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A Method for Recognizing Stop Sections in Vehicle Trajectories Based on Dynamic Threshold

A technology of dynamic threshold and vehicle trajectory, which is applied in the traffic control system of road vehicles, traffic flow detection, instruments, etc., can solve the problems of missing target vehicles, increasing the workload of case handling personnel, reducing the efficiency of case handling, etc. Case-handling time, improvement of case-handling efficiency, and high accuracy

Active Publication Date: 2018-06-01
TAIHUA WISDOM IND GRP CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] If the fixed threshold is set too large, it is possible to miss the stop section of the target vehicle. If the setting is too small, non-stop section will be introduced. Later, when the monitoring of the stop section is called, the workload of the case handlers will be increased and the case handling will be reduced. Efficiency; in addition, the distance between different road sections, traffic congestion, etc. are not the same, if a single threshold is used to limit, it is not applicable to all road sections, and the results obtained have many errors

Method used

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  • A Method for Recognizing Stop Sections in Vehicle Trajectories Based on Dynamic Threshold
  • A Method for Recognizing Stop Sections in Vehicle Trajectories Based on Dynamic Threshold
  • A Method for Recognizing Stop Sections in Vehicle Trajectories Based on Dynamic Threshold

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Embodiment 1

[0039] see figure 1 Shown is a flow chart of a method for identifying a stop section in a vehicle trajectory based on a dynamic threshold described in the present application, including:

[0040] Step 101. Obtain the passing trajectory of the target vehicle passing through the traffic checkpoints in chronological order. The number of checkpoints passed by the target vehicle is n, and the number of checkpoints passed by the target vehicle is k m , and the time of passing through each bayonet is denoted as the passing time t m , where, 1≤m≤n, the process trajectory of the target vehicle is expressed as: (k 1 ,t 1 ), (k 2 ,t 2 ), (k 3 ,t 3 ),……, (k n-1 ,t n-1 ), (k n ,t n );

[0041] Step 102: Segment the passing trajectory of the target vehicle through the traffic checkpoint to obtain n-1 checkpoint pairs, each checkpoint pair represents a section of the actual traffic road, and the checkpoint pair is expressed as: ((k 1 ,t 1 ), (k 2 ,t 2 )), ((k 2 ,t 2 ), (k ...

Embodiment 2

[0045] On the basis of Embodiment 1, in step 103, the said passing data is calculated and analyzed to obtain the dynamic threshold value of each bayonet pair and the time it takes for the target vehicle to pass through each bayonet pair, and determine the target vehicle Whether to stay in the road section corresponding to a checkpoint pair, further:

[0046] Step 201, for the i-th bayonet pair ((k i ,t i ), (k i+1 ,t i+1 )), get t i and t i+1 All passes through bayonet k within the current date i and bayonet k i+1 The passing data of , among them, 1≤i≤n-1.

[0047] In order to prevent the impact of abnormal conditions such as road construction or traffic control, the date here is in days.

[0048] Step 202, according to the passing data, calculate each vehicle through the bayonet pair ((k i ,t i ), (k i+1 ,t i+1 )) The time spent on the corresponding road section.

[0049] That is to say, for the current bayonet pair, the passing time t of all vehicles passing the...

Embodiment 3

[0066] A kind of application embodiment of the present invention is provided below, see image 3 .

[0067] Methods for identifying stop segments in vehicle trajectories based on dynamic thresholds include:

[0068]Step 301, using the distributed system infrastructure Hadoop to limit the time range and the license plate number of the target vehicle, and obtain the checkpoints and time series of passing vehicles in chronological order, such as Figure 4 As shown, assume that the number of checkpoints that the target vehicle passes is n, and the number of checkpoints that the target vehicle passes is k m Indicates that the passing time is represented by t m means (1≤m≤n);

[0069] Step 302, segment the passing bayonet track, see Figure 4 , assuming that the passing trajectory of the target vehicle is (k 1 ,t 1 ), (k 2 ,t 2 ), (k 3 ,t 3 ),……, (k n-1 ,t n-1 ), (k n ,t n ), decompose to get n-1 bayonet pairs:

[0070] ((k 1 ,t 1 ), (k 2 ,t 2 )),

[0071] ((k 2...

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Abstract

The present application discloses a method for identifying stop sections in vehicle trajectories based on dynamic thresholds, including: obtaining the passing trajectories of target vehicles passing through traffic checkpoints in chronological order, the number of checkpoints that the target vehicle passes through is n, and the checkpoints that the target vehicle passes through represent is km, the moment of passing through each checkpoint is tm, and the process trajectory of the target vehicle is expressed as (k1, t1), (k2, t2), ..., (kn, tn); for the passing of the target vehicle through the traffic checkpoint The trajectory is divided to obtain n-1 bayonet pairs, which are expressed as: ((k1,t1), (k2,t2)),..., ((kn-1,tn-1), (kn, tn)); obtain the passing data of each bayonet pair within a specified time period, analyze the passing vehicle data, obtain the dynamic threshold of each bayonet pair and the time spent by the target vehicle passing through each bayonet pair, and judge whether the target vehicle is in the Stop in the road section corresponding to a checkpoint pair.

Description

technical field [0001] The present application relates to the technical field of intelligent traffic control, and in particular, relates to a method for identifying a stop section in a vehicle trajectory based on a dynamic threshold. Background technique [0002] With the increasing popularity of motor vehicles, the number of criminal cases using motor vehicles as means of transportation or targeting motor vehicles continues to increase. While the number of various types of fraud, burglary, and robbery crimes involving motor vehicles is increasing, the number of theft and robbery cases targeting various types of motor vehicles is also gradually increasing. How to investigate the trajectory of the vehicle involved and how to obtain the information of the driver is very necessary. [0003] With the widespread use of monitoring equipment such as urban video surveillance and traffic checkpoints, and the gradual improvement of license plate and model recognition algorithms, vehi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01
CPCG08G1/0125G08G1/0137
Inventor 辛国茂李占强李庆功吴永李善宝周永利张同义曹连超马述杰
Owner TAIHUA WISDOM IND GRP CO LTD
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