CN108830488B - Road area risk degree evaluation method - Google Patents
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Abstract
The invention provides a road area risk degree evaluation method, which comprises the following steps: acquiring vehicle parameter information by using a sensor and an OBU (on-board unit) carried by a vehicle, taking a certain area in front of the running of the vehicle as a target, and collecting the parameter information of all vehicles in the target area; processing vehicle parameter information in a target area, calculating energy correlation strength of two vehicles and a workshop relation of the two vehicles, and obtaining group energy of the target area through a group energy formula of a vehicle cluster; and abstracting the road into a lattice, and calculating the risk in each lattice according to the workshop relation of two vehicles and a group energy formula of the vehicles in the target area to obtain the area risk thermodynamic diagram. The method for evaluating the road area danger degree is more suitable for the actual road condition, and more effective in processing the complex relation of the road.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a road area risk degree evaluation method based on a cloud computing frame and an Ising model, which provides a research basis for intelligent driving.
Background
With the increase of the vehicle keeping quantity, the number of casualties is increased year by year due to traffic accidents, and the research on the traffic field is more and more concerned.
Intelligent traffic is an important direction for future development, and a road traffic system is very complex and is formed by combining multiple influences. In a complex traffic network, how to reasonably evaluate risks and avoid risks becomes one of key research projects in the traffic field, so evaluation of road risks is a very important topic in intelligent traffic. At present, automatic driving and assistant driving also become popular research projects in the field of intelligent transportation, and the research on the risk index of a driving road has very important reference significance for the decision of a vehicle driving path.
Whether the driver or the system acting as the driver behavior is in driving, the driver is not interested in the cause of the risk, only the distribution and the situation of the risk need to be known, and the avoidance of the risk is the most important purpose, and many driving behaviors or model evaluations of traffic systems exist for reducing the risk, and in the currently known risk evaluation models: in the article, "Predictive risk assessment for interactive ADAS functions", a concept of survival probability is cited, a vehicle transfer scene is used to explain probability of a collision event and a survival function of a construction time parameter, heuristic risk assessment is deduced, and multiple risk conditions are explained. However, the vehicle-passing approach proposed by this risk assessment only considers a single row of vehicles and is not suitable for real-scene applications.
In the article "Relating microscopical risk models with acquired statistics", in order to study the road microscopic risk assessment model, the Solomon curve is reinterpreted from the generalized risk model, and the parameters of the generalized risk model are revealed to be capable of completely mapping the parameters of the microscopic risk model. The asymmetry of the minimum value and the average value of the speed in the Solomon curve is predicted and explained through deduction, and the Solomon curve-based micro risk assessment model is verified. However, the main research is the relation between the individual vehicle speed and the accident probability at different time, and is statistical probability research based on history, and the research parameters are single, and the reference value for road real-time evaluation is one-sided.
The article "A large vehicle first clustering method based road section level estimation" proposes an iterative model using a classical physical model for interpreting the relationship between vehicles. The composite index of the regional risk level of a large vehicle activity (CSPI, environmental condition of the target road segment) becomes an important parameter in the field of road safety. And (3) providing a comprehensive risk index representing real-time traffic conditions by combining the CSPI and the risk level of the vehicle cluster, and indicating the risk level of the road section passed by the large vehicle. However, the method mainly studies the influence of large vehicles, and the danger of a general road area cannot be completely reflected.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art, and particularly provides a road area risk degree evaluation method.
In order to achieve the above object of the present invention, the present invention provides a road area risk assessment method, which includes the steps of:
s1, acquiring vehicle parameter information including speed, driving direction and position by using a sensor and an OBU carried by a vehicle, uploading the vehicle parameter information to a cloud server, and collecting the parameter information including the speed, the driving direction and the position of all vehicles in a target area by taking a certain area in front of the driving vehicle as a target;
and S2, processing the vehicle parameter information in the target area, and calculating:
J(i,j)＝W(i,j)〈(H(i)-<H(i)>)(H(j)-<H(j)>)>，
wherein i and J are vehicle serial numbers, J is energy correlation strength, H (i) is vehicle health status, W (i, J) is weight factor,
k is an attenuation coefficient greater than 1, rho is the vehicle density in the target area, dijIs the distance between i, j two vehicles, d0For minimum safe distance between cars,. DELTA.vijFor the relative velocity of j car with respect to i car, the car-to-car relationship of i, j two cars is expressed as:
R(i,j)＝J(i,j)·H(i)·H(j)，
r (i, j) is the interaction of two vehicles, and the energy formula of the population of the vehicles in the target area is expressed as:
wherein V is the average velocity vector of a plurality of vehicles in the area,
and S3, abstracting the road into a lattice, calculating the risk in each lattice, and synthesizing to obtain the regional risk thermodynamic diagram.
The method and the device have the advantages that the condition of the road area is considered only one-sidedly in the existing other methods, the dynamic information of the vehicles on the road is considered comprehensively, the actual condition of the road is fitted better, and the complex relation of the road is processed more effectively.
In a preferred embodiment of the present invention, the method for calculating the risk in each cell in step S3 is:
and S31, acquiring the risk degree of each grid in the area, wherein the risk degree is the sum of the relationship action of the grid and other grids, and the relevance R of the grid without the vehicle is set to be 0. Let the risk of the grid corresponding to the ith vehicle be:
n is the number of vehicles;
s32, taking each grid as the center, calculating its radiation energy to surrounding points:
wherein L is the distance between the lattice to be calculated and the central lattice;
s33, each cell having an energy that is a superposition of the radiant energies of all other cells:
the method has the advantages that the calculation is convenient after the region is divided, and the obtained graph is discrete, so that the distribution of the region risk degree can be visually embodied.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic diagram of abstracting a road into a lattice in a first preferred embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The method improves the real road condition, establishes a model by explaining the relation between vehicles, and evaluates the risk thermodynamic diagram of the vehicle advancing area. In the road area, the risk evaluation of the vehicle cluster is actually a comprehensive risk evaluation of a group, which not only depends on the action relationship among the vehicles, but also is related to the action intention tendency of the individual PV of the vehicle, therefore, the group of the vehicle cluster shows a social relationship in fact, and the connotation of how to explain the relationship is the key to objectively and effectively evaluate the relationship.
Based on the cloud computing processing framework, the cloud computing processing framework is divided into three layers: cloud processing, fog node processing (MEC) and vehicle-mounted OBU processing. Cloud end processing is mainly used for vehicle mission planning, and regional vehicle data processing is mainly by fog node and on-vehicle OBU. The method comprises the steps of taking a region in front of a vehicle as a target, collecting vehicle parameter information (speed, driving direction, position and the like) of the target region, analyzing the information in a fog node processing unit, obtaining risk degree evaluation of the target region by utilizing an MEC technology, and finally generating a region risk thermodynamic diagram.
The invention provides a road area risk degree evaluation method, which comprises the following steps:
s1, acquiring vehicle parameter information including vehicle speed, driving direction and position by using a sensor and an OBU carried by a vehicle, uploading the vehicle parameter information to a cloud server, and collecting the parameter information of all vehicles in a target area including the vehicle speed, the driving direction and the position by taking a certain area in front of the driving of the vehicle as a target;
and S2, processing the vehicle parameter information in the target area, and calculating:
J(i,j)＝W(i,j)<(H(i)-<H(i)>)(H(j)-<H(j)>)>，
wherein i and J are vehicle serial numbers, J is energy correlation strength, H (i) is vehicle health status, W (i, J) is weight factor,
k is attenuation coefficient, the value is more than 1, rho is the vehicle density in the target area, dijIs the distance between i, j two vehicles, d0To a minimumSafe distance between vehicles, Δ vijFor the relative velocity of j car with respect to i car, the car-to-car relationship of i, j two cars is expressed as:
R(i,j)＝J(i,j)·H(i)·H(j)，
based on the research object of the invention, a road area scene is regarded as a two-dimensional plane, only the action relationship between every two vehicles in a target range is considered, and then the risk of the whole vehicle cluster is evaluated by combining the speed V of each vehicle, wherein R (i, j) is the interaction of the two vehicles, and the group energy formula of the vehicles in the target area is expressed as:
wherein V is the average velocity vector of a plurality of vehicles in the area,
s3, abstracting the road into a lattice, calculating the risk in each lattice as a dotted line lattice shown in figure 1, and synthesizing to obtain the regional risk thermodynamic diagram. When the grid lattice is divided, the size of the grid can be determined according to actual conditions, and the grid is preferably larger than the size of the vehicle, and when the vehicle (a dark rectangle in fig. 1) runs, the position of the gravity center of the vehicle is used for determining which grid the vehicle is positioned in.
In the present embodiment, the method of calculating the risk in each cell in step S3 is:
s31, obtaining the risk of each grid in the area, wherein the risk is the sum of the relationship action of the grid and other grids, setting the correlation R of the grid without vehicles as 0, and setting the risk of the grid corresponding to the ith vehicle as:
n is the number of vehicles;
and S32, taking the grid where the ith vehicle is located as the center, calculating the radiation energy of the ith vehicle to the surrounding points with the vehicles:
wherein L is the distance between the lattice to be calculated and the central lattice;
s33, each compartment having an energy that is a superposition of the radiant energies of all other compartments having vehicles:
at the moment, the obtained road area is divided into a risk degree evaluation thermodynamic diagram of a grid array, and after the thermodynamic diagram is obtained, a plurality of researches based on road traffic development, such as risk avoidance, path planning, intelligent driving and the like, can be further carried out according to the real-time evaluation diagram.
The invention is provided with a plurality of fog node processing units on the road test of vehicle running, and utilizes the nearest fog node processing unit to process the vehicle parameter information of the target area, so that the data volume is large, and the calculated pressure and delay are reduced by using the fog node processing.
In the present embodiment, the method for determining the specific vehicle health state h (i) may adopt the method for determining the permanent vehicle health parameter in paragraph 0036 of patent CN105303836B issued by the applicant.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example" or "some examples" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (2)
1. A road area risk assessment method is characterized by comprising the following steps:
s1, acquiring vehicle parameter information including speed, driving direction and position by using a sensor and an OBU carried by a vehicle, uploading the vehicle parameter information to a cloud server, and collecting the parameter information including the speed, the driving direction and the position of all vehicles in a target area by taking a certain area in front of the driving vehicle as a target;
and S2, processing the vehicle parameter information in the target area, and calculating:
J(i,j)＝W(i,j)<(H(i)-<H(i)>)(H(j)-<H(j)>)>，
wherein i and J are vehicle serial numbers, J is energy correlation strength, H (i) is vehicle health status, W (i, J) is weight factor,
k is an attenuation coefficient greater than 1, rho is the vehicle density in the target area, dijIs the distance between i, j two vehicles, d0For minimum safe distance between cars,. DELTA.vijFor the relative velocity of j car with respect to i car, the car-to-car relationship of i, j two cars is expressed as:
R(i,j)＝J(i,j)·H(i)·H(j)，
r (i, j) is the interaction of two vehicles, and the energy formula of the population of the vehicles in the target area is expressed as:
wherein V is the average velocity vector of a plurality of vehicles in the area,
s3, abstracting the road into a lattice, calculating the risk in each lattice, and comprehensively obtaining an area risk thermodynamic diagram, wherein the method for calculating the risk in each lattice comprises the following steps:
s31, acquiring the risk degree of each grid in the area, wherein the risk degree is the sum of the relationship action of the grid and other grids, and the relevance R of the grid without vehicles is set to be 0; let the risk of the grid corresponding to the ith vehicle be:
n is the number of vehicles;
s32, taking each grid as the center, calculating its radiation energy to surrounding points:
wherein L is the distance between the lattice to be calculated and the central lattice;
s33, each cell having an energy that is a superposition of the radiant energies of all other cells:
2. the method for assessing the risk of a road area according to claim 1, wherein a plurality of fog node processing units are provided on a road test on which the vehicle runs, the vehicle parameter information of the target area is processed by the nearest fog node processing unit, the data size is large, and the calculation pressure and delay are reduced by the fog node processing.
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| CN110379123A (en) * | 2019-07-26 | 2019-10-25 | 合肥金人科技有限公司 | A kind of emergency response system based on big data |
| CN110766005B (en) * | 2019-10-23 | 2022-08-26 | 森思泰克河北科技有限公司 | Target feature extraction method and device and terminal equipment |
| CN111062970A (en) * | 2019-12-10 | 2020-04-24 | 广州电力工程监理有限公司 | Track generation method and system based on thermodynamic diagram |
| CN113183960B (en) * | 2021-03-18 | 2023-06-30 | 北京汽车研究总院有限公司 | Environment danger degree calculating method and device, storage medium and controller |
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