CN111598069B - Highway vehicle lane change area analysis method based on deep learning - Google Patents
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Abstract
The invention discloses a highway vehicle lane change area analysis method based on deep learning, which comprises the steps of firstly carrying out structural modeling on lanes and lane lines of a road; simultaneously detecting the outer frame of the vehicle in the high-definition monitoring video of the highway; tracking the vehicle track in the video according to the detection result of the vehicle in each frame of image; combining the vehicle track with the road structured data, identifying the vehicle lane change according to the lane area where the vehicle passes, and detecting the vehicle lane change position according to the intersection position of the vehicle frame and the lane line; and finally, carrying out clustering analysis on the lane changing positions of the vehicles passing through in different time periods to obtain the lane changing hot spot areas of the vehicles on the expressway in different time periods. The method has the advantages of simple steps and accurate result, detects the lane changing behavior of the vehicle and analyzes the lane changing area by using the camera data in the expressway, and provides powerful support for the fine traffic management and lane design of the expressway.
Description
Technical Field
The invention relates to the field of driving behavior detection, in particular to a highway vehicle lane change area analysis method based on deep learning.
Background
With the rapid development of economic society, the demand of people on transportation and travel is continuously increased, especially the demand of long-distance travel. According to statistics, the number of the domestic automobiles at the end of 2019 is 26150 thousands, and 2122 thousands of automobiles are added compared with the number of the domestic automobiles at the end of the last year. The conflict between the increase in traffic demand and the existing road conditions is increasingly prominent. Highways are used as the main artery of traffic between cities, and the traffic pressure of the highways is increasing. Reasonable traffic management can effectively reduce the occurrence of traffic jam. In order to improve the traffic management level, the driving behavior of vehicles on the expressway needs to be accurately analyzed.
Lane changing of vehicles on a highway is the most common driving behavior, and lane changing refers to driving behavior of vehicles on a road to change lanes as required. The reasonable lane change on the expressway can improve the speed of the whole traffic flow, relieve traffic pressure and improve road traffic capacity, but the instability of the traffic flow can be increased by the frequent lane change and the unreasonable lane change, and the traffic danger is increased. In recent years, lane changing of vehicles has become an important cause of traffic accidents, and people have to pay attention to safety problems. Therefore, in order to ensure driving safety and fully and reasonably utilize road resources of the expressway, the lane changing behavior of vehicles on the expressway needs to be detected and analyzed.
The existing research is analyzed, the data collected by the highway at present mainly comprises traffic flow, vehicle speed, lane occupancy and the like, the data are macroscopic state data of traffic, and more microscopic traffic data are needed in traffic fine management. The lane change information is the very important microscopic traffic data. In conventional vehicle lane change detection, vehicle lane change parameters are typically acquired using onboard GPS positioning data. The method is limited by the positioning precision of the GPS, and simultaneously, not all vehicles carry the GPS, which causes the inaccuracy and incompleteness of the detection of the lane changing behavior of the highway vehicles, thereby providing the analysis method of the lane changing area of the highway vehicles based on deep learning.
Disclosure of Invention
In order to solve the problems, the invention provides a method for analyzing a lane change area of a highway vehicle based on deep learning, and aims to detect the lane change behavior of the vehicle based on the deep learning method by adopting cameras widely distributed on the highway and analyze based on a detection result so as to provide support for refined traffic management and lane design. To achieve the purpose, the invention provides a highway vehicle lane change area analysis method based on deep learning, which comprises the following steps:
(1) extracting a road background according to a monitoring video of the expressway, and dividing lane areas according to lanes and lane lines in the road background to obtain road structured data;
(2) detecting the vehicle in each frame of image of the monitoring video by adopting a target detection model based on deep learning to obtain the outer frame of the vehicle;
(3) tracking the vehicle track in the monitoring video according to the detection result of the vehicle in each frame of image;
(4) combining the vehicle track with the road structured data, identifying the vehicle lane change according to the lane area where the vehicle passes, and detecting the vehicle lane change position according to the intersection position of the outer frame of the vehicle and the lane line;
(5) and carrying out clustering analysis on the lane changing positions of the vehicles passing through in different time periods to obtain the lane changing hot spot areas of the vehicles on the expressway in different time periods.
As a further improvement of the present invention, the step (1) comprises the following substeps:
(1.1) for the monitoring video on the expressway, selecting continuous T frame video pictures, superposing and summing the collected pictures, and then solving an average value as a road background, wherein the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,Pthe picture is a background picture of the road,I i is as followsiThe number of frames of a picture is,Tis a continuous frame number;
(1.2) loading a road background, drawing a road lane line and lane areas on the road background, calculating the minimum external rectangles of all the lane areas as the lane areas, and storing all the result data in a json format to obtain road structured data;
as a further improvement of the invention, the step (2) comprises the following steps:
the step (2) comprises the following substeps:
(2.1) cutting out a lane area image from the video frame image according to the lane area obtained in the step (1);
and (2.2) inputting the lane area image into a trained target detection model based on deep learning, calculating and outputting the outer frame of the vehicle in the picture, wherein the target detection model is EfficientDet, and training by using the manually marked vehicle detection frame data.
As a further improvement of the invention, in the step (3), the vehicle track in the monitoring video is tracked by adopting an SORT method.
As a further improvement of the present invention, the step (4) includes the following substeps:
(4.1) newly establishing a variable D = { key, value }, and recording the lane number of the vehicle, wherein key is the vehicle number, and value is the lane number;
(4.2) traversing all vehicles according to the track result of each frame of picture, calculating the lane number of the vehicle, and analyzing according to the lane number and the vehicle number:
if the lane number does not exist, directly analyzing the next vehicle;
if the vehicle number is not in the key of the variable D, adding the vehicle number and the lane number where the current vehicle is located into the variable D;
and if the vehicle number is already in the key of the variable D, further judging whether the lane number is changed: if the lane number is not changed, directly analyzing the next vehicle, and if the lane number is changed, considering that lane change occurs;
and (4.3) for the vehicle with the lane change, performing intersection calculation by using the edge of the lower edge of the outer frame of the vehicle and the lane line to obtain intersection point coordinates on the lane line, and taking the point as a lane change position point.
As a further improvement of the present invention, in the step (4.2), the lane number where the vehicle is located is calculated by the following specific method:
using trisection points A and B of the lower edge of an outer frame of the vehicle as judgment points of a lane where the vehicle is located, and judging whether the point A and the point B are in a polygon of a lane area by adopting a ray method; if both A and B are in the same lane area, the vehicle is considered to be in the lane.
As a further improvement of the present invention, said step (5) comprises the sub-steps of:
(5.1) dividing each lane line into N equal parts, selecting lane change position data of vehicles in a time period of t1-t2, and counting lane change times in equal segments of each lane;
(5.2) smoothing the number of conversion passes in each lane equal segment by using Gaussian filtering;
and (5.3) constructing a three-dimensional analysis space of the lane change region by using three elements of time, lane change position and lane change number, and performing visual analysis on the lane change region.
Compared with the prior art, the method for analyzing the lane change area of the highway vehicles based on deep learning has the following technical effects:
(1) the invention applies the efficient and accurate target detection method in the deep learning field to the detection of vehicles on the expressway, and simultaneously adopts a lightweight front-end interaction means to model the road lane line and the lane area, thereby greatly improving the convenience and the accuracy of vehicle lane change detection by combining the two methods, reducing the use of other sensors such as a GPS and the like, and belonging to the interdisciplinary application of the deep learning algorithm in the intelligent traffic field.
(2) By carrying out cluster analysis on lane change positions, the method can obtain the hot spot areas of the lane change of the vehicles on the expressway in different time periods, solve the problem that the lane change of the vehicles is difficult to analyze in the traditional method, and provide data support for refined traffic management.
Drawings
FIG. 1 is a general flow diagram of the process of the present invention;
FIG. 2 is a drawing showing the frames of a video frame;
FIG. 3 is a road background view;
FIG. 4 is a plot of lane lines and lane areas;
fig. 5 is a diagram showing a result of vehicle detection.
Detailed Description
The invention is described in further detail below with reference to the following figures and embodiments:
the invention provides a method for analyzing a lane change area of a highway vehicle based on deep learning, which aims to detect the lane change behavior of the vehicle by adopting cameras widely distributed on the highway based on the deep learning method, and simultaneously analyze based on a detection result so as to provide support for refined traffic management and lane design.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As an embodiment, a surveillance video on a certain highway is selected as a data source, and fig. 2 is a picture of 9 consecutive frames in the video.
As shown in fig. 1, a method for analyzing a lane change area of a highway vehicle based on deep learning includes the following steps:
(1) for the high-definition monitoring video on the expressway, extracting a road background, and performing structured modeling on lanes and lane lines of the road by using front-end interaction, namely dividing lane areas; the method specifically comprises the following substeps:
(1.1) for the monitored video frames, continuous 100 frames of video frames are selected, and preferably, no vehicle exists in the continuous 100 frames of video frames. The collected pictures are superposed and summed, then an average value is obtained as a road background, and the obtained result is shown in fig. 3, wherein the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,Pthe picture is a background picture of the road,I i is as followsiThe number of frames of a picture is,Tfor the number of consecutive frames, 100 is taken in this example.
(1.2) loading a road background picture on a front page, drawing a road lane line and a lane area on the road background picture, wherein the drawing result is shown in figure 4, meanwhile, calculating the minimum external rectangles of all the lane areas as the lane areas, and storing all the result data in a json format.
(2) The method comprises the following steps of detecting vehicles in each frame of image of a high-definition monitoring video of the expressway by adopting a target detection model based on deep learning, and acquiring an outer frame (Bounding box) of the vehicles, wherein the method comprises the following substeps:
(2.1) cutting out an image of the lane area from the video frame image according to the lane area obtained in the step (1.2) so as to reduce the calculation amount of target detection and the data amount of data transmission;
(2.2) inputting the lane area image into a vehicle detection model based on EfficientDet, and outputting the outer frame of the vehicle in the picture after calculation, wherein the vehicle detection model is trained in advance by using manually marked vehicle detection frame data, the model can provide a calling interface for the outside in an API (application programming interface) mode, and the detection result is shown in FIG. 5; the vehicle detection model is not limited to the EfficientDet, and models such as SSD, Yolo, Faster RCNN and the like can be adopted.
(3) And tracking the vehicle track in the video by using an SORT method according to the detection result of the vehicle in each frame of image, wherein the SORT method mainly comprises the steps of acquiring an adjacency matrix, simply matching, Hungary matching, updating the state and the like. Further, Deep SORT and the like can be used.
(4) The method combines the vehicle track with the road structured data, identifies the vehicle lane change according to the lane area where the vehicle passes, and detects the vehicle lane change position according to the intersection position of the outer frame of the vehicle and the lane line, and comprises the following substeps:
(4.1) creating a variable D = { key, value } with the type as a dictionary, and recording the lane number where the vehicle is located, wherein the key is the vehicle number and is obtained by the vehicle detection time number in the step (2), and the value is the lane number;
(4.2) traversing all vehicles according to the track result of each frame of picture, calculating the lane number of the vehicle, analyzing according to the lane number and the vehicle number, directly analyzing the next vehicle if the lane number does not exist, adding the vehicle number and the lane number where the current vehicle is located into a variable D if the vehicle number is not in the key of the variable D, and further judging whether the lane number is changed if the vehicle number is in the key of the variable D: and if the lane number is not changed, directly analyzing the next vehicle, and if the lane number is changed, considering that lane change occurs. The lane number calculation of the vehicle can adopt the following method: using trisection points A and B of the lower edge of an outer frame of the vehicle as judgment points of a lane where the vehicle is located, judging whether the point A and the point B are in a polygon of a lane area by adopting a ray method, namely making a horizontal ray from the point, if an odd number of intersection points exist in the polygon of the lane area, the point A and the point B are in the lane area, otherwise, the point A and the point B are not in the lane area, and if the point A and the point B are in the same lane area, the vehicle is considered to be in the lane.
And (4.3) for the vehicle with the lane change, performing intersection calculation by using the edge of the lower edge of the outer frame of the vehicle and the lane line to obtain intersection point coordinates on the lane line, and taking the point as a lane change position point.
(5) Clustering analysis is carried out on the lane changing positions of the vehicles passing through in different time periods to obtain highway vehicle lane changing hot spot areas in different time periods, and the method comprises the following substeps:
(5.1) dividing each lane line into N equal parts, selecting vehicle lane change position data in a time period t1-t2, and counting lane change times in each lane equal section. For example, each lane line is divided into 10 equal parts, and a time slot 8 is selected: calculating lane change position data of vehicles between 00 and 10:00, counting the number of lane change in each lane equal segment in every 10 minutes, and obtaining the results of lane change statistics between lanes 1 and 2 as shown in table 1;
TABLE 1 statistical table of the number of passes
(5.2) smoothing the number of conversion passes in each lane equal segment by using Gaussian filtering, wherein the formula is as follows:
where x is a random variable (in this embodiment, the index of the position of the gaussian kernel), μ is the expected value of the gaussian distribution, and σ is the standard deviation of the gaussian distribution.
Selecting 1 x 3 Gaussian kernels in the smoothing, wherein mu is 0, sigma is 0.8, the obtained Gaussian kernels are [0.239,0.522 and 0.239], and the result of statistical smoothing of lane change of lane lines before lanes 1 and 2 is shown in table 2;
TABLE 2 statistics Gaussian smoothing Table for number of passes
And (5.3) constructing a three-dimensional analysis space of the lane change area by using three elements of time, lane line position of lane change and lane change number, and carrying out visual analysis on the lane change area, so that the lane change area is more visual.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (5)
1. A highway vehicle lane change area analysis method based on deep learning is characterized by comprising the following specific steps:
(1) extracting a road background according to a monitoring video of the expressway, and dividing lane areas according to lanes and lane lines in the road background to obtain road structured data;
(2) detecting the vehicle in each frame of image of the monitoring video by adopting a target detection model based on deep learning to obtain the outer frame of the vehicle;
(3) tracking the vehicle track in the monitoring video according to the detection result of the vehicle in each frame of image;
(4) combining the vehicle track with the road structured data, identifying the vehicle lane change according to the lane area where the vehicle passes, and detecting the vehicle lane change position according to the intersection position of the outer frame of the vehicle and the lane line; the method comprises the following substeps:
(4.1) newly establishing a variable D = { key, value }, and recording the lane number of the vehicle, wherein key is the vehicle number, and value is the lane number;
(4.2) traversing all vehicles according to the track result of each frame of picture, calculating the lane number of the vehicle, and analyzing according to the lane number and the vehicle number:
if the lane number does not exist, directly analyzing the next vehicle;
if the vehicle number is not in the key of the variable D, adding the vehicle number and the lane number where the current vehicle is located into the variable D;
and if the vehicle number is already in the key of the variable D, further judging whether the lane number is changed: if the lane number is not changed, directly analyzing the next vehicle, and if the lane number is changed, considering that lane change occurs;
the lane number of the vehicle is calculated, and the specific method comprises the following steps:
using trisection points A and B of the lower edge of an outer frame of the vehicle as judgment points of a lane where the vehicle is located, and judging whether the point A and the point B are in a polygon of a lane area by adopting a ray method; if the two points A and B are in the same lane area, the vehicle is considered to be in the lane;
(4.3) for the vehicles with lane change, performing intersection calculation by using the edge of the lower edge of the outer frame of the vehicle and a lane line to obtain intersection point coordinates on the lane line, and taking the point as a lane change position point;
(5) and carrying out clustering analysis on the lane changing positions of the vehicles passing through in different time periods to obtain the lane changing hot spot areas of the vehicles on the expressway in different time periods.
2. The method for analyzing the lane change area of the highway vehicle based on the deep learning of claim 1, wherein the method comprises the following steps: the step (1) comprises the following substeps:
(1.1) for the monitoring video on the expressway, selecting continuous T frame video pictures, superposing and summing the collected pictures, and then solving an average value as a road background, wherein the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,Pthe picture is a background picture of the road,I i is as followsiThe number of frames of a picture is,Tis a continuous frame number;
and (1.2) loading a road background, drawing a road lane line and lane areas on the road background, and calculating the minimum circumscribed rectangle of all the lane areas as the lane areas.
3. The method for analyzing the lane change area of the highway vehicle based on the deep learning of claim 1, wherein the method comprises the following steps: the step (2) comprises the following substeps:
(2.1) cutting out a lane area image from the video frame image according to the lane area obtained in the step (1);
and (2.2) inputting the lane area image into a trained target detection model based on deep learning, calculating and outputting the outer frame of the vehicle in the picture, wherein the target detection model is EfficientDet, and training by using the manually marked vehicle detection frame data.
4. The method for analyzing the lane change area of the highway vehicle based on the deep learning of claim 1, wherein the method comprises the following steps: in the step (3), the vehicle track in the monitoring video is tracked by adopting an SORT method.
5. The method for analyzing the lane change area of the highway vehicle based on the deep learning of claim 1, wherein the method comprises the following steps: the step (5) comprises the following substeps:
(5.1) dividing each lane line into N equal parts, selecting lane change position data of vehicles in a time period of t1-t2, and counting lane change times in equal segments of each lane;
(5.2) smoothing the number of conversion passes in each lane equal segment by using Gaussian filtering;
and (5.3) constructing a three-dimensional analysis space of the lane change region by using three elements of time, lane change position and lane change number, and performing visual analysis on the lane change region.
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| CN113128382A (en) * | 2021-04-06 | 2021-07-16 | 青岛以萨数据技术有限公司 | Method and system for detecting lane line at traffic intersection |
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