respectively l
dAnd l
z, or be all l
zThe time belong to situation in the described emergent accident Vehicle length typelib, if judge situation about not belonging in the emergent accident Vehicle length typelib, then carry out accident monitoring and error correction; If judge situation about belonging in the emergent accident Vehicle length typelib, then be defined as emergent accident, and carry out moderate and report to the police, start I level rescue response, carry out again accident monitoring and error correction;
Peril is judged flow process: judge V
iWhether greater than V
q, if not, then carry out accident monitoring and error correction; Whether the Vehicle length type of if so, then judging i car and i-1 car belongs to the situation in the peril Vehicle length typelib: when the Vehicle length type of i car and i-1 car is respectively l
dAnd l
z, or be all l
dThe time belong to situation in the described peril Vehicle length typelib, if judge situation about not belonging in the peril Vehicle length typelib, then carry out accident monitoring and error correction; If judge situation about belonging in the peril Vehicle length typelib, then be defined as peril, and carry out severe and report to the police, start I level rescue response, carry out again accident monitoring and error correction;
Step 6, accident monitoring and error correction, carry out according to following several steps:
The first step, whether monitoring has an accident, if do not have an accident, then i=i+1 carries out the monitoring of next car, gets back to step 2; Have an accident if monitor, then carry out the database error correction;
Second step, the database error correction if monitor the generation ordinary accident, is then upgraded the default subdata base of ordinary accident, i=i+1, and get back to step 2; If monitor the generation emergent accident, then upgrade the default subdata base of emergent accident, i=i+1, and get back to step 2; Have an accident if monitor, then upgrade the default subdata base of peril, i=i+1, and get back to step 2;
The default subdata base of wherein said renewal ordinary accident is specially: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described ordinary accident;
The default subdata base of described renewal emergent accident is specially: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described emergent accident;
The default subdata base of described renewal peril is specially: the Vehicle length type of described headstock distance L, an i car and i-1 car is put into the default subdata base of described peril.
2. identifying and early warning method for traffic accidents according to claim 1, it is characterized in that: described presetting database is divided into fine presetting database, rainy day presetting database, snow day presetting database and greasy weather presetting database, wherein, fine empirical data is housed in the described fine presetting database, the empirical data of rainy day is housed in the described rainy day presetting database, the empirical data in snow sky is housed in the described snow day presetting database, the empirical data in greasy weather is housed in the described greasy weather presetting database.
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|---|---|---|---|
| CN 201010156105 CN101819718B (en) | 2010-04-26 | 2010-04-26 | Identifying and early warning method for traffic accidents |
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| CN 201010156105 CN101819718B (en) | 2010-04-26 | 2010-04-26 | Identifying and early warning method for traffic accidents |
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| CN106228493A (en) * | 2016-08-01 | 2016-12-14 | 深圳市永兴元科技有限公司 | Car accident processing method and processing device |
| CN106448180B (en) * | 2016-10-24 | 2019-09-10 | 东南大学 | A kind of major long tunnel traffic events real-time detection method and detection system |
| CN106627466B (en) * | 2016-12-30 | 2019-03-22 | 安徽杰瑞信息科技有限公司 | Rent-a-car casualty loss quick response system |
| CN109410567B (en) * | 2018-09-03 | 2021-10-12 | 江苏大学 | Intelligent analysis system and method for accident-prone road based on Internet of vehicles |
| CN112863244B (en) * | 2019-11-28 | 2023-03-14 | 大众汽车股份公司 | Method and device for promoting safe driving of vehicle |
| CN115019532A (en) * | 2022-04-21 | 2022-09-06 | 东北林业大学 | Automatic identification and early warning system for potential safety hazards of roads based on passenger traffic data |
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