A vehicle road test condition development method

Abstract

The present invention relates to the technical field of vehicle road test conditions, and in particular to a vehicle road test condition development method, comprising: collecting first vehicle driving data; constructing a road test condition library based on the first driving data and in combination with a preset travel scenario; calculating the first eigenvalue of each road test condition in the road test condition library, and then obtaining the second eigenvalue of the travel scenario; calculating the first grey correlation between each first eigenvalue and the second eigenvalue, and then screening out the first road test condition whose first grey correlation meets the preset first standard from the road test condition library, and using the first road test condition as the vehicle road test condition development result. The road test condition library constructed by the present invention can reflect the actual driving behavior of the user in the corresponding travel scenario, and screen out the road test condition that can fully represent the actual driving behavior of the user in the corresponding travel scenario, so that the road test condition developed based on the method can accurately reflect the actual driving behavior of the user in a specific travel scenario.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle road test conditions, and in particular to a vehicle road test condition development method. Background Art

[0002] Actual road testing of vehicles is of great significance for evaluating vehicle performance and driving experience. It is also an effective way to evaluate core performance indicators such as vehicle durability, fuel consumption and driving behavior. The methods and test results of actual road testing of vehicles depend on road test conditions. Road test conditions are a series of standardized road test conditions set to evaluate and compare vehicle power, economy, fuel efficiency and emissions, including the selection of vehicle driving roads, vehicle travel time periods and driving areas, etc.

[0003] At present, automobile manufacturers mainly use two methods to develop road test conditions, including using the tester's free driving time and route as road test conditions, and counting the hot roads where car owners travel as road test conditions. These two methods do not combine the user's actual driving data with the actual road conditions for calculation and analysis. The road test conditions obtained cannot comprehensively reflect the user's driving behavior habits in specific driving scenarios. The vehicle road test results obtained are quite different from the user's actual driving conditions, and cannot effectively guide automobile manufacturers to analyze user driving behavior and improve vehicles. Summary of the invention

[0004] The purpose of the present invention is to provide a vehicle road test condition development method, so that the developed road test condition can accurately reflect the actual driving behavior of the user and improve the accuracy of the actual road test of the vehicle based on the road test condition.

[0005] In order to achieve the above object, the present invention provides a vehicle road test condition development method, comprising:

[0006] Collecting first driving data of the vehicle;

[0007] Building a road test condition library based on the first driving data and in combination with preset travel scenarios;

[0008] Calculating a first characteristic value of each road test condition in the road test condition library, and then obtaining a second characteristic value of the travel scenario;

[0009] Calculate the first grey correlation between each of the first eigenvalues ​​and the second eigenvalues, and then select the first road test condition whose first grey correlation meets the preset first standard from the road test condition library, and use the first road test condition as the vehicle road test condition development result.

[0010] The above-mentioned vehicle road test condition development method, for a vehicle travel scenario, combines the actual data of the first vehicle driving data to construct a road test condition library, so that the constructed road test condition library can reflect the actual driving behavior of the user in the corresponding travel scenario. And by comparing the characteristic values ​​of the road test condition and the travel scenario, the road test condition with a strong grey correlation with the corresponding travel scenario is obtained, so as to screen out the road test condition that can fully represent the actual driving behavior of the user in the corresponding travel scenario, and finally obtain the road test condition with a high correlation with the travel scenario and a strong representativeness of the user's driving behavior as the vehicle road test condition development result, so that the road test condition developed based on this method can accurately reflect the actual driving behavior of the user in a specific travel scenario, and improve the accuracy of the actual road test based on the road test condition.

[0011] In a possible implementation, the vehicle road test condition development method further includes:

[0012] Calculating a first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues, and then screening out a plurality of second road test conditions whose first grey correlation degrees meet a preset first standard;

[0013] Performing a road test based on any of the second road test conditions to obtain second driving data, and further obtaining a third characteristic value of the second road test condition based on the second driving data;

[0014] The second grey correlation degree between each of the third eigenvalues ​​and the second eigenvalue is calculated, and then the second road test condition whose second grey correlation degree meets the preset second standard is screened out, and the second road test condition is used as the vehicle road test condition development result.

[0015] In this implementation, for the obtained several second road test conditions with strong grey correlation with the corresponding travel scene, the actual road test effects of these road test conditions are further verified. Specifically, the actual road test is carried out using the second road test condition, and based on the second driving data obtained from the actual road test, the actual road test characteristic value of the second road test condition can be calculated, and the actual road test characteristic value is used as the third characteristic value, and the grey correlation between the third characteristic value and the characteristic value of the travel scene is calculated, which can further verify the representativeness of the second road test condition for the actual driving behavior of the user in the corresponding travel scene. Finally, among the several second road test conditions selected in the road test condition library, the second road test condition with the highest grey correlation between the actual road test characteristic value and the travel scene characteristic value is further selected as the road test condition result representing the travel scene, so that the road test condition developed based on this method can more accurately reflect the actual driving behavior of the user in a specific travel scene, and further improve the accuracy of the actual road test based on the road test condition.

[0016] In a possible implementation, the collecting the first vehicle driving data includes:

[0017] Establishing a test fleet, and continuously collecting initial driving data through the test fleet;

[0018] Calculating the chi-square test confidence of the initial driving data;

[0019] When the chi-square test confidence exceeds a preset confidence threshold, the test fleet stops collecting data and uses the initial driving data as the first driving data.

[0020] In this implementation, in order to collect stable vehicle driving data, it is necessary to perform a chi-square test on a large amount of vehicle driving data collected by the test fleet over a long period of time. The chi-square test is used to calculate the chi-square test confidence between the data of the second month and the data of the first month in the initial driving data. If the confidence of the chi-square test reaches more than 95%, it proves that the initial driving data has reached stability and can be used as the first driving data; if the confidence of the chi-square test does not reach the required confidence threshold, the test fleet needs to continue data collection, collect vehicle driving data for the third month, and calculate the chi-square test confidence between the data of the third month and the data of the first two months, until the chi-square test confidence between the newly collected vehicle driving data and the vehicle driving data collected in the historical months reaches more than 95%, and it is determined that the initial driving data has reached stability and can be used as the first driving data. This implementation method effectively improves the credibility of the first driving data, and thus can improve the credibility of a road test condition library constructed based on the first driving data and in combination with a preset travel scenario, thereby enhancing the representativeness of the road test condition library for the travel scenario, and improving the accuracy of subsequent screening of road test conditions based on the road test condition library.

[0021] In a possible implementation, building a road test condition library based on the first driving data and in combination with a preset travel scenario specifically includes:

[0022] According to the time when the vehicle is started and the time when the vehicle is turned off, the first driving data is divided into a plurality of travel chains, and one of the travel chains is used as one of the road test conditions;

[0023] All the road test conditions that meet the preset travel scenarios are screened, and then the road test condition library corresponding to the travel scenarios is constructed.

[0024] In this implementation, it is considered that the test fleet records the actual driving information of the vehicle in a continuous period of time during the process of collecting driving data, including the "driving" information of the vehicle when it is parked; and in the process of collecting driving data for several months, the test vehicle collects different original data of road test conditions every day, and these original data are recorded together in the driving data of the vehicle without being divided. Therefore, it is necessary to analyze the first driving data and divide it into several road test conditions. Specifically, this implementation divides the original data of the vehicle into several travel chains according to the time when the vehicle is started and the vehicle is turned off. Each travel chain represents the driving route of the vehicle after one start driving until the next long-term parking. Taking the travel chain as the road test condition, the vehicle driving data is divided into several road test conditions in a concise and effective manner, avoiding the invalid parking data from being mistakenly recorded as the road test condition, and avoiding the division of multiple travel routes into one road test condition, thereby improving the accuracy of the road test condition library subsequently constructed according to the obtained road test conditions.

[0025] Furthermore, this implementation method requires a specific travel scenario of the road test condition, and all road test conditions that meet the travel scenario conditions, so that the constructed road test condition library can reflect the user's actual driving behavior in the corresponding travel scenario.

[0026] In a possible implementation, before calculating the first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues, and then screening out the first road test condition whose first grey correlation degree meets the preset first standard from the road test condition library, and using the first road test condition as the vehicle road test condition development result, the method further includes:

[0027] Collect road network data;

[0028] The road test condition library is updated based on the road network data.

[0029] In this implementation method, taking into account the differences in traffic conditions on different roads in actual urban traffic data, road test conditions involving roads with unstable traffic conditions and frequent abnormal data or special data are removed from the road test condition library, thereby updating the road test condition library so that all road test condition data in the road test condition library have the stability required for accurate calculation, ensuring the credibility of the first eigenvalue calculated for each road test condition in the road test condition library, and improving the accuracy of the subsequent calculation of the grey correlation between the first eigenvalue of the road test condition and the second eigenvalue of the travel scenario.

[0030] In a possible implementation, updating the road test condition library based on the road network data includes:

[0031] Based on the road network data, a number of roads included in each road test condition are obtained, and then an average speed of each road is obtained;

[0032] For any of the roads, based on the average speed of the road and the road test condition library, obtain the speed fluctuation of the road;

[0033] Selecting the road whose speed fluctuation is greater than a preset fluctuation threshold as a fluctuating road;

[0034] The road test conditions corresponding to the fluctuating road are removed from the road test conditions library.

[0035] In this implementation, the first driving data reflecting the actual driving trajectory of the vehicle is matched with the road network data reflecting the actual layout of the urban traffic roads, and the driving route of the road test condition is matched with the several road network roads it passes through, so that the average speed of the vehicle on each road can be calculated based on the driving data in all road test conditions. Further, this implementation calculates the speed fluctuation of a road through the total average speed of a road and the average speed of each road test condition passing through the road when driving on the road, and then quantitatively evaluates the data fluctuation of the road, so as to objectively evaluate whether a road involves unstable traffic conditions and often has abnormal data or special data. Finally, the road test conditions involving fluctuating roads are eliminated from the road test condition library, so that all road test condition data in the road test condition library can have the stability required for accurate calculation, ensuring the credibility of the first eigenvalue calculated for each road test condition in the road test condition library, and improving the accuracy of the subsequent calculation of the gray correlation between the first eigenvalue of the road test condition and the second eigenvalue of the travel scene.

[0036] In a possible implementation, obtaining a plurality of roads included in each road test condition based on the road network road data includes:

[0037] For any of the road test conditions, calculating a matching weight between each of the roads in the road network data and the road test condition;

[0038] The road whose matching weight meets the preset matching standard is used as a matching road;

[0039] All the matching roads are taken as the several roads included in the road test condition.

[0040] In this implementation, considering that the first driving data collected by the test vehicle is raw data, it truly reflects the possible lane changing and overtaking behaviors during the vehicle's driving process, so that the vehicle's driving trajectory recorded in the first driving data is not completely equal to the actual layout of urban traffic roads. In order to make the road test condition correspond to the actual layout of urban traffic roads, and make the road test condition match all the roads it passes through during its corresponding driving process, by calculating the matching weights of the road test condition and all roads, objectively evaluate all possible roads that the road test condition passes through, and finally determine all roads with sufficiently high matching weights with the road test condition as the roads that the road test condition passes through, eliminating subjective factors in the data processing process, improving the accuracy of the road test condition matching the road network, and thus improving the accuracy of the subsequent elimination of the road test condition corresponding to the fluctuating road from the road test condition library and completing the update of the road test condition library.

[0041] In a possible implementation, obtaining the average speed of each road includes:

[0042] For any of the roads, respectively calculating the first speed average value corresponding to the road in each road test condition;

[0043] The average speed of the road is calculated based on all the first speed averages.

[0044] In this implementation, the average speed of vehicles traveling on each road is calculated based on the driving data in all road test conditions. Specifically, as an objective existence in the urban traffic road network, there are situations where several road test conditions pass through the same road. For different road test conditions, the average speeds when passing through the same road are obviously different. For a road, this implementation first calculates the average speed of each road test condition when passing through the road as the first speed average, and further calculates the average of all first speed averages as the average speed of the road. It should be understood that the average speed of the above-mentioned road is the average speed of the road corresponding to the above-mentioned travel scenario.

[0045] In a possible implementation, any of the above vehicle road test condition development methods further includes:

[0046] Preset multiple travel scenarios;

[0047] For any of the travel scenarios, the road test condition library is constructed, and then a single-scenario road test condition development result corresponding to the travel scenario is obtained;

[0048] Based on all the single-scenario road test condition development results, multi-scenario road test condition development results corresponding to the multiple travel scenarios are obtained.

[0049] In this implementation, the road test conditions obtained from the first driving data are classified according to a variety of travel scenarios, and a plurality of road test condition libraries corresponding to different scenarios are constructed, so that the present invention can develop road test conditions representing corresponding scenarios for a variety of scenarios respectively, so that the present invention is not limited to seeking road test conditions for one travel scenario. The multiple road test conditions developed based on this implementation can accurately reflect the actual driving behavior of the user in a variety of travel scenarios, improve the representativeness of each road test condition for its corresponding travel scenario, and the scope of application of actual road tests based on multiple road test conditions.

[0050] In a possible implementation, the preset multiple travel scenarios include:

[0051] Constructing the travel scenario based on any preset time period and any preset driving area;

[0052] The time periods include the morning peak period, the daytime off-peak period, the evening peak period and the night off-peak period;

[0053] The driving area includes an urban area, a suburban area and a high-speed area.

[0054] In this implementation, a variety of travel scenarios with different time periods and driving areas are constructed, so that the road test conditions obtained from the first driving data are classified according to the time periods and driving areas, and multiple road test condition libraries corresponding to different time periods and different driving areas are constructed, so that the present invention can develop road test conditions representing corresponding scenarios for scenarios in different time periods and different driving areas.

[0055] A vehicle road test condition development method provided by the present invention has at least the following advantages compared with the prior art:

[0056] First, the above-mentioned vehicle road test condition development method, for a vehicle travel scenario, combines the actual data of the first vehicle driving data to construct a road test condition library, so that the constructed road test condition library can reflect the actual driving behavior of the user in the corresponding travel scenario. And by comparing the characteristic values ​​of the road test condition and the travel scenario, the road test condition with a strong grey correlation with the corresponding travel scenario is obtained, so as to screen out the road test condition that can fully represent the actual driving behavior of the user in the corresponding travel scenario, and finally obtain the road test condition with a high correlation with the travel scenario and a strong representativeness of the user's driving behavior as the vehicle road test condition development result, so that the road test condition developed based on this method can accurately reflect the actual driving behavior of the user in a specific travel scenario, and improve the accuracy of the actual road test based on the road test condition.

[0057] Secondly, the above-mentioned vehicle road test condition development method can construct a variety of travel scenarios with different time periods and driving areas, so as to classify the road test conditions obtained from the first driving data according to the time period and the driving area, and construct multiple road test condition libraries corresponding to different time periods and different driving areas, so that the present invention can develop road test conditions representing corresponding scenarios for different time periods and different driving areas respectively, so that the present invention is not limited to seeking road test conditions for one travel scenario. The multiple road test conditions developed based on this implementation method can accurately reflect the user's actual driving behavior in a variety of travel scenarios, improve the representativeness of each road test condition for its corresponding travel scenario, and the scope of application of actual road tests based on multiple road test conditions.

[0058] Finally, the above-mentioned vehicle road test condition development method, taking into account the differences in traffic conditions on different roads in actual urban traffic data, removes road test conditions involving roads with unstable traffic conditions and frequent abnormal data or special data from the road test condition library, thereby updating the road test condition library so that all road test condition data in the road test condition library have the stability required for accurate calculation, ensuring the credibility of the first eigenvalue calculated for each road test condition in the road test condition library, and improving the accuracy of the subsequent calculation of the grey correlation between the first eigenvalue of the road test condition and the second eigenvalue of the travel scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of a vehicle road test condition development method provided by an embodiment of the present invention;

[0060] Figure 2 is a flow chart of another vehicle road test condition development method provided by an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of a travel chain division provided by an embodiment of the present invention;

[0062] Figure 4 It is a schematic diagram of the development results of a road test condition in an urban area scenario during a morning rush hour provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0064] The following detailed descriptions are all exemplary descriptions, and are intended to provide further detailed descriptions of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments, and are not intended to limit the present application; the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0065] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0066] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] See also Figure 1 An embodiment of the present invention provides a vehicle road test condition development method, comprising:

[0068] S101, collecting first vehicle driving data;

[0069] S102, constructing a road test condition library based on the first driving data and in combination with a preset travel scenario;

[0070] S103, calculating a first characteristic value of each road test condition in the road test condition library, and then obtaining a second characteristic value of the travel scenario;

[0071] S104, calculating a first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues, and then screening out a first road test condition whose first grey correlation degree meets a preset first standard from the road test condition library, and using the first road test condition as a vehicle road test condition development result.

[0072] An embodiment of the present invention provides another vehicle road test condition development method, comprising:

[0073] Collect the first driving data of the vehicle, which includes driving time, driving speed, driving accuracy and driving latitude. Divide the vehicle into travel chains based on the first driving data, and regard the first driving data between the time node of "vehicle start" and the time period of "vehicle parking and shutdown for more than 30 minutes" as a travel chain. Treat a travel chain as a road test condition.

[0074] Based on all the road test conditions obtained from the first driving data, screening the road test conditions that meet the preset travel scenario conditions, and taking all the road test conditions that meet the travel scenario conditions as a road test condition library;

[0075] The first characteristic value of each road test condition in the road test condition library is calculated, and the first characteristic value specifically includes the average speed characteristic value of the road test condition, the average acceleration characteristic value of the road test condition, and the average deceleration characteristic value of the road test condition. Based on the first characteristic values ​​of all road test conditions in the road test condition library, the second characteristic value of the corresponding travel scene is comprehensively calculated, and the second characteristic value specifically includes the average speed characteristic value of the travel scene, the average acceleration characteristic value of the travel scene, and the average deceleration characteristic value of the travel scene.

[0076] The first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues ​​is calculated. The first eigenvalue of the road test condition can be expressed as Formula 1:

[0077] X i = (x i1 ,x i2 ,x i3 ) (Formula 1)

[0078] Among them, X i is the comparison series, representing the first eigenvalue of the i-th road test condition; x i1 represents the first characteristic value of the i-th road test condition, which may be the average speed of the road test condition; x i2 represents the second first characteristic value of the i-th road test condition, which may be the average acceleration of the road test condition; x i3 The third first eigenvalue representing the i-th road test condition may be the average deceleration of the road test condition.

[0079] The second eigenvalue of the travel scenario can be expressed as formula 2:

[0080] X0=(x 01 ,x 02 ,x 03 ) (Formula 2)

[0081] Among them, X0 is the reference series, representing the second eigenvalue of the travel scenario; x 01 The first and second eigenvalues ​​of the travel scene can be the average speed of the travel scene; x 02 The second eigenvalue representing the travel scenario can be the average acceleration of the travel scenario; 03 The third second eigenvalue representing the travel scenario may be the average deceleration of the travel scenario.

[0082] The eigenvalues ​​in the comparison series and the reference series are dimensionless, as shown in Formula 3:

[0083] x′ i =x i / x0 (Formula 3)

[0084] The first grey relational degree calculation method can be expressed as formula 4 and formula 5:

[0085]

[0086]

[0087] Among them, ik is the correlation coefficient corresponding to the kth first eigenvalue of the i-th road test condition, x 0k ′ is the kth second eigenvalue of the travel scenario in the dimensionless reference series, x ik ′ is the kth characteristic parameter of the ith road test condition in the dimensionless comparison series, and ρ is the resolution coefficient, which is generally taken as 0.5.

[0088] Among them, r i is the first grey relational degree of the i-th road test condition, and n is the number of the first eigenvalues ​​of the road test condition.

[0089] A first road test condition whose first grey correlation degree meets a preset first standard is screened out from the road test condition library, and the first road test condition is used as a vehicle road test condition development result.

[0090] In a possible embodiment, the vehicle road test condition development method further includes:

[0091] Calculating a first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues, and then screening out a plurality of second road test conditions whose first grey correlation degrees meet a preset first standard;

[0092] Performing a road test based on any of the second road test conditions to obtain second driving data, and further obtaining a third characteristic value of the second road test condition based on the second driving data;

[0093] The second grey correlation degree between each of the third eigenvalues ​​and the second eigenvalue is calculated, and then the second road test condition whose second grey correlation degree meets the preset second standard is screened out, and the second road test condition is used as the vehicle road test condition development result.

[0094] In a possible embodiment, collecting the first vehicle driving data includes:

[0095] A test fleet is formed, and initial driving data is continuously collected through the test fleet. The data parameters include driving time, driving speed, driving longitude and driving latitude. When the initial driving data is stable, data collection is stopped and the initial driving data is used as the first driving data.

[0096] The method for judging the stability of initial driving data is as follows:

[0097] At least two months of initial vehicle driving data are collected, and the vehicle speed-acceleration distribution of each month is calculated based on the driving speed in the collected initial driving data. Then, based on the vehicle speed-acceleration distribution of each month, the chi-square test is used to calculate the chi-square test confidence of the vehicle speed-acceleration distribution data between the second month and the first month. If the confidence of the chi-square test reaches more than 95%, data collection can be stopped, and the initial driving data is used as the first driving data. If the confidence of the chi-square test does not reach 95%, it is necessary to continue to collect initial driving data, that is, collect data for the third month, and perform a chi-square test on the newly collected data, that is, the speed-acceleration distribution data of the driving data of the third month, and the speed-acceleration distribution data of the data collected in the previous two months, until the confidence of the chi-square test between the latest month's data and the historical month's data reaches more than 95%.

[0098] In a possible embodiment, the step of building a road test condition library based on the first driving data and in combination with a preset travel scenario specifically includes:

[0099] According to the time when the vehicle is started and the time when the vehicle is turned off, the first driving data is divided into a plurality of travel chains, and one of the travel chains is used as one of the road test conditions;

[0100] All the road test conditions that meet the preset travel scenarios are screened, and then the road test condition library corresponding to the travel scenarios is constructed.

[0101] In a possible embodiment, before calculating the first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues, and then screening out the first road test condition whose first grey correlation degree meets the preset first standard from the road test condition library, and using the first road test condition as the vehicle road test condition development result, the method further includes:

[0102] Collect road network data;

[0103] The road test condition library is updated based on the road network data.

[0104] In a possible embodiment, updating the road test condition library based on the road network data includes:

[0105] Based on the road network data, a plurality of roads included in each of the road test conditions are obtained;

[0106] For any of the roads, the first speed average value corresponding to the road in each road test condition is calculated respectively; and the average speed of the road is calculated based on all the first speed average values.

[0107] For any of the roads, based on the average speed of the road and the road test condition library, the speed fluctuation of the road is obtained. The calculation process of the speed fluctuation is shown in Formula 6:

[0108]

[0109] Among them, α is the velocity fluctuation, σ v is the standard deviation of road speed, is the average road speed, v i is the average first speed of the road in the i-th road test condition, and n is the number of road test conditions corresponding to the travel scenario.

[0110] Filter the roads with speed fluctuation greater than or equal to 0.1 as fluctuating roads; remove the road test conditions corresponding to the fluctuating roads from the road test condition library; or, filter the roads with speed fluctuation less than 0.1 as non-fluctuating roads, and then filter all the included roads as road test conditions of non-fluctuating roads, and remove the remaining road test conditions from the road test condition library.

[0111] In a possible embodiment, the obtaining, based on the road network road data, a plurality of roads included in each of the road test conditions includes:

[0112] For any of the road test conditions, a multi-feature matching method is used to match the vehicle driving accuracy and driving latitude in the road test condition with the longitude and latitude of the urban road network in the road network data. In view of the problem of dense road network and longitude and latitude drift errors, the principle of comprehensive similarity is adopted to comprehensively consider the road distance and direction to calculate the matching weight of the road network relative to the road test condition, thereby reducing the error. The specific weight calculation formula can be expressed by the following formula 7:

[0113]

[0114] Among them, X a is the longitude of the next time point at the current moment, X b is the longitude at the previous time point, Y a is the latitude of the next time point at the current moment, Y b is the latitude at the last time point, l is the distance between the projection point and the coordinate point under the earth ellipse model, σ is the standard deviation of the vehicle's longitude and latitude drift error, and w 方向 is the road direction weight, w 道路距离 is the road distance weight, and w is the road weight.

[0115] w is the road whose road weight meets the preset matching standard as the matching road;

[0116] All the matching roads are taken as the several roads included in the road test condition.

[0117] In a possible embodiment, any of the above vehicle road test condition development methods further includes:

[0118] Preset multiple travel scenarios;

[0119] For any of the travel scenarios, the road test condition library is constructed, and then a single-scenario road test condition development result corresponding to the travel scenario is obtained;

[0120] Based on all the single-scenario road test condition development results, multi-scenario road test condition development results corresponding to the multiple travel scenarios are obtained.

[0121] In a possible embodiment, the preset multiple travel scenarios include:

[0122] Constructing the travel scenario based on any preset time period and any preset driving area;

[0123] The time periods include the morning peak period, the daytime off-peak period, the evening peak period and the night off-peak period;

[0124] The driving area includes an urban area, a suburban area and a high-speed area.

[0125] See also Figure 2The present application embodiment provides a vehicle road test condition development method, comprising:

[0126] S201, vehicle actual driving data collection:

[0127] Set up a test fleet and collect actual vehicle driving data, including driving time, driving speed, driving accuracy, and driving latitude. Stop data collection when the actual driving data is stable.

[0128] The method for judging the stability of actual driving data is as follows: collect at least two months of actual vehicle driving data, calculate the vehicle speed-acceleration distribution for each month based on the driving speed in the collected actual driving data, and then use the chi-square test to calculate the chi-square test confidence of the vehicle speed-acceleration distribution data between the second month and the first month based on the vehicle speed-acceleration distribution for each month. If the confidence of the chi-square test reaches more than 95%, data collection can be stopped. If the confidence of the chi-square test does not reach 95%, it is necessary to continue to collect actual driving data, that is, collect data for the third month, and perform a chi-square test on the newly collected data, that is, the speed-acceleration distribution data of the driving data for the third month, and the speed-acceleration distribution data of the data collected for the previous two months, until the confidence of the chi-square test between the data of the latest month and the data of the historical months reaches more than 95%.

[0129] In this embodiment, the number of vehicles in the test fleet is 50, and when the data is collected for the eighth month, the actual driving data is stable.

[0130] S202, travel chain division:

[0131] The vehicle travel chain is divided according to the actual driving data, and the actual driving data between the time node of "vehicle start" and the time period of "vehicle parking and shutdown for more than 30 minutes" is regarded as a travel chain. A travel chain is regarded as a road test condition.

[0132] See also Figure 3 , is a schematic diagram of a travel chain division provided by an embodiment of the present invention. The "time interval > 30min" shown in the figure means the time period of "the vehicle is parked and turned off for more than 30 minutes". As shown in the figure, this embodiment divides the actual driving data into travel chain 1 and travel chain 2 according to the time node of "vehicle start" and the time period of "vehicle is parked and turned off for more than 30 minutes".

[0133] S203, road test condition library construction:

[0134] Construct a variety of travel scenarios based on time periods and driving areas. The time periods include morning peak hours, daytime off-peak hours, evening peak hours, and nighttime off-peak hours; the driving areas include urban areas, suburban areas, and highway areas. Classify all road test conditions obtained in S202 according to time periods and driving areas, and assign each road test condition to a travel scenario. If a road test condition involves multiple time periods or driving areas, assign the road test condition to the corresponding travel scenario with the highest time period ratio. Finally, each travel scenario is assigned a number of corresponding road test conditions. For any travel scenario, a road test condition library is constructed based on all corresponding road test conditions.

[0135] S204, calculation of travel scenario characteristic values:

[0136] For any travel scenario, the vehicle driving characteristic values ​​of each road test condition in the corresponding road test condition library are calculated. The vehicle driving characteristic values ​​specifically include the average speed of the road test condition, the average acceleration of the road test condition, and the average deceleration of the road test condition. Based on the vehicle driving characteristic values ​​of all road test conditions in the road test condition library, the vehicle driving characteristic values ​​of the travel scenario are comprehensively calculated.

[0137] In this embodiment, the urban area in Beijing during the morning rush hour is used as a travel scenario, and the corresponding vehicle driving characteristic values ​​are calculated. The results are shown in Table 1 below.

[0138] Table 1 Characteristic values ​​of urban areas during morning rush hour in Beijing

[0139]

[0140] S205. Road network matching for road test conditions:

[0141] For any travel scenario, based on any of the road test conditions in the travel scenario, a multi-feature matching method is used to match the vehicle driving accuracy and driving latitude in the road test condition with the longitude and latitude of the urban road network in the road network data. In view of the problem of dense road network and longitude and latitude drift errors, the principle of comprehensive similarity is adopted to comprehensively consider the road distance and direction to calculate the matching weight of the road network relative to the road test condition, thereby reducing the error. The specific weight calculation formula can be expressed by the following formula 7:

[0142]

[0143] Among them, X a is the longitude of the next time point at the current moment, X b is the longitude at the previous time point, Y a is the latitude of the next time point at the current moment, Y bis the latitude at the last time point, l is the distance between the projection point and the coordinate point under the earth ellipse model, σ is the standard deviation of the vehicle's longitude and latitude drift error, and w 方向 is the road direction weight, w 道路距离 is the road distance weight, and w is the road weight.

[0144] w is the road whose road weight meets the preset matching standard as the matching road;

[0145] All the matching roads are taken as the several roads included in the road test condition.

[0146] S206. Calculation of characteristic values ​​of road network:

[0147] For any travel scenario, based on any road in the travel scenario, the first speed average value corresponding to the road in each road test condition in the travel scenario is calculated respectively; based on all the first speed average values, the average speed of the road is calculated.

[0148] S207. Calculation of road network speed fluctuation:

[0149] For any travel scenario, based on any road in the travel scenario, based on the average speed of the road and the road test condition library of the travel scenario, the speed fluctuation of the road is obtained. The calculation process of the speed fluctuation is shown in Formula 6:

[0150]

[0151] Among them, α is the velocity fluctuation, σ v is the standard deviation of road speed, is the average road speed, v i is the first average speed of the road in the i-th road test condition of the travel scenario, and n is the number of road test conditions of the travel scenario.

[0152] S208, road test condition stability screening:

[0153] For any travel scenario, the roads with speed fluctuation greater than or equal to 0.1 are screened as fluctuating roads; the road test conditions corresponding to the fluctuating roads are eliminated from the road test condition library; or, the roads with speed fluctuation less than 0.1 are screened as non-fluctuating roads, and then all the roads included are screened as road test conditions of non-fluctuating roads, and the remaining road test conditions are eliminated from the road test condition library.

[0154] S209, Representative screening of road test conditions:

[0155] For any travel scenario, the grey correlation between the characteristic value of each road test condition and the characteristic value of the travel scenario is calculated. The characteristic value of the road test condition can be expressed as formula 1:

[0156] X i =(x i1 ,x i2 ,x i3 ) (Formula 1)

[0157] Among them, X i is a comparison series, representing the characteristic value of the i-th road test condition in this travel scenario; x i1 represents the first characteristic value of the i-th road test condition, which can be the average speed of the road test condition; x i2 represents the second characteristic value of the i-th road test condition, which can be the average acceleration of the road test condition; x i3 The third eigenvalue representing the i-th road test condition may be the average deceleration of the road test condition.

[0158] The characteristic value of the travel scenario can be expressed as formula 2:

[0159] X0=(x 01 ,x 02 ,x 03 ) (Formula 2)

[0160] Among them, X0 is the reference series, representing the characteristic value of the travel scene; x 01 The first characteristic value representing the travel scenario can be the average speed of the travel scenario; x 02 The second eigenvalue representing the travel scenario can be the average acceleration of the travel scenario; x 03 The third eigenvalue representing the travel scenario may be the average deceleration of the travel scenario.

[0161] After dimensionless processing, the eigenvalues ​​x′ i As shown in formula 3:

[0162] x′ i =x i / x0 (Formula 3)

[0163] The calculation method of grey relational degree can be expressed as formula 4 and formula 5:

[0164]

[0165]

[0166] Among them, ik is the grey correlation coefficient corresponding to the kth eigenvalue of the ith road test condition in the travel scenario, x 0k′ is the kth eigenvalue of the travel scenario in the dimensionless reference series, x ik ′ is the kth characteristic parameter of the ith road test condition in the dimensionless comparison series, and ρ is the resolution coefficient, which is generally taken as 0.5.

[0167] Among them, r i is the grey correlation degree of the i-th road test condition in the travel scenario, and n is the number of eigenvalues ​​of the road test condition.

[0168] According to the grey correlation calculation results, the five road test conditions with the largest grey correlation in this travel scenario are screened out.

[0169] S210, road test condition output:

[0170] For any travel scenario, actual road tests are performed based on the five road test conditions with the largest grey correlation to obtain corresponding actual driving data, and then the measured characteristic values ​​of each road test condition are obtained.

[0171] The grey correlation between the measured characteristic values ​​of the five road test conditions and the characteristic values ​​of the travel scenario is calculated respectively, and then the road test condition with the largest grey correlation is selected and used as the vehicle road test condition development result.

[0172] In this embodiment, for the travel scenarios in the urban area during the morning rush hour in Beijing, the five road test conditions with the largest grey correlation are screened out, and the grey correlation between the measured characteristic values ​​and the travel scenarios is further calculated. The calculation results are shown in Table 2 below:

[0173] Table 2 Grey correlation degree of road test conditions

[0174]

[0175] According to Table 2, the measured characteristic value of the road test condition No. 5 has the highest grey correlation with the travel scenario, so the road test condition with a departure time of 7:20 AM (within half an hour before and after), a starting point of Huilongguan, Beijing, and an end point of Beijing West Railway Station is finally selected as the road test condition for the urban area scenario during the morning rush hour in Beijing. The test route map is as follows Figure 4 shown.

[0176] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0177] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope recorded in this specification.

[0178] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several improvements and substitutions may be made without departing from the concept of the present application, and these improvements and substitutions shall also be regarded as the protection scope of the present invention. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle road test condition development method, characterized in that: include: Collecting first driving data of the vehicle; Building a road test condition library based on the first driving data and in combination with preset travel scenarios; Calculating a first characteristic value of each road test condition in the road test condition library, and then obtaining a second characteristic value of the travel scenario; Calculate the first grey correlation between each of the first eigenvalues ​​and the second eigenvalues, and then select the first road test condition whose first grey correlation meets the preset first standard from the road test condition library, and use the first road test condition as the vehicle road test condition development result.

2. A vehicle road test condition development method according to claim 1, characterized in that: Also includes: Calculating a first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues, and then screening out a plurality of second road test conditions whose first grey correlation degrees meet a preset first standard; Performing a road test based on any of the second road test conditions to obtain second driving data, and further obtaining a third characteristic value of the second road test condition based on the second driving data; The second grey correlation degree between each of the third eigenvalues ​​and the second eigenvalue is calculated, and then the second road test condition whose second grey correlation degree meets the preset second standard is screened out, and the second road test condition is used as the vehicle road test condition development result.

3. A vehicle road test condition development method according to claim 1, characterized in that: The collecting of first vehicle driving data includes: Establishing a test fleet, and continuously collecting initial driving data through the test fleet; Calculating the chi-square test confidence of the initial driving data; When the chi-square test confidence exceeds a preset confidence threshold, the test fleet stops collecting data and uses the initial driving data as the first driving data.

4. A vehicle road test condition development method according to claim 1, characterized in that: The step of constructing a road test condition library based on the first driving data and in combination with a preset travel scenario specifically includes: According to the time when the vehicle is started and the time when the vehicle is turned off, the first driving data is divided into a plurality of travel chains, and one of the travel chains is used as one of the road test conditions; All the road test conditions that meet the preset travel scenarios are screened, and then the road test condition library corresponding to the travel scenarios is constructed.

5. A vehicle road test condition development method according to claim 1, characterized in that: Before calculating the first grey correlation degree between each of the first eigenvalues ​​and the second eigenvalues, and then selecting the first road test condition whose first grey correlation degree meets the preset first standard from the road test condition library, and using the first road test condition as the vehicle road test condition development result, the method further includes: Collect road network data; The road test condition library is updated based on the road network data.

6. A vehicle road test condition development method according to claim 5, characterized in that: The updating of the road test condition library based on the road network data comprises: Based on the road network data, a number of roads included in each road test condition are obtained, and then an average speed of each road is obtained; For any of the roads, based on the average speed of the road and the road test condition library, obtain the speed fluctuation of the road; Selecting the road whose speed fluctuation is greater than a preset fluctuation threshold as a fluctuating road; The road test conditions corresponding to the fluctuating road are removed from the road test conditions library.

7. A vehicle road test condition development method according to claim 6, characterized in that: Based on the road network data, a plurality of roads included in each road test condition are obtained, including: For any of the road test conditions, calculating a matching weight between each of the roads in the road network data and the road test condition; The road whose matching weight meets the preset matching standard is used as a matching road; All the matching roads are taken as the several roads included in the road test condition.

8. A vehicle road test condition development method according to claim 6, characterized in that: The average speed of each road is obtained, including: For any of the roads, respectively calculating the first speed average value corresponding to the road in each road test condition; The average speed of the road is calculated based on all the first speed averages.

9. A vehicle road test condition development method according to any one of claims 1 to 8, characterized in that: Also includes: Preset multiple travel scenarios; For any of the travel scenarios, the road test condition library is constructed, and then a single-scenario road test condition development result corresponding to the travel scenario is obtained; Based on all the single-scenario road test condition development results, multi-scenario road test condition development results corresponding to the multiple travel scenarios are obtained.

10. A vehicle road test condition development method according to claim 9, characterized in that: The preset multiple travel scenarios include: Constructing the travel scenario based on any preset time period and any preset driving area; The time periods include the morning peak period, the daytime off-peak period, the evening peak period and the night off-peak period; The driving area includes an urban area, a suburban area and a high-speed area.

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