CN113989768A - Automatic driving test scene analysis method and system - Google Patents
Automatic driving test scene analysis method and system Download PDFInfo
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Abstract
The invention discloses an automatic driving test scene analysis method, which relates to the technical field of unmanned driving and comprises the steps of establishing a database; extracting frame-by-frame information of a scene to be analyzed and analyzing scene characteristic data, judging the similarity of the scene characteristic data and scene characteristic data stored in a database, and selecting the scene type with the highest similarity as the frame; screening out continuous multi-frame data belonging to the same scene category as a target scene segment; calculating the difficulty of each frame of data in the target scene segment; and screening continuous multi-frame data meeting the data difficulty requirement as a required scene segment. The invention provides a scheme of scene similarity initial screening and difficulty quantitative evaluation to determine the target segment, avoids the problem that the scene extraction and cutting can not meet the test requirement due to the fact that the quantity of data is insufficient and the scene can not be finely divided in the early development stage, and effectively reduces redundant scene segments.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to an automatic driving test scene analysis method and system.
Background
At present, two schemes are mostly adopted for processing test scene data of an automatic driving scene, and the processing modes of the data mainly comprise two schemes, one scheme is that manual screening and cutting are adopted, so that the whole process is time-consuming and labor-consuming, and the efficiency is low; and secondly, processing relevant characteristic quantity of original data, analyzing the original data based on the extracted characteristic and the original scene category characteristic, taking the starting and ending time points corresponding to the frame number (and the frames before and after the frame) meeting the condition as the starting and ending points of the scene data needing to be reserved, and automatically slicing the scene based on the starting and ending time points.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects of the prior art and provides an automatic driving test scene analysis method and system.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an automatic driving test scene analysis method comprises the following steps,
establishing a database, wherein different historical scene categories are stored in the database, and for each historical scene category, corresponding scene characteristic data is extracted to be used as a scene data reference for initial comparison;
extracting frame-by-frame information and analyzing scene characteristic data of a scene to be analyzed, judging the similarity of the scene characteristic data of each frame and historical scene characteristic data of scene categories stored in a database, and selecting the scene category corresponding to the scene characteristic data with the highest similarity as the scene category of the frame;
screening continuous multi-frame data belonging to the same scene category in the whole time span of a scene to be analyzed as a target scene segment;
based on the screened target scene segment, a fuzzy analytic hierarchy process is adopted to convert linguistic descriptions of relative importance degree judgment of multiple scoring experts on different indexes or different classes into corresponding fuzzy numbers to construct a fuzzy judgment matrix, and defuzzification processing is carried out on the fuzzy judgment matrix to obtain index weight based on subjective factor analysis, then, an entropy weight method is adopted, the weight of each index based on data drive is obtained by calculating the information entropy of each index, multiplying and normalizing the weight of each index with the index weight obtained based on the fuzzy analytic hierarchy process, determining the weight evaluation of each index relative to the difficulty of the corresponding frame, calculating the deviation between the related index and the optimal scheme and the worst scheme by adopting a TOPSIS (technique for order preference by similarity to similarity) method, and calculating the difficulty of each frame of data by combining the weight evaluation of each index relative to the difficulty of the corresponding frame;
and screening continuous multi-frame data meeting the data difficulty requirement as a required scene segment.
As a preferable aspect of the automatic driving test scenario analysis method of the present invention, wherein: the scene characteristic data comprises surrounding traffic participants, road identification and traffic light state information.
As a preferable aspect of the automatic driving test scenario analysis method of the present invention, wherein: the n frame data which are continuous and belong to the same scene category are screened out in the whole time span of the scene to be analyzed as target scene segments,
screening n frame data which are continuous and belong to the same scene category in the whole time span of the scene to be analyzed;
setting a threshold range of similarity;
and judging whether the similarity obtained by correspondingly calculating each frame data is within a set similarity threshold range, and if the similarity is within the set similarity threshold range, taking the continuous n frame data as a target scene segment.
As a preferable aspect of the automatic driving test scenario analysis method of the present invention, wherein: the screened continuous multi-frame data meeting the data difficulty requirement is used as a required scene segment,
recording start and stop time points of continuous multi-frame data meeting the data difficulty requirement to obtain a target time period, and forward and backward extending time t on the basis of the target time period to capture data from the scene time span to be analyzed by taking the time t as a reference point to obtain a required scene segment.
As a preferable aspect of the automatic driving test scenario analysis method of the present invention, wherein: the time t is generally 1-7 s.
The invention also provides an automatic driving test scene analysis system, which comprises,
the scene database layer is stored with a plurality of historical scene categories and historical scene characteristic data corresponding to each scene category;
the scene category judgment layer is used for extracting frame-by-frame information and analyzing scene characteristic data of a scene to be analyzed, carrying out similarity judgment on the scene characteristic data of each frame and historical scene characteristic data of scene categories stored in the database, selecting the scene category corresponding to the historical scene characteristic data with the highest similarity as the scene category of the frame, and screening out continuous multi-frame data belonging to the same scene category as a target scene segment;
a scene difficulty evaluation layer, which adopts a fuzzy analytic hierarchy process to convert linguistic descriptions of relative importance degree judgment of multiple scoring experts on different indexes or different classes into corresponding fuzzy numbers, constructs a fuzzy judgment matrix, and carries out defuzzification processing on the fuzzy judgment matrix to obtain index weights based on subjective factor analysis, then adopts an entropy weight method to calculate the information entropy of each index to obtain the weight of each index based on data driving, and multiplies and normalizes the weight of each index with the index weight obtained based on the fuzzy analytic hierarchy process to determine the weight evaluation of each index relative to the difficulty of the corresponding frame, then adopts a TOPSIS method to calculate the deviation between the related index and the optimal scheme and the worst scheme, and then combines the weight evaluation calculation of each index relative to the difficulty of the corresponding frame to obtain the difficulty of each frame data; and the number of the first and second groups,
and the scene intercepting layer is used for screening continuous multi-frame data meeting the data difficulty requirement and intercepting required scene fragments.
The invention has the beneficial effects that:
(1) the invention provides a layered scene automatic screening scheme, which is characterized in that a scheme of scene similarity primary screening and difficulty quantitative evaluation is adopted to determine a target segment, the scheme can avoid the problem that the scene extraction and cutting can not meet the test requirement due to the fact that the quantity of data is insufficient and the scene can not be finely divided in the early development stage, the redundant scene segments are effectively reduced, and the efficiency of evaluation and analysis is improved.
(2) The invention can realize quantitative analysis of scene difficulty, and avoid the problems of judgment difference caused by pure subjective factors.
(3) The fuzzy analytic hierarchy process, the entropy weight method and the TOPSIS method of the invention evaluate and calculate the difficulty of each frame of data in the target scene segment, thereby integrating the advantages of the subjective experience of experts and the characteristic distribution of the data, and having more persuasion to the result of quantitative evaluation of the difficulty; moreover, the system has strong expansibility, can directly stack indexes or categories influencing the quantitative evaluation of difficulty and difficulty for newly added scene categories and has good compatibility and strong flexibility.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an analysis method for an automatic driving test scenario provided by the present invention.
Detailed Description
In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
The embodiment provides an analysis method for an automatic driving test scene, which comprises steps S101 to S105, and the specific steps are as follows:
step S101: establishing a database, wherein a plurality of different historical scene categories are stored in the database, and for each historical scene category, corresponding historical scene characteristic data is extracted to be used as a scene data reference for initial comparison.
Specifically, historical data is stored in the database, and the historical data includes N scene categories and corresponding historical scene feature data. Specifically, the first scene type, the second scene type …, the nth scene type, the historical first scene feature data corresponding to the first scene type, the historical second scene feature data … corresponding to the second scene type, and the historical nth scene feature data corresponding to the nth scene type.
Step S102: extracting frame-by-frame information and analyzing scene characteristic data of a scene to be analyzed, judging the similarity of the scene characteristic data of each frame and historical scene characteristic data of different scene types stored in a database, and selecting the scene type corresponding to the scene characteristic data with the highest similarity as the scene type of the frame.
Step S103: and screening continuous multi-frame data belonging to the same scene category in the whole time span of the scene to be analyzed as a target scene segment.
Specifically, a threshold range of similarity is set, then the similarity of the screened continuous n frame data belonging to the same scene category is calculated, whether the similarity obtained by corresponding calculation of each frame data is within the set threshold range of similarity is judged, and if the similarity is within the set threshold range of similarity, the continuous n frame data is used as the target scene segment.
Step S104: based on the screened target scene segment, a fuzzy analytic hierarchy process is adopted to convert linguistic descriptions of relative importance degree judgment of multiple scoring experts on different indexes or different classes into corresponding fuzzy numbers to construct a fuzzy judgment matrix, and defuzzification processing is carried out on the fuzzy judgment matrix to obtain index weight based on subjective factor analysis, then, an entropy weight method is adopted, the weight of each index based on data drive is obtained by calculating the information entropy of each index, and multiplying and normalizing the weight of each index with the index weight obtained based on the fuzzy analytic hierarchy process to determine the weight evaluation of each index relative to the difficulty of the corresponding frame, calculating the deviation between the related index and the optimal scheme and the worst scheme by adopting a TOPSIS (technique for order preference by similarity to similarity) method, and calculating the difficulty of each frame of data by combining the weight evaluation of each index relative to the difficulty of the corresponding frame.
Step S105: and screening continuous multi-frame data meeting the data difficulty requirement as a required scene segment.
Specifically, continuous multi-frame data meeting the data difficulty requirement are screened out firstly, then starting and stopping time points of the multi-frame data are recorded to obtain a target time period, time t is extended forwards and backwards on the basis of the target time period, and data are intercepted from a scene time span to be analyzed by taking the time t as a reference point to obtain a required scene segment. And the target time periods are respectively expanded forward and backward for a period of time, so that a leader link of a small section of scene and a subsequent link of the scene are added on the basis of the extracted target scene segments, and the extracted scene is more complete. The time t can vary according to different scenes, and is generally 1-7 s, and in the embodiment, the extended time t is 5 s.
Therefore, the embodiment provides a hierarchical scene automatic screening scheme, the target segments are determined by adopting a scheme of scene similarity primary screening and difficulty quantitative evaluation, the scheme can avoid the problem that the test requirements cannot be met by scene extraction and cutting caused by the fact that the quantity of data cannot be finely divided in the early development stage, redundant scene segments are effectively reduced, and the efficiency of evaluation and analysis is improved.
The embodiment also provides an automatic driving test scene analysis system which comprises a scene database layer, a scene category judgment layer, a scene difficulty evaluation layer and a scene interception layer.
The scene database layer is stored with a plurality of historical scene categories and scene characteristic data corresponding to each historical scene category. Specifically, the scene classification may include a first scene classification, a second scene classification …, an nth scene classification, first scene feature data corresponding to the first scene classification, and second scene feature data … corresponding to the second scene classification and the nth scene feature data corresponding to the nth scene classification.
The scene category judgment layer is used for extracting frame-by-frame information and analyzing scene characteristic data of a scene to be analyzed, carrying out similarity judgment on the scene characteristic data of each frame and scene characteristic data of historical scene categories stored in the database, selecting the scene category corresponding to the scene characteristic data with the highest similarity as the scene category of the frame, and screening out continuous multi-frame data belonging to the same scene category as a target scene segment.
In the present embodiment, the scene feature data includes, but is not limited to, surrounding traffic participants (e.g., actual movement of different surrounding vehicles and pedestrians), road signs (e.g., whether to pass through an intersection, lane lines, speed limit, etc.), and traffic light status information.
It should be noted that, when a target scene segment is screened, a threshold range of similarity is set first, then the similarity of the screened continuous multi-frame data belonging to the same scene category is calculated, whether the similarity obtained by calculating the corresponding frame data is within the set threshold range of similarity is determined, and if all the similarities are within the set threshold range of similarity, the continuous multi-frame data is used as the target scene segment.
The scene difficulty evaluation layer is used for converting linguistic description of a scoring expert on importance degree judgment into corresponding fuzzy numbers by adopting a fuzzy analytic hierarchy process, constructing a fuzzy judgment matrix, performing defuzzification processing on the fuzzy judgment matrix to obtain index weights, then calculating the weight of each index by adopting an entropy weight method through calculating the information entropy of each index, multiplying and normalizing the weight by the index weight class obtained based on the fuzzy analytic hierarchy process to determine the weight evaluation of each index relative to the difficulty degree of a corresponding frame, then calculating the distance between the related index and an optimal scheme and a worst scheme by adopting a TOPSIS (technique for order preference) method, and calculating the difficulty degree of each frame data by combining the weight evaluation of each index relative to the difficulty degree of the corresponding frame.
The fuzzy analytic hierarchy process comprises the steps of obtaining fuzzy evaluation matrix, classifying the fuzzy evaluation matrix, and obtaining fuzzy evaluation result. After integrating the scoring results of a plurality of experts to generate a final fuzzy judgment matrix, performing defuzzification processing on the final fuzzy judgment matrix, and converting the final fuzzy judgment matrix into a final index weight.
The entropy weight method is characterized in that index weight is determined according to the characteristic of the order degree of information contained in each evaluation index, the information entropy of each index is calculated, the weight of each index is obtained through calculation, and the weight evaluation of each index relative to the difficulty of a corresponding frame can be determined after the weight of each index is multiplied and normalized by the weight obtained based on the fuzzy analytic hierarchy process.
On the basis, considering the difference of the dimensions of each index and the difference between each index and an ideal sequence, the TOPSIS method is adopted to process the related indexes, and the distance between the related indexes and the optimal scheme and the worst scheme is calculated. And the reference sequences of the optimal scheme and the worst scheme are derived from historical scene characteristic data of the same scene category in the database. On the basis, the difficulty degree calculation result of the corresponding frame can be obtained by combining the weight evaluation of each index obtained by calculation relative to the difficulty degree of the corresponding frame.
And the scene intercepting layer is used for screening continuous multi-frame data meeting the data difficulty requirement and intercepting required scene segments.
The automatic driving test scene analysis device is an automatic driving scene analysis system based on a similarity and difficulty level hierarchical analysis scheme, the scheme does not depend on manual extraction and screening, and does not need mass historical scene data to perform model training and feature extraction, so that the overall scene data interception efficiency is improved; meanwhile, the complexity of each scene can be quantitatively evaluated, the automatic driving scene data set is finely cut and extracted, partial redundant scene segments are effectively eliminated, and the evaluation and analysis efficiency is improved.
In addition to the above embodiments, the present invention may have other embodiments; all technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (6)
1. An automatic driving test scene analysis method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
establishing a database, wherein different scene categories are stored in the database, and for each scene category, corresponding historical scene characteristic data is extracted to be used as a scene data reference for initial comparison;
extracting frame-by-frame information and analyzing scene characteristic data of a scene to be analyzed, judging the similarity of the scene characteristic data of each frame and historical scene characteristic data of scene categories stored in a database, and selecting the scene category corresponding to the historical scene characteristic data with the highest similarity as the scene category of the frame;
screening continuous multi-frame data belonging to the same scene category in the whole time span of a scene to be analyzed as a target scene segment;
based on the screened target scene segment, a fuzzy analytic hierarchy process is adopted to convert linguistic descriptions of relative importance degree judgment of multiple scoring experts on different indexes or different classes into corresponding fuzzy numbers to construct a fuzzy judgment matrix, and defuzzification processing is carried out on the fuzzy judgment matrix to obtain index weight based on subjective factor analysis, then, an entropy weight method is adopted, the weight of each index based on data drive is obtained by calculating the information entropy of each index, multiplying and normalizing the weight of each index with the index weight obtained based on the fuzzy analytic hierarchy process, determining the weight evaluation of each index relative to the difficulty of the corresponding frame, calculating the deviation between the related index and the optimal scheme and the worst scheme by adopting a TOPSIS (technique for order preference by similarity to similarity) method, and calculating the difficulty of each frame of data by combining the weight evaluation of each index relative to the difficulty of the corresponding frame;
and screening continuous multi-frame data meeting the data difficulty requirement as a required scene segment.
2. The automated driving test scenario analysis method of claim 1, wherein: the scene characteristic data comprises surrounding traffic participants, road identification and traffic light state information.
3. The automated driving test scenario analysis method of claim 1, wherein: the n frame data which are continuous and belong to the same scene category are screened out in the whole time span of the scene to be analyzed as target scene segments,
setting a threshold range of similarity;
screening n frame data which are continuous and belong to the same scene category in the whole time span of the scene to be analyzed;
and judging whether the similarity obtained by correspondingly calculating each frame data is within a set similarity threshold range, and if the similarity is within the set similarity threshold range, taking the continuous n frame data as a target scene segment.
4. The automated driving test scenario analysis method of claim 1, wherein: the screened continuous multi-frame data meeting the data difficulty requirement is used as a required scene segment,
recording start and stop time points of continuous multi-frame data meeting the data difficulty requirement to obtain a target time period, and forward and backward extending time t on the basis of the target time period to capture data from the scene time span to be analyzed by taking the time t as a reference point to obtain a required scene segment.
5. The automated driving test scenario analysis method of claim 4, wherein: the time t is 1-7 s.
6. An automatic driving test scene analysis system is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the scene database layer is stored with a plurality of scene categories and historical scene characteristic data corresponding to each scene category;
the scene category judgment layer is used for extracting frame-by-frame information and analyzing scene characteristic data of a scene to be analyzed, carrying out similarity judgment on the scene characteristic data of each frame and historical scene characteristic data of scene categories stored in the database, selecting the scene category corresponding to the historical scene characteristic data with the highest similarity as the scene category of the frame, and screening out continuous multi-frame data belonging to the same scene category as a target scene segment;
a scene difficulty evaluation layer, which adopts a fuzzy analytic hierarchy process to convert linguistic descriptions of relative importance degree judgment of multiple scoring experts on different indexes or different classes into corresponding fuzzy numbers, constructs a fuzzy judgment matrix, and carries out defuzzification processing on the fuzzy judgment matrix to obtain index weights based on subjective factor analysis, then adopts an entropy weight method to calculate the information entropy of each index to obtain the weight of each index based on data driving, and multiplies and normalizes the weight of each index with the index weight obtained based on the fuzzy analytic hierarchy process to determine the weight evaluation of each index relative to the difficulty of the corresponding frame, then adopts a TOPSIS method to calculate the deviation between the related index and the optimal scheme and the worst scheme, and then combines the weight evaluation calculation of each index relative to the difficulty of the corresponding frame to obtain the difficulty of each frame data; and the number of the first and second groups,
and the scene intercepting layer is used for screening continuous multi-frame data meeting the data difficulty requirement and intercepting required scene fragments.
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