CN114753199A - Open road grading method and device based on intelligent network connection automobile test - Google Patents
Open road grading method and device based on intelligent network connection automobile test Download PDFInfo
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- CN114753199A CN114753199A CN202210265438.7A CN202210265438A CN114753199A CN 114753199 A CN114753199 A CN 114753199A CN 202210265438 A CN202210265438 A CN 202210265438A CN 114753199 A CN114753199 A CN 114753199A
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- 238000012360 testing method Methods 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000006855 networking Effects 0.000 claims abstract description 65
- 230000007613 environmental effect Effects 0.000 claims abstract description 16
- 238000011156 evaluation Methods 0.000 claims description 43
- 230000000694 effects Effects 0.000 claims description 5
- 238000000926 separation method Methods 0.000 claims description 4
- 206010039203 Road traffic accident Diseases 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 238000005286 illumination Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
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- 238000011835 investigation Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
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Classifications
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C1/00—Design or layout of roads, e.g. for noise abatement, for gas absorption
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention discloses an open road grading method and device based on intelligent networking automobile testing. Wherein, the method comprises the following steps: determining the intelligent level of the current road section according to the road complexity, the traffic complexity and the environmental influence degree of the current road section; determining the networking grade of the current road section according to the digitization degree of the infrastructure of the current road section; and determining the intelligent networking grade of the current road section according to the intelligent grade and the networking grade. The invention provides a relatively objective, reproducible, generalized, complete and unified open road test grading method by respectively carrying out intelligent and networking grading on open roads, and can scientifically and quantitatively guide the test of open road grading, so that the differentiated test requirements are further provided according to the intelligent and networking grades of the roads and the technical level of intelligent networking automobiles, and the test requirements of intelligent networking automobiles and the traffic safety of the open roads can be better coordinated.
Description
Technical Field
The embodiment of the invention relates to the technical field of open road grading, in particular to an open road grading method and device based on intelligent networking automobile testing.
Background
Under the push of national policy and industrial enterprises, the development of the key technology of the intelligent networked automobile achieves great results, and the intelligent networked automobile starts to enter the stage of open road testing. After a test road is opened, a plurality of enterprises are often attracted to carry out automatic driving technology test in the front, and the test road matched with the automatic driving capability of the intelligent networked automobile is opened differently according to the technical level of the intelligent networked automobile in order to ensure the traffic safety and the test efficiency of the test process as far as possible while meeting the test requirement of the intelligent networked automobile, so that a road grading method corresponding to the test difficulty is necessary to be formed.
And the local standard specifications are provided for different regions aiming at the open road test classification method. The definition of some cities on road grading and traffic complexity is mainly qualitative description, the method for grading the road environment of other cities depends on the researched road network data set, and when the cities implement open road test work, the cities have respective thinking, so that the method for grading the open road test of the city grade has small and large differences, and the differences bring certain influence on technical iteration and industrial development and are not convenient for popularization and reproduction of other cities.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an open road grading method and device based on intelligent networking automobile testing, so as to realize a complete and unified open road testing grading method which is relatively objective, reproducible and popularized.
In a first aspect, an embodiment of the present invention provides an open road classification method based on an intelligent internet automobile test, including:
s110, determining the intelligent level of the current road section according to the road complexity, the traffic complexity and the environmental influence degree of the current road section;
s120, determining the networking grade of the current road section according to the infrastructure digitization degree of the current road section;
and S130, determining the intelligent networking grade of the current road section according to the intelligent grade and the networking grade.
Optionally, the road complexity evaluation elements include road network topology, road type, road geometry, road surface, intersection condition, separation mode, road surface identification, traffic sign, and road facility elements.
Optionally, the traffic flow complexity evaluation elements include traffic density, population activity along the route, traffic safety risk, and traffic composition.
Optionally, the environmental impact evaluation element includes weather, light, view conditions, and connectivity.
Optionally, S110 specifically includes:
determining the variation coefficient of each evaluation element by adopting a variation coefficient method according to the evaluation elements of road complexity, traffic flow complexity and environmental influence degree and evaluation indexes contained in the evaluation elements;
determining the weight coefficient of each evaluation element according to the variation coefficient;
determining the total score of the current road section according to the weight coefficient and average value of each evaluation element;
and determining the intelligent level of the current road according to the total score.
Optionally, the number of the intelligent levels is the same as that of the networking levels, and accordingly, S130 includes:
and comparing the intelligent level and the networking level of the current road section, and taking the lowest level as the intelligent networking level of the current road section.
In a second aspect, an open road grading device based on intelligent networking automobile test comprises:
the intelligent level determining module is used for determining the intelligent level of the current road section according to the road complexity, the traffic complexity and the environmental influence degree of the current road section;
the networking grade determining module is used for determining the networking grade of the current road section according to the digital degree of the infrastructure of the current road section;
and the intelligent networking grade determining module is used for determining the intelligent networking grade of the current road section according to the intelligent grade and the networking grade.
According to the technical scheme of the embodiment of the invention, the grading evaluation system of the open road for testing the intelligent networked automobile is constructed, so that the grading of the open road for testing can be scientifically and quantitatively guided, the differentiated testing requirements can be further provided according to the intelligent level of the road, the networking level and the technical level of the intelligent networked automobile, and the testing requirements of the intelligent networked automobile and the traffic safety of the open road can be better coordinated in the testing process.
Drawings
Fig. 1 is a flowchart of an open road classification method based on an intelligent internet automobile test according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some structures related to the present invention are shown in the drawings, not all of them.
Fig. 1 is a flowchart of an open road classification method based on an intelligent internet automobile test according to an embodiment of the present invention, which specifically includes the following steps:
and S110, determining the intelligent level of the current road section according to the road complexity, the traffic complexity and the environmental influence degree of the current road section.
The road complexity evaluation elements include, but are not limited to, road information elements such as road network topology, road types, road geometry, road surfaces, intersection conditions, separation modes, road surface identifiers, traffic signs, road facility elements, and the like, and the road information elements include, but are not limited to, the following sub-elements, as shown in table 1:
TABLE 1 road information element and sub-elements thereof
The traffic flow complexity evaluation elements comprise but are not limited to information such as traffic density, along-line population activity, traffic safety risk degree, traffic composition condition and the like, wherein:
a) the traffic density refers to an actual traffic flow (v/min: vehicle/minute)/road design maximum traffic capacity (v/min) reflects the traffic operating conditions of the road, and a higher density indicates a more congested traffic condition. The traffic index value range is 0-1, and the traffic index value range is divided into four levels according to relevant models and algorithms:
(1) when the traffic density is less than or equal to 0.3, the traffic density is low, the road traffic is in the optimal state, and the congestion phenomenon is avoided;
(2) when the traffic density is greater than 0.3 and less than or equal to 0.6, the traffic density is medium density, the road traffic is basically in a smooth state, and the occasional road section slowly travels in a small range;
(3) when the traffic density is greater than 0.6 and less than or equal to 0.8, the road is high density, the road traffic is in a partially congested state, and a certain degree of congestion occurs on a partial road section;
(4) when the traffic density is greater than 0.8, the traffic density is considered as congested traffic, the road traffic has large-range severe congestion, and part of the road may be in a standstill.
b) The population activity along the line is set into three grades according to the conditions of residential areas and residential districts around the road and the population dwelling density:
(1) when no or a small number of closed residential districts are arranged around the road, and no resident population or small population density is low population liveness;
(2) when partial discontinuous closed residential districts are arranged at the periphery of the road and a certain number of resident population exists but the population density is relatively not concentrated, the degree of activeness of the middle population is determined;
(3) when the road is in a large residential area or the periphery of the road is provided with continuous open residential districts, and the living population is large or the population density is concentrated, the road is high in population activity.
c) The traffic safety risk sets road safety into three levels according to the number of traffic accidents on the road or in the area where the road is located:
(1) the method comprises the following steps of (1) low-risk roads, wherein no traffic accidents occur on the roads or in the areas where the roads are located or the number of the traffic accidents is small;
(2) the method comprises the following steps that (1) a certain number of traffic accidents occur on a medium-risk road or an area where the road is located;
(3) traffic accidents frequently occur on high-risk roads, on roads or in areas where roads are located.
d) Traffic participants include, but are not limited to, cars, vans, buses, non-motorized vehicles, pedestrians, obstacles, animals, and the like.
The environmental impact evaluation elements include, but are not limited to, weather, light, visual field conditions, connectivity, etc., wherein:
a) weather includes, but is not limited to: information of different weather types such as sunny days, rainy days, snowy days, foggy days and the like and different visibility and the like;
b) illumination refers to environments under different light levels, including but not limited to: such as environment with different illumination intensity such as sunny day, night, dusk, and backlight;
c) visual field conditions include, but are not limited to: presence or absence of plant or building shading, etc.;
d) connectivity includes, but is not limited to: network connectivity of the environment where the vehicle is located, V2X connectivity, whether characteristics such as high-precision maps are supported, and the like.
According to evaluation elements of three dimensions of road complexity, traffic flow complexity and environmental influence degree and evaluation indexes contained in the evaluation elements, a variation coefficient method is adopted to comprehensively evaluate each influence element of the open road test, and the evaluation elements are used as a basis for determining index weights of each element. The coefficient of variation formula of each evaluation element is shown in formula (1):
wherein the content of the first and second substances,viis the coefficient of variation, σ, of the index of the item iiIs the standard deviation of the i-th index,
is the average of the i-th index. The weights of the indices are shown in equation (2):
the intelligent level of the road for the intelligent networking automobile open test can be evaluated according to the weight coefficient and the average score of each evaluation element, and the evaluation formula is shown as a formula (3):
wherein S iskTotal score, ω, for the kth road sectioniTo evaluate the weighting factor of the ith index of an element,
in order to evaluate the average score of the ith index of the element, the assigned value range of each index of the test road section is 0-10.
The total score of the roads of each test road section is calculated according to a formula (3), normalization processing is carried out as shown in a formula (4), the range of the total score is mapped to be within 0-100 points, the intelligent grade of the open road is divided into five grades in the embodiment, the test difficulty is the lowest when the intelligent grade of the road is I1 grade, and the test difficulty is the highest when the intelligent grade of the road is I5 grade.
The classification criteria are as follows:
when the test road section is totally divided into (20, 40), the intelligent level of the road is I1 level;
when the test road section is totally divided into (20, 40), the intelligent level of the road is I2 level;
when the test road section is totally divided into (40, 60), the intelligent level of the road is I3 level;
when the test road section is totally divided into (60, 80), the intelligent level of the road is I4 level;
when the test road segment total is (80, 100), the intelligentized level of the road is level I5.
And S120, determining the networking grade of the current road section according to the infrastructure digitization degree of the current road section.
The method is characterized in that the network communication technology is adopted to realize that the automatically driven vehicle needs to test the road to provide digital infrastructure, namely information support, the open road can have the digital capability only after being subjected to informatization transformation, and the road network networking grade can be evaluated by evaluating whether the open road provides static digital information/map support, whether the open road provides dynamic digital information, whether the open road provides information real-time transmission capability, whether the open road has the capability of guiding traffic and the like. Therefore, the open road is subjected to networking grading according to the digitization degree of the infrastructure, and the networking grading of the open road is divided into five grades, wherein the N1 grade provides the least digital infrastructure, the N5 grade provides the most digital infrastructure, and the supporting degree for the automatic driving test is also the highest, and the grading standard is shown in the table 2.
TABLE 2 road networking classification
And S130, determining the intelligent networking grade of the current road section according to the intelligent grade and the networking grade.
In this embodiment, the intelligent level and the networking level have the same level number, for example, both are 5 levels, the intelligent level and the networking level of the current road section are compared, and the lowest level is used as the intelligent networking level of the current road section. That is, if the road intelligent level and the networking level of the kth road section are Ip and Nq respectively, the intelligent networking automobile testing capability level of the road section is Rj, wherein j is min { p, q }.
According to the technical scheme, the grading evaluation system for the open road for the intelligent networking automobile test is constructed, the grading of the open road for the test can be scientifically and quantitatively guided, so that the differentiated test requirements are provided according to the intelligent level of the road, the networking level and the technical level of the intelligent networking automobile, and the test requirements of the intelligent networking automobile and the traffic safety of the open road can be better coordinated in the test process.
Examples
Taking an open road for testing an intelligent internet automobile in a certain city as an example, one road section is selected to carry out open road intelligent internet grading evaluation on the road section. Considering that the normal running of non-tested vehicles and the normal running of surrounding roads cannot be influenced, the intelligent networked automobile test should avoid the peak time periods in the morning and the evening in principle, so that the conventional state of the peak leveling time period is only considered for evaluation element indexes of road complexity, traffic flow complexity and environmental influence degree.
And (3) fully investigating the road section, recording the conditions of each evaluation element of the road section, inviting a plurality of experts to score the evaluation elements, and calculating to obtain the intelligent grade of the road section by combining the formulas (1) to (4). And (5) obtaining the networking grade of the road section according to the current condition of investigation and the combination of the table 2. And comparing the intelligent level of the road section with the networking level, wherein the lowest level is used as the intelligent networking level of the road section. After the open road classification, a test road matched with the open road can be opened for testing according to the automatic driving capability of the intelligent network automobile.
The embodiment of the invention also provides an open road grading device based on the intelligent networking automobile test, which is characterized by comprising the following components:
the intelligent level determining module is used for determining the intelligent level of the current road section according to the road complexity, the traffic complexity and the environmental influence degree of the current road section;
the networking grade determining module is used for determining the networking grade of the current road section according to the digitization degree of the infrastructure of the current road section;
and the intelligent networking grade determining module is used for determining the intelligent networking grade of the current road section according to the intelligent grade and the networking grade.
The road complexity evaluation elements comprise road network topological structure, road type, road geometry, road surface, intersection condition, separation mode, road surface mark, traffic sign and road facility elements.
The traffic flow complexity evaluation elements comprise traffic density, population activeness along a line, traffic safety risk degree and traffic composition condition.
The environmental impact degree evaluation elements comprise weather, illumination, visual field conditions and connectivity.
Further, the intelligent level determination module is specifically configured to: determining the variation coefficient of each evaluation element by a variation coefficient method according to the evaluation elements of the road complexity, the traffic flow complexity and the environmental influence degree and evaluation indexes contained in the evaluation elements;
determining the weight coefficient of each evaluation element according to the variation coefficient;
determining the total score of the current road section according to the weight coefficient and average value of each evaluation element;
and determining the intelligent level of the current road according to the total score.
The networking level determining module is specifically configured to: and comparing the intelligent level and the networking level of the current road section, and taking the lowest level as the intelligent networking level of the current road section.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (7)
1. An open road grading method based on intelligent networking automobile test is characterized by comprising the following steps:
s110, determining an intelligent level of the current road section according to the road complexity, the traffic complexity and the environmental influence degree of the current road section;
s120, determining the networking grade of the current road section according to the digitalization degree of the infrastructure of the current road section;
and S130, determining the intelligent networking grade of the current road section according to the intelligent grade and the networking grade.
2. The method of claim 1, wherein the road complexity evaluation elements comprise road network topology, road types, road geometry, road surfaces, intersection conditions, separation, pavement markings, traffic signs, and infrastructure elements.
3. The method of claim 1, wherein the traffic flow complexity evaluation elements include traffic density, along-line population activity, traffic safety risk, and traffic composition.
4. The method of claim 1, wherein the environmental impact evaluation elements include weather, lighting, field of view, and connectivity.
5. The method according to any one of claims 2 to 4, wherein S110 specifically comprises:
determining the variation coefficient of each evaluation element by a variation coefficient method according to the evaluation elements of the road complexity, the traffic flow complexity and the environmental influence degree and evaluation indexes contained in the evaluation elements;
determining the weight coefficient of each evaluation element according to the variation coefficient;
determining the total score of the current road section according to the weight coefficient and average value of each evaluation element;
and determining the intelligent level of the current road according to the total score.
6. The method of claim 1, wherein the intelligent classes and the networking classes have the same number of classes, and accordingly, S130 comprises:
and comparing the intelligent level and the networking level of the current road section, and taking the lowest level as the intelligent networking level of the current road section.
7. The utility model provides an open road grading plant based on intelligent networking automobile test which characterized in that includes:
the intelligent grade determining module is used for determining the intelligent grade of the current road section according to the road complexity, the traffic complexity and the environmental influence degree of the current road section;
the networking grade determining module is used for determining the networking grade of the current road section according to the digitization degree of the infrastructure of the current road section;
and the intelligent networking grade determining module is used for determining the intelligent networking grade of the current road section according to the intelligent grade and the networking grade.
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