CN111177361A - Intelligent networking automobile accident reduction amount calculation method - Google Patents
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Abstract
The invention relates to an intelligent networked automobile accident reduction amount calculation method, which comprises the following steps: screening to obtain a sample document; extracting effective information of the sample literature to obtain the safety benefit of each technology in each sample literature; carrying out heterogeneity check on the safety benefit of each technology in each sample document, and excluding unsuitable sample documents; merging the safety benefits of each technology in each sample document by using a random effect model to obtain the merged safety benefits of each technology in the sample document; based on the merged safety benefit, performing bias test on the safety benefit of each technology in each sample document by using a funnel graph to obtain the final merged safety benefit of each technology in the sample document; and obtaining the accident reduction amount of the intelligent networked automobile based on the final combined safety benefit. Compared with the prior art, which technology is urgently needed to be realized in the current scene can be judged according to the obtained accident reduction amount, so that the technology is deployed in practice to achieve the purpose of reducing accidents.
Description
Technical Field
The invention relates to the field of traffic safety, in particular to an intelligent networked automobile accident reduction amount calculation method.
Background
Traffic accidents have been a serious social problem worldwide. The consequences of tragic traffic accidents have to put more attention to traffic safety situations, and the continuously advancing scientific technology is applied to the research work of traffic safety, so that automobiles can benefit human beings better, and various countries are taking countermeasures from roads and traffic engineering to different degrees.
With the popularization of emerging technologies such as big data and cloud computing, the intelligent networked automobile (CAV) has rapidly developed, and possibility is brought to the reduction of human errors in the driving process. At present, the CAV technology is vigorously pursued in china and the united states, wherein forward collision warning, vulnerable traffic participant collision warning, intersection action assisting systems, blind spot warning systems and lane change warning systems are generally concerned in two countries.
CAV comprises technologies in multiple aspects, and the safety benefits brought by different technologies are different, so that the safety benefits obtained by different technologies have important significance for reducing traffic accidents under different situations.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an intelligent networked automobile accident reduction amount calculation method.
The purpose of the invention can be realized by the following technical scheme:
an intelligent networked automobile accident reduction amount calculation method comprises the following steps:
step S1: screening to obtain a sample document;
step S2: extracting effective information of the sample literature to obtain the safety benefit of each technology in each sample literature;
step S3: carrying out heterogeneity check on the safety benefit of each technology in each sample document, and excluding unsuitable sample documents;
step S3: merging the safety benefits of each technology in each sample document by using a random effect model to obtain the merged safety benefits of each technology in the sample document;
step S4: based on the merged safety benefit, performing bias test on the safety benefit of each technology in each sample document by using a funnel graph to obtain the final merged safety benefit of each technology in the sample document;
step S5: and obtaining the accident reduction amount of the intelligent networked automobile based on the final combined safety benefit.
The screening conditions are as follows:
whether related indexes of safety benefit exist or not;
whether multiple technologies are involved;
whether the safety benefit is the severity of the collision;
whether the size of the data volume is definite;
whether based on the secondary utilization of existing research.
The safety benefit Y for each technique in each sample document is:
wherein, PWithFor the probability of occurrence of an accident when equipping the technology, PWithoutThe accident probability when the technology is not equipped is as follows:
or
Or
Or
Wherein, TTC is the time of collision,TTCthas time-to-collision threshold, TTCallFor all collision times, TET is the sum of the time when the collision time is less than the collision time threshold, MLD is the number of lane departure meters, DM is the driving distance, WLD is the lane departure warning, and TT is the total time.
The Q statistics of the heterogeneity test are:
wherein i is the ith document, g is the number of documents involved in each technique, and WiAs a weight of each document, YiThe safety benefits of each technology in each document.
The combined safety benefits of each technique in the sample literature are:
where k is the number of documents involved in each technique after heterogeneity test, WreFor each document's correction weight obtained by a random effect model, WreExpressed as:
where se is the standard error and is expressed as:
in step S4, the bias is adjusted by a trimming method.
The step S5 includes:
calculating the accident reduction amount by independently utilizing each technology;
and calculating and comprehensively utilizing the accident reduction amount of all the technologies.
The accident reduction amount by using each technology alone is obtained by using the accident reduction ratio by using each technology alone, and the accident reduction ratio P by using each technology aloneAExpressed as:
wherein, PCIs the proportion of accidents that occur each year in a pre-crash scenario,
to finally merge the security benefits;
the accident reduction amount of the comprehensive utilization of the prior art is obtained by comprehensively utilizing the accident reduction ratio of the prior art, wherein the accident reduction ratio CE of the comprehensive utilization of the prior art is as follows:
where m is the number of classes of technology and s is the number of documents involved in each technology after bias test.
Compared with the prior art, the invention has the following advantages:
the safety benefits of each technology are objectively analyzed, the actual scene is combined, the accident reduction amount of the intelligent networked automobile is obtained, and the technology which needs to be realized in the current scene can be judged according to the obtained accident reduction amount, so that the technology is deployed in practice to achieve the purpose of reducing accidents.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a diagram of a combined safety benefit funnel for each technique of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The embodiment provides an intelligent networked automobile accident reduction amount calculation method, as shown in fig. 1, including the steps of:
step S1: screening to obtain a sample document;
step S2: extracting effective information of the sample literature to obtain the safety benefit of each technology in each sample literature;
step S3: carrying out heterogeneity check on the safety benefit of each technology in each sample document, and excluding unsuitable sample documents;
step S3: merging the safety benefits of each technology in each sample document by using a random effect model to obtain the merged safety benefits of each technology in the sample document;
step S4: based on the merged safety benefit, performing bias test on the safety benefit of each technology in each sample document by using a funnel graph to obtain the final merged safety benefit of each technology in the sample document;
step S5: and obtaining the accident reduction amount of the intelligent networked automobile based on the final combined safety benefit.
Specifically, the method comprises the following steps:
(1) document retrieval is performed using "Google Scholar", "sciences direct", AND "Scopus", AND the retrieval keywords take the form of "technical name abbreviation 'OR' technical name full name 'AND' Safe '" AND "technical name abbreviation' OR 'technical name full name' AND 'Crash' OR 'accession'".
The relevant documents were screened using 5 screening conditions: whether related indexes of safety benefit exist or not; whether multiple technologies are involved; whether the safety benefit is the severity of the collision; whether the size of the data volume is definite; whether based on the secondary utilization of existing research. The references are shown in table 1.
(2) For the incorporated documents, extracting effective information and establishing a database, wherein the effective information comprises: an author; the year; the country in which the experiment was conducted; safety benefits; the size of the data volume; standard error; and (4) technical categories. The safety benefit and standard error can not be directly obtained from all the documents, if a document contains the above two items, the document is directly used, but for the research not containing the above items, the document is converted by using a benefit calculation formula, and the safety benefit Y of each technology in each sample document is shown in formula (1).
Wherein, PWithFor the probability of occurrence of an accident when equipping the technology, PWithoutThe accident probability when the technology is not provided is selected from the formulas (2), (3), (4) and (5) because the accident probability is more in a research evaluation mode. In addition, if the safety benefit of a certain technology in a certain document varies within a certain range, the average value is taken as the safety benefit. For documents without standard errors, the standard errors are calculated by using a method of calculating the standard errors by using a non-contrast two-classification model, see formula (6).
Wherein: wherein, TTC is collision time, the smaller the TTC, the larger the collision risk, the TTCthFor time to collision threshold, take 1.5s, TTCallTime of impact, T, for all collisionsET is the sum of the time that the collision time is less than the collision time threshold, MLD is the number of lane departure meters, DM is the mileage traveled, WLD is the lane departure warning, TT is the total time, and g is the number of documents involved in each technology.
(3) For meta-analysis, two models are commonly used, one being a fixed effect model and the other being a random effect model. The statistical approach to the fixed-effect model assumes homogeneity of variances for individual studies, such that the variance of the combined estimate of the effect magnitude includes only variances within individual studies. The stochastic effect model does not assume the homogeneity of the variances of the individual studies, and the variance of the effect magnitude comprehensive estimation comprises the variances in the individual studies and the variances among the individual studies. The stochastic effect model results are more general. The stochastic effect model considers an inter-group additive estimate in addition to the intra-group variance. The stochastic effect model calculation is shown in equations (7) and (8). Where k is the number of documents involved in each technique after heterogeneity testing.
Wherein, WreFor each document's correction weight obtained by a random effect model, WiWeight of each document before random Effect modeling, WiOne-half the variance of Y, Q is the Q statistic for the heterogeneity test, where se is derived based on k, as shown in equation (9).
Wherein i is the i-th document, YiThe safety benefits of each technology in each document.
The combined safety benefit of each technique in the sample literature is shown in equation (10).
The 95% confidence interval CI for the weighted estimated security benefit can be calculated by equation (11).
TABLE 1 literature data sheet (incomplete)
(4) Funnel plots were used to examine the presence and extent of publication bias. Delivery bias refers to the tendency of a study report to be unpublished, e.g., the results of the study are not statistically significant, or are considered anomalous, hard to interpret, or even undesirable. However, this also results in an assessment result that is not objective, and only if the bias is removed, the assessment result can be closest to the true benefit. The combined safety benefit funnel diagram of each technology is shown in fig. 2, wherein the ordinate of fig. 2 is the combined safety benefit, and the ordinate is the standard error. The bias was adjusted using a clipping and padding approach by filling the missing part of the funnel map with k changed to s, resulting in the final merged safety benefit for each technique in the sample literature
(5) And respectively calculating the accident reduction amount of each technology which is independently utilized and the accident reduction amount of all the technologies which are comprehensively utilized according to the proportion of the accident types of China, America, Canada, Australia, New Zealand, UK and India as the weight, and establishing the corresponding intelligent networking technology development strategy.
Relevant research of the U.S. highway traffic safety bureau shows that the safety benefit is generally expressed as the number of accidents reduced each year, and the formula (12) is used for calculating the number of potential target breakdowns which can be avoided by the intelligent networking technology aiming at a certain pre-collision scene. However, since the present embodiment considers a plurality of countries, and since the difference between the vehicle retention amount and the number of accidents in each country is large, it is difficult to use the change in the number of accidents as an evaluation index, the amount of reduction in accidents using each technology is calculated using equation (13).
In the formula: n is a radical ofAA reduced number of incidents with each technology alone;
NCthe number of accidents that occur each year in a pre-crash scenario;
PA-accident reduction rate for each technology is used separately;
PC-the proportion of accidents that occur each year in a pre-crash scenario.
Since a pre-crash scenario may involve multiple intelligent networking technologies, the accident reduction amount of all technologies needs to be calculated and comprehensively utilized, as shown in equation (14). However, this calculation may partially exaggerate the results of the study because there is usually an intersection between the various techniques. However, since a conservative value is selected when the security benefit of each intelligent networking technology is estimated, the exaggerated effect is relieved to some extent. And calculating the accident reduction proportion CE of each country after the comprehensive application of the intelligent network connection technology by using a formula (14).
The following is a specific example:
first, document retrieval is performed using "Google Scholar", "sciences direct", AND "scope", AND the retrieval keywords take the form of "technical name abbreviation 'OR' technical name full name 'AND' Safe '" AND "technical name abbreviation' OR 'technical name full name' AND 'blast' OR 'accession'". 912 studies associated with CAV technology were retrieved and 73 safety benefit-related documents were screened, this example involving 8 CAV technologies.
Then, in the meta-analysis, the random effect model is used for combining effect quantities, publication bias is checked through a funnel diagram, and the safety benefit of each technology is objectively evaluated by adjusting through a shearing and supplementing method. The safety benefit of the automatic emergency braking system in the aspect of front-end collision avoidance is found to be greater than that of the self-adaptive endurance system and the forward collision early warning system; lane-change warning systems have a higher safety benefit in changing lanes than blind-spot warning systems. The safety benefit of each CAV technology is objectively evaluated, developers are helped to make decisions on the development priority of various CAV technologies, and countries are helped to make development strategies of intelligent networking technologies.
And finally, calculating the accident reduction amount of each technology independently utilized by each country and the accident reduction amount of all technologies comprehensively utilized by each country according to the accident data of 7 countries. The results show that if all 8 CAV technologies are implemented, an average 37.87% reduction in accidents can be achieved, with a maximum indian reduction of 48.89%, a reduction of 40.53% in the united states, a reduction of 27.39% in canada and a reduction of 32.02% in china. In addition, based on the accident reduction amount by using each technology alone, different countries should have different emphasis on the development of CAV technology, china should preferentially develop lane change warning technology and pedestrian collision warning technology, the united states, australia, uk and canada should preferentially develop automatic emergency braking system, and new zealand and india should preferentially develop electronic stability control system.
Compared with the prior art, the embodiment has the following advantages:
the security benefits that the intelligent networking technology can exert under multiple conditions are considered, meta-analysis is carried out on 8 important and common intelligent networking technologies, and the security benefits of the technologies are contrastively analyzed.
Aiming at 7 major countries, the safety benefit of the intelligent networking technology to each country is analyzed based on accident data and meta-analysis results.
And intelligent networking technology development strategies and priorities of various countries are recommended.
Claims (8)
1. An intelligent networked automobile accident reduction amount calculation method is characterized by comprising the following steps:
step S1: screening to obtain a sample document;
step S2: extracting effective information of the sample literature to obtain the safety benefit of each technology in each sample literature;
step S3: carrying out heterogeneity check on the safety benefit of each technology in each sample document, and excluding unsuitable sample documents;
step S3: merging the safety benefits of each technology in each sample document by using a random effect model to obtain the merged safety benefits of each technology in the sample document;
step S4: based on the merged safety benefit, performing bias test on the safety benefit of each technology in each sample document by using a funnel graph to obtain the final merged safety benefit of each technology in the sample document;
step S5: and obtaining the accident reduction amount of the intelligent networked automobile based on the final combined safety benefit.
2. The intelligent networked automobile accident reduction amount calculation method according to claim 1, wherein the screening condition is as follows:
whether related indexes of safety benefit exist or not;
whether multiple technologies are involved;
whether the safety benefit is the severity of the collision;
whether the size of the data volume is definite;
whether based on the secondary utilization of existing research.
3. The intelligent networked automobile accident reduction amount calculation method according to claim 1, wherein the safety benefit Y of each technology in each sample document is as follows:
wherein, PWithFor the probability of occurrence of an accident when equipping the technology, PWithoutThe accident probability when the technology is not equipped is as follows:
or
Or
Or
Wherein TTC is time to collision, TTCthAs time-to-collision threshold, TTCallFor all collision times, TET is the sum of the time when the collision time is less than the collision time threshold, MLD is the number of lane departure meters, DM is the driving distance, WLD is the lane departure warning, and TT is the total time.
4. The intelligent networked automobile accident reduction amount calculation method according to claim 3, wherein the Q statistic of the heterogeneity test is as follows:
wherein i is the ith document, g is the number of documents involved in each technique, and WiAs a weight of each document, YiThe safety benefits of each technology in each document.
5. The intelligent networked automobile accident reduction amount calculation method according to claim 4, wherein the combined safety benefits of each technology in the sample documents are as follows:
where k is the number of documents involved in each technique after heterogeneity test, WreFor each document's correction weight obtained by a random effect model, WreExpressed as:
where se is the standard error and is expressed as:
6. the method of claim 1, wherein the step S4 is performed by adjusting the bias using a clipping method.
7. The intelligent networked automobile accident reduction amount calculation method according to claim 1, wherein the step S5 includes:
calculating the accident reduction amount by independently utilizing each technology;
and calculating and comprehensively utilizing the accident reduction amount of all the technologies.
8. The method as claimed in claim 7, wherein the accident reduction amount of each technology is calculated by using the accident reduction ratio of each technology separately, and the accident reduction ratio P of each technology separatelyAExpressed as:
wherein, PCIs the proportion of accidents that occur each year in a pre-crash scenario,
to finally merge the security benefits;
the accident reduction amount of the comprehensive utilization of the prior art is obtained by comprehensively utilizing the accident reduction ratio of the prior art, wherein the accident reduction ratio CE of the comprehensive utilization of the prior art is as follows:
where m is the number of classes of technology and s is the number of documents involved in each technology after bias test.
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