CN110087280B - Vehicle density estimation method based on beacon message - Google Patents
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- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
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Abstract
The invention relates to a vehicle density estimation method based on beacon messages, and belongs to the field of vehicle-mounted self-organizing networks. The method comprises the following steps: s1: the vehicles collect and process the traffic information of the surrounding vehicles through the beacon messages, a vehicle neighbor node information table is constructed, and the number of neighbor nodes of the vehicles is calculated according to the information in the neighbor node information table; s2: classifying the distribution condition of the vehicle neighbor nodes according to the constructed neighbor node information table, calculating the retention time of the neighbor nodes in the estimated vehicle communication range by combining the distribution category of each neighbor node, and updating the neighbor node information table according to the retention time; s3: and establishing a vehicle-to-vehicle distance distribution function of the vehicle, and calculating the maximum likelihood estimation of the corresponding distribution function to estimate the vehicle density.
Description
Technical Field
The invention belongs to the field of vehicle-mounted self-organizing networks, and relates to a vehicle density estimation method based on beacon messages.
Background
As the number of automobiles increases, vehicle safety, traffic congestion, and driving experience have become three issues of great concern. An Intelligent and networked Transportation control System (ITS) is established to effectively alleviate the problems, so that an Intelligent Transportation System (ITS) is developed. The vehicle-mounted self-organizing network is an important component of an intelligent traffic system, vehicles in the vehicle-mounted network can realize communication among the vehicles through a wireless communication technology, the vehicles can predict road conditions according to received information, the probability of traffic accidents is reduced, driving can be assisted, and the driving experience of drivers is improved.
The vehicle density is one of important indexes for evaluating the road traffic condition, and has great influence on the communication performance of the vehicle-mounted ad hoc network. Most current vehicle density estimation methods are based on infrastructure to perform density estimation, for example, one vehicle density estimation method is to generate a multi-column full convolution neural network model by using deep learning training, take road image information as input, and obtain a vehicle density distribution map according to the output of the network model. Although the method can accurately and quickly complete the estimation task in the daytime, the vehicle density has larger errors due to the obvious reduction of the image precision at night. Another vehicle density estimation method is to acquire a road video through a video data collection device and extract three frames of images in the video at a remote terminal for calculating the vehicle density, but this density estimation method cannot be used for real-time vehicle density estimation since it takes a long time to record and process the video. The above method requires that the detection device (such as an induction loop detector or a traffic monitoring camera) is installed at different positions to acquire corresponding vehicle information, and is greatly influenced by external conditions, long in processing time and low in applicability.
With the rise of the vehicle-mounted ad hoc network, vehicle nodes can communicate with each other based on a wireless channel, so that the vehicles can collect and process traffic information without depending on any fixed infrastructure, and the vehicles can complete the estimation of vehicle density according to the received beacon messages during the running process. After the vehicle in the vehicle-mounted network acquires the surrounding vehicle density, a corresponding communication mechanism can be formulated according to the vehicle density so as to improve the performance of the vehicle-mounted network, so that the estimation of the vehicle density has important research significance.
Disclosure of Invention
In view of the above, the present invention provides a method for estimating vehicle density based on beacon messages, which is applied to vehicle density estimation based on inter-vehicle communication in a vehicle-mounted ad hoc network.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for estimating vehicle density based on beacon messages is characterized by comprising the following steps:
s1: collecting and processing traffic information of surrounding vehicles by the vehicles through Beacon messages (Beacon messages), constructing a vehicle neighbor node information table, and calculating the number of neighbor nodes of the vehicles according to information in the neighbor node information table;
s2: classifying the distribution conditions of the vehicle neighbor nodes according to the constructed neighbor node information table, calculating the retention time of the neighbor nodes in the estimated vehicle communication range by combining the distribution category of each neighbor node, and updating the neighbor node information table according to the retention time;
s3: and establishing a vehicle-to-vehicle distance distribution function of the vehicle, and calculating the maximum likelihood estimation of the corresponding distribution function to estimate the vehicle density.
Further, the step S1 specifically includes the following steps:
s11: the method comprises the steps that neighbor nodes periodically broadcast beacon messages, wherein the beacon messages comprise vehicle positions, speeds, driving directions and neighbor node numbers, and the neighbor node numbers are the neighbor node numbers positioned in front of and behind the nodes;
s12: the vehicle establishes a neighbor node information table according to the received beacon message, calculates the relative position of each neighbor node and the vehicle based on the position information in the information table, and screens out neighbor nodes which are farthest away from the vehicle in front of and behind the vehicle;
s13: the vehicle calculates the number of neighbor nodes in a one-hop range according to the data in the neighbor node information table, and calculates the number of two-hop neighbor nodes according to the number of the neighbor nodes of the farthest one-hop node.
Further, the step S2 specifically includes the following steps:
s21: dividing the distribution conditions of the neighbor nodes into six categories according to the positions of the neighbor nodes, the front and back, the speed and the driving direction;
s22: let the speed of the vehicle be V o The propagation range is R, and the speed of the kth neighbor node of the vehicle is V k At a distance R from the vehicle k And calculating the retention time t of the kth neighbor node in the vehicle propagation range by combining six types of distribution conditions of the neighbor nodes k The calculation formula is as follows:
wherein P represents the position information for storing the neighbor nodes, P is 1 and represents the position in front of the vehicle, and P is-1 and represents the position behind the vehicle; d represents the direction information for storing the neighbor nodes, D is 1 and represents the same direction as the vehicle, D is-1 and represents the opposite direction to the vehicle; q represents the speed information for storing the neighbor nodes, Q is 1, the speed is greater than that of the vehicle, and Q is-1, the speed is less than that of the vehicle;
s23: setting the retention time of the neighbor node as the survival time of the corresponding node in the vehicle neighbor node information table, if no beacon message of the corresponding node arrives in the updating interval, considering that the node leaves the vehicle communication range, and deleting the node information in the information table.
Further, the step S3 specifically includes the following steps:
s31: let vehicle A be the estimated vehicle, x 1 Representing the number of neighbor nodes, y, in the range of one hop of the vehicle A 1 Representing the distance, n and r, that the furthest neighbor node B is from vehicle A 1 The propagation range of the vehicle A is R, the vehicle density is rho vehicle/kilometer, and the number x of the neighbor nodes of the vehicle A is calculated 1 = n, and distance y to one-hop farthest neighbor node B 1 ＝r 1 Probability P (y) 1 ＝r 1 ,x 1 ＝n)；
S32: let vehicle C be the farthest one-hop neighbor node, x, of node B 2 Representing the number of neighbor nodes in a node B hop range, y 2 Represents the distance, m and r, from the boundary of the propagation range of the vehicle A to the vehicle C 2 As constants, respectively representing the number of neighbor nodes actually existing in the one-hop range of the node B and the actual distance from the boundary of the propagation range of the vehicle A to the vehicle C, calculating the number of two-hop neighbor nodes of the vehicle A as n + m, and the distance from the two-hop farthest neighbor node C as r 2 Probability of + R P (y) 1 ＝r 1 ,x 1 ＝n,y 2 ＝r 2 ,x 2 ＝m)；
S33: maximizing the probability P (y) based on maximum likelihood estimation 1 ＝m 1 ,x 1 ＝n 1 ,y 2 ＝m 2 ,x 2 ＝n 2 ) The vehicle density p is an optimal estimated value, and the calculation formula is as follows:
wherein K represents the number of samples used for estimation, n i +m i The number of two-hop neighbor nodes of the estimated vehicle in the ith sample is represented.
The invention has the beneficial effects that: the invention can be applied to vehicle density estimation based on inter-vehicle communication in the vehicle-mounted self-organizing network. Compared with the traditional vehicle density estimation method, the method has the advantages of wider application range, stronger environmental adaptability, higher estimation precision and higher speed.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of beacon message based vehicle density estimation according to the present invention;
FIG. 2 is a flowchart illustrating a neighbor node update procedure according to the present invention;
FIG. 3 is a diagram illustrating the distribution of neighboring nodes according to the present invention;
FIG. 4 is a vehicle map according to the present invention;
FIG. 5 is a simulation diagram of absolute error of the method of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and embodiments may be combined with each other without conflict.
As shown in FIG. 1, the acquisition of vehicle density in the present invention is mainly divided into three steps, firstly, the present invention provides a neighbor node estimation method based on beacon messages, which establishes a neighbor node information table based on received beacon messages, and calculates neighbor nodes in a vehicle two-hop range according to the information in the table; then, according to the positions of the nodes, the driving directions and the speed, dividing the distribution conditions of the neighboring nodes into six types, and calculating corresponding retention time for each type of distribution to update an information table; and finally, establishing a vehicle distance distribution function, and obtaining an estimated value of the vehicle density by solving the maximum likelihood estimation of the distribution function.
The invention relates to a vehicle density estimation method based on neighbor nodes, which comprises the following steps:
establishing neighbor node information table and calculating neighbor node number
1) The method comprises the steps that neighbor nodes periodically broadcast beacon messages, wherein the beacon messages comprise the positions, the speeds, the driving directions and the number of the neighbor nodes, and the number of the neighbor nodes is divided into the number of the neighbor nodes positioned in front of and behind the nodes;
2) The vehicle establishes an information table of the neighbor nodes according to the received beacon message, and specifically as shown in table 1, the vehicle label is a unique identifier which is allocated to each neighbor node by the estimated vehicle; the position is the relative position of the neighbor node and the estimated vehicle, and represents that the neighbor node is positioned in front of or behind the estimated vehicle; the distance is the distance between the neighbor node and the estimated vehicle; the number of vehicles in front of or behind the neighbor node is a one-hop neighbor node of the estimated vehicle neighbor node; the retention time is the survival time of the node in the table, and if the retention time is reduced to zero and no beacon message of the corresponding node arrives, the information of the node is deleted.
Table 1 neighbor node information table
3) According to the position information and the distance information in table 1, the estimated vehicle can screen out the most front and the most rear neighbor nodes, and the information is stored in table 2. In Table 1, the number of nodes in front of the vehicle is estimated as n front The number of nodes located at the rear is n back Then n = n front +n back 。
Table 2 farthest neighbor node information table
The information in table 2 is combined to obtain the number of two-hop neighbor nodes in front of and behind the vehicle:
the total number of neighbor nodes in the two-hop range of the vehicle is estimated as follows:
N total ＝N front +N back ＝n front +n back +m front +m back ＝n+m front +m back (2)
(II) updating neighbor node information table
The neighbor node update steps are as follows, see fig. 2:
1) And after receiving the beacon message, judging whether the information of the beacon message source node exists in the information table or not, if so, updating the corresponding node information, and if not, adding a line in the information table for storing the node information.
2) The retention time is the key for updating the information table, and the specific steps of the calculation are as follows:
a. the distribution of the neighboring nodes is divided into six categories according to the positions of the neighboring nodes, the speed and the driving direction, as shown in fig. 3.
b. Taking the speed of the vehicle as V o The propagation range is R, and the speed of the kth neighbor node of the vehicle is V k At a distance R from the vehicle k And calculating the retention time t of the kth neighbor node in the vehicle propagation range by combining the six distribution conditions of the neighbor nodes k The calculation formula is as follows:
wherein P represents the position information for storing the neighbor nodes, P is 1 and represents the position in front of the vehicle, and P is-1 and represents the position behind the vehicle; d represents the direction information for storing the neighbor nodes, D is 1 and represents the same direction as the vehicle, D is-1 and represents the opposite direction to the vehicle; q represents the speed information for storing the neighbor nodes, Q is 1, which represents that the speed is greater than that of the vehicle, Q is-1, which represents that the speed is less than that of the vehicle;
3) If the retention time is reduced to zero and no beacon message of the corresponding node arrives, the node is considered to have driven away from the communication range of the estimated vehicle, and the information of the node is deleted.
(III) estimating vehicle Density
Vehicle distribution referring to fig. 4, the vehicle density estimation steps are as follows:
1) Let vehicle A be the estimated vehicle, x 1 Representing the number of neighbor nodes, y, in a one-hop range of the vehicle A 1 Representing the distance, n and r, that the furthest neighbor node B is from vehicle A 1 The propagation range of the vehicle A is R, and the vehicle density is rho vehicles/kilometer, the number x of the neighbor nodes in one hop range is x 1 The probability of = n is:
then at x 1 ＝nThe distance y between the farthest one-hop neighbor node B and the vehicle A 1 ≤r 1 The probability of (c) is:
the joint equation (4) can be further derived:
the neighbor node number x of the vehicle A can be obtained by calculating the derivative of the formula (6) 1 = n, and distance y to one-hop farthest neighbor node B 1 ＝r 1 Probability of (c):
2) Let vehicle C be the farthest one-hop neighbor node, x, of node B 2 The number of neighbor nodes in one-hop range of the node B is represented, namely the number of neighbor nodes of the second hop of the vehicle A, y 2 Indicating the distance, m and r, from the boundary of the transmission range of vehicle A to vehicle C 2 Is constant and respectively represents the number of neighbor nodes actually existing in the one-hop range of the node B and the actual distance from the boundary of the propagation range of the vehicle A to the vehicle C, because y 1 ＝r 1 Is represented by (r) 1 R) has no node (otherwise the node will become the farthest one-hop neighbor node for vehicle a). Therefore the two-hop neighbor node of vehicle A must be located at (R, R + R) 2 ) In, then in x 1 ＝n，y 1 ＝r 1 In the case of (2), the number x of second-hop neighbor nodes of the vehicle A 2 The probability of = m is:
calculating the number of two-hop neighbor nodes of the vehicle A as n + m and the distance from the two-hop farthest neighbor node C as r by combining the formula (7) 2 The probability of + R, which is calculated by the formula:
3) Maximizing the probability P (y) based on maximum likelihood estimation 1 ＝m 1 ,x 1 ＝n 1 ,y 2 ＝m 2 ,x 2 ＝n 2 ) The vehicle density rho is an optimal estimated value, and the specific calculation steps are as follows:
according to the probability P (y) 1 ＝m 1 ,x 1 ＝n 1 ,y 2 ＝m 2 ,x 2 ＝n 2 ) Establishing a maximum likelihood function related to the vehicle density rho, wherein the calculation formula is as follows:
taking the logarithm of equation (9) yields:
derivative of lnL (ρ):
making equation (10) equal to 0 yields:
so that the best estimate of vehicle density
Comprises the following steps:
where K denotes the number of samples taken for estimation, n i +m i The number of two-hop neighbor nodes of the estimated vehicle in the ith sample is represented and can be calculated according to the formula (1).
The effect of the present invention is further described below with the simulation experiment:
the simulation experiment was performed using SUMO and NS 3.26. An expressway moving model is generated in SUMO software, the distance between vehicles in the model obeys exponential distribution with average vehicle density of rho vehicles/kilometer, the propagation radius of the vehicles is set to be 250m, the moving speed is set to be 30m/s, the data packet size of a beacon message is 500byte, the transmission rate is 6Mbit/s, and the broadcasting period is 0.05s. The best estimate from the vehicle density estimation method is compared with the global density p and the absolute error is calculated as
The simulation was repeated multiple times to record data and the Mean Absolute Error (MAE) calculated by averaging the Absolute Error found for each simulation data. As shown in fig. 5, the accuracy based on the estimated density gradually increases as the number of sample estimates increases. For example, at ρ = 10/km, the MAE of the estimated density is reduced from 40% to around 16% by increasing the number of samples from 1 to 10. When the number of samples k =10, the MAE of the estimated density is lower than 10% in most cases, and it can be seen that in the case of the limited estimated vehicle propagation radius R, the more samples are used for estimation, and the higher the accuracy is
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (1)
1. A method for estimating vehicle density based on beacon messages is characterized by comprising the following steps:
s1: the vehicles collect and process the traffic information of the surrounding vehicles through the beacon messages, a vehicle neighbor node information table is constructed, and the number of neighbor nodes of the vehicles is calculated according to the information in the neighbor node information table; the method specifically comprises the following steps:
s11: the method comprises the steps that neighbor nodes periodically broadcast beacon messages, wherein the beacon messages comprise vehicle positions, speeds, driving directions and neighbor node numbers, and the neighbor node numbers are the neighbor node numbers positioned in front of and behind the nodes;
s12: the vehicle establishes a neighbor node information table according to the received beacon message, calculates the relative position of each neighbor node and the vehicle based on the position information in the information table, and screens out neighbor nodes which are farthest away from the vehicle in front of and behind the vehicle;
s13: the vehicle calculates the number of neighbor nodes in a one-hop range according to the data in the neighbor node information table, and calculates the number of two-hop neighbor nodes according to the number of the neighbor nodes of the farthest one-hop node;
s2: classifying the distribution condition of the vehicle neighbor nodes according to the constructed neighbor node information table, calculating the retention time of the neighbor nodes in the estimated vehicle communication range by combining the distribution category of each neighbor node, and updating the neighbor node information table according to the retention time; the method specifically comprises the following steps:
s21: dividing the distribution conditions of the neighbor nodes into six categories according to the positions of the neighbor nodes, the front and back, the speed and the driving direction;
s22: let the speed of the vehicle be V o The propagation range is R, and the speed of the kth neighbor node of the vehicle is V k At a distance R from the vehicle k And calculating the retention time t of the kth neighbor node in the vehicle propagation range by combining six types of distribution conditions of the neighbor nodes k The calculation formula is as follows:
wherein P represents the position information for storing the neighbor nodes, P is 1 and represents the position in front of the vehicle, and P is-1 and represents the position behind the vehicle; d represents the direction information for storing the neighbor nodes, D is 1 and represents the same direction as the vehicle, D is-1 and represents the opposite direction to the vehicle; q represents the speed information for storing the neighbor nodes, Q is 1, the speed is greater than that of the vehicle, and Q is-1, the speed is less than that of the vehicle;
s23: setting the retention time of the neighbor node as the survival time of the corresponding node in the vehicle neighbor node information table, if no beacon message of the corresponding node arrives in the updating interval, considering that the node leaves the vehicle communication range, and deleting the node information in the information table;
s3: establishing a vehicle distance distribution function of the vehicle, and calculating the maximum likelihood estimation of the corresponding distribution function to estimate the vehicle density; the method specifically comprises the following steps:
s31: let vehicle A be the estimated vehicle, x 1 Representing the number of neighbor nodes, y, in a one-hop range of the vehicle A 1 Representing the distance, n and r, that the farthest neighbor node B is from vehicle A 1 The propagation range of the vehicle A is R, the vehicle density is rho vehicle/kilometer, and the number x of the neighbor nodes of the vehicle A is calculated 1 = n, and distance y to one-hop farthest neighbor node B 1 ＝r 1 Probability P (y) of 1 ＝r 1 ,x 1 ＝n)；
S32: let vehicle C be the farthest one-hop neighbor node, x, of node B 2 Representing the number of neighbor nodes in a node B hop range, y 2 Represents the distance from the propagation range boundary of the vehicle A to the vehicle C, m and r 2 The number of the neighbor nodes actually existing in the one-hop range of the node B and the actual distance from the boundary of the propagation range of the vehicle A to the vehicle C are respectively expressed as constants, the number of the two-hop neighbor nodes of the vehicle A is calculated to be n + m, and the distance from the two-hop farthest neighbor node C is calculated to be r 2 Probability of + R P (y) 1 ＝r 1 ,x 1 ＝n,y 2 ＝r 2 ,x 2 ＝m)；
S33: maximizing the probability P (y) based on maximum likelihood estimation 1 ＝m 1 ,x 1 ＝n 1 ,y 2 ＝m 2 ,x 2 ＝n 2 ) The vehicle density p is an optimal estimated value, and the calculation formula is as follows:
wherein K represents the number of samples used for estimation, n i +m i The number of two-hop neighbor nodes of the estimated vehicle in the ith sample is represented.
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