CN111681435B - Traffic control method and device based on edge calculation, electronic equipment and storage medium - Google Patents
Traffic control method and device based on edge calculation, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN111681435B CN111681435B CN202010358960.0A CN202010358960A CN111681435B CN 111681435 B CN111681435 B CN 111681435B CN 202010358960 A CN202010358960 A CN 202010358960A CN 111681435 B CN111681435 B CN 111681435B
- Authority
- CN
- China
- Prior art keywords
- traffic
- human body
- equipment
- target marking
- marking
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
-
- 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
- G08G1/07—Controlling traffic signals
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Multimedia (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Human Computer Interaction (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Biology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The application discloses a traffic control method, a device, electronic equipment and a storage medium based on edge calculation, wherein the traffic control method is used for the edge calculation equipment, and comprises the following steps: identifying the acquired real-time pavement image by using an image identification model to acquire human body characteristics, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics in the image; acquiring geographic coordinates of traffic indicating equipment stored in a cloud; judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking; and if the coordinate distance is smaller than the set threshold, adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment, the human body characteristics and the relation characteristics of the target marking and the human body characteristics. The state of the traffic indicating equipment can be adjusted according to the position relation between the road marking and the pedestrian, and the problem of data communication delay existing in the existing mode is avoided.
Description
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a traffic control method and apparatus based on edge calculation, an electronic device, and a storage medium.
Background
At present, with the development of urbanization, the number of vehicles in cities is increased sharply, and the role of a signal lamp system in traffic control is increasingly highlighted. The existing signal lamp system realizes real-time data acquisition through the front end, and data are uploaded to the cloud end to realize calculation on the cloud end.
In the prior art, signal lamp control modes include two modes, one mode is to set the switching of red, yellow and green signal lamps according to historical traffic flow statistical data; the other type is that the signal lamp system is controlled by the cloud, real-time road condition data are collected by the front end of the signal lamp system and uploaded to the cloud, data calculation operation is achieved at the cloud, and then the data are fed back to the signal lamp system according to a calculation result to control signals for controlling switching of red, yellow and green signal lamps.
For the first mode, the parking time of red, yellow and green signal lamps cannot be flexibly adjusted according to real-time data of traffic flow, and the pedestrian traffic and the vehicle traffic are very inconvenient; for the second mode, although switching of red, yellow and green signal lamps in the signal lamp system can be controlled according to real-time road condition data, for traffic control, data communication delay of almost zero is required, and obviously, the cloud control mode cannot meet the requirement of zero delay.
Disclosure of Invention
The embodiment of the invention provides a traffic control method, a traffic control device, electronic equipment and a storage medium based on edge calculation, which are used for solving the defect that the traffic control mode in the prior art cannot flexibly control the traffic of vehicles and pedestrians according to real-time road condition data.
In a first aspect, an embodiment of the present invention provides a traffic control method based on edge calculation, where the traffic control method is used for an edge calculation device, and the traffic control method includes:
identifying the acquired real-time pavement image by using an image identification model to acquire human body characteristics, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics in the image;
acquiring geographic coordinates of traffic indicating equipment stored in a cloud;
judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking;
and if the coordinate distance is smaller than the set threshold value, acquiring the state data of the traffic indicating equipment, and adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment, the human body characteristics and the relation characteristics of the target marking and the human body characteristics.
Based on any embodiment of the invention, the method further comprises the following steps:
through training, generating an image recognition model, wherein the image recognition model is used for recognizing a road surface image and acquiring human body features in the road surface image, geographical coordinates of the marking and relation features of the marking and the human body features; the geographic coordinates of the marked lines are determined based on at least one of road identification, path relation and associated marks contained in the images.
Based on any embodiment of the present invention, the generating an image recognition model through training specifically includes: acquiring a sample data set, wherein the sample data set comprises a plurality of road surface images, and each road surface image corresponds to a human body characteristic label, a geographic coordinate label and a relation characteristic label of a marking line and a human body characteristic;
dividing the sample data set into a training data set and a testing data set;
inputting the training data set as an input parameter into a neural network model for learning, and taking a model obtained through repeated learning operation as an initial image recognition model;
and inputting the test data set into an initial image recognition model, finishing model training when the matching accuracy of the human body characteristics, the geographic coordinate marks and the relation signs of the marked lines and the human body characteristics of the road surface image recognized by the image recognition model is larger than a preset value, and determining the initial image recognition model as the image recognition model.
Based on any embodiment of the present invention, the relationship characteristic between the target reticle and the human body characteristic specifically includes at least one of the following relationship characteristics: the method comprises the following steps of enabling the target marking to be free of human body features in the marking range of the target marking, enabling the human body features to be in the marking range of the target marking, and enabling the number of the human body features in the marking range of the target marking to be large.
Based on any embodiment of the present invention, the adjusting the state of the traffic indicating device according to the state data of the traffic indicating device, the human body characteristics, and the relationship characteristics between the target marking and the human body characteristics specifically includes:
if the relation characteristics of the target marking and the human body characteristics comprise that the human body characteristics exist in the marking range of the target marking, the state of the traffic indicating equipment is adjusted based on the number of the human body characteristics in the marking range of the target marking and according to the state data of the traffic indicating equipment;
and if the relation characteristics of the target marking and the human body characteristics comprise that no human body characteristics exist in the marking range of the target marking, keeping the state of the traffic indicating equipment.
Based on any embodiment of the present invention, after determining whether the coordinate distance between the geographic coordinate of the traffic indication device and the geographic coordinate of the target marking is smaller than the set threshold based on the geographic coordinate of the target marking, the method further includes:
And if the coordinate distance is greater than a set threshold value, acquiring the driving data of the traffic equipment in the preset area range of the target marking, and generating a control signal for the traffic equipment according to the driving data of the traffic equipment.
According to any embodiment of the invention, the driving data of the traffic equipment comprises: the driving geographic coordinates of the traffic equipment, the driving speed of the traffic equipment and the driving state of the traffic equipment;
the generating of the control signal to the traffic equipment according to the driving data of the traffic equipment comprises:
calculating the distance value from the running geographic coordinate of the traffic equipment to the target marking and the estimated time from the traffic equipment to the target marking based on the running geographic coordinate of the traffic equipment, the running speed of the traffic equipment and the running state of the traffic equipment;
and generating a control signal for the traffic equipment if the distance is less than a set threshold value or the estimated time is less than a time threshold value according to the distance and the estimated time.
In a second embodiment of the present invention, there is provided an edge calculation-based traffic control apparatus, where the traffic control apparatus is used for an edge calculation device, and the traffic control apparatus includes:
the identification module is used for identifying the acquired real-time pavement image by using the image identification model so as to acquire human body characteristics, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics;
The acquisition module is used for acquiring the geographic coordinates of the traffic indicating equipment stored in the cloud;
the processing module is used for judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking, and if so, acquiring the state data of the traffic indicating equipment;
and the control module is used for acquiring the state data of the traffic indicating equipment when the coordinate distance is smaller than a set threshold value, and adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment, the human body characteristics and the relation characteristics of the target marking and the human body characteristics.
In an embodiment of the third aspect of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the traffic control method based on edge calculation according to the embodiment of the first aspect of the present invention.
A fourth aspect of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the traffic control method based on edge calculation according to the first aspect of the present invention.
According to the traffic control method and device based on edge calculation, the electronic device and the storage medium, the real-time pavement image is identified through the image identification model, the geographic coordinates of the target marking can be obtained, whether the traffic indicating device exists at the target marking is judged according to the geographic coordinate information of the target marking, and for the traffic indicating device, the state of the traffic indicating device is adjusted according to the state data of the traffic indicating device and the relation characteristics of the target marking and the human body characteristics; and if no traffic indicating equipment exists, acquiring the driving data of the traffic equipment within the preset area range of the target marking, and generating a control signal for the traffic equipment according to the driving data of the traffic equipment. According to the invention, based on the edge calculation, the above traffic control method is executed by the edge calculation equipment, so that the state adjustment of the traffic indication equipment and the driving state adjustment of the traffic equipment can be controlled according to the position relation between the road marked line and the pedestrian, the problem of data communication delay existing in the conventional traffic control realized in a cloud mode is solved, meanwhile, the problem that the signal lamp system cannot be flexibly controlled according to the real-time state of the pedestrian and the real-time state of the vehicle is solved, the traffic is convenient to pass, and the traffic passing safety is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a traffic control method based on edge calculation according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a traffic control method based on edge calculation according to another embodiment of the present invention;
fig. 3 is a schematic flow chart of a traffic control method based on edge calculation according to another embodiment of the present invention;
FIG. 4 is a schematic diagram of a traffic control device based on edge calculation according to an embodiment of the present invention;
fig. 5 illustrates a physical structure diagram of an electronic device.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a traffic control method based on edge calculation according to an embodiment of the present invention, where the traffic control method is used in an edge calculation device. The edge computing of the embodiment of the invention refers to an open platform which integrates network, computing, storage and application core capabilities at the edge side of a network close to an object or a data source and provides edge intelligent service nearby; an edge computing device refers to a device that is capable of providing edge computing services at the edge side of a network near the source of the object or data. As shown in fig. 1, a traffic control method based on edge calculation according to an embodiment of the present invention includes:
specifically, the image recognition model is a model for recognizing a road surface image, and is generated through training, so that whether the road surface image acquired by the acquisition equipment includes human body features or not and the number of the human body features can be recognized; and whether the road surface image comprises the target marking, the geographic coordinates of the target marking and the relation characteristics of the target marking and the human body characteristics can be identified. The target reticle may be: zebra crossings, stop lines, giving-up signs, etc.
In one example, the relationship between the target reticle and the human body feature specifically includes at least one of the following relationship: the number of the human body features does not exist in the range of the marked line of the target marked line, the human body features exist in the range of the marked line of the target marked line, and the human body features exist in the range of the marked line of the target marked line.
102, acquiring geographic coordinates of traffic indicating equipment stored in a cloud;
specifically, the cloud stores geographic coordinate data of all traffic indication devices, and the data is generated when the traffic indication devices are arranged and stored in the cloud. The edge computing device can acquire the geographic coordinates of the traffic indicating device from the cloud end through a mobile communication network; in order to improve the communication efficiency, the geographic coordinates of the traffic indicating device within the preset radius range may be acquired based on the geographic coordinates of the target marking acquired in step 102, with the geographic coordinates of the target marking as a center.
In an embodiment of the invention, the geographical coordinates of the traffic indicating device may be represented in terms of longitude and latitude coordinate values, and may also be represented in the form of, for example, "road-building" based on geographical location. Likewise, the geographic coordinates of the target reticle may also be based on geography as represented by ". road.. cell".
103, judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking;
specifically, the edge computing device compares the acquired geographic coordinates of all the traffic indicating devices with the geographic coordinates of the target marking one by one, calculates the coordinate distance between the geographic coordinates of the traffic indicating devices and the geographic coordinates of the target marking, determines the size relation between the coordinate distance and a set threshold value, and acquires the state data of the traffic indicating devices for the traffic indicating devices with the coordinate distances smaller than the set threshold value.
In a specific implementation, the coordinate distance may be a difference value between a target marking line and a traffic indication device, the target marking line and a geographic coordinate of the traffic indication device may be marked on a map respectively, and then a linear distance is calculated by a distance measurement tool in the map, and the linear distance is used as the coordinate distance between the target marking line and the traffic indication device. The calculation method and the representation method of the coordinate distance are not particularly limited in the embodiments of the present invention, and any other methods that can be determined by those skilled in the art may be used in the embodiments of the present invention.
The method comprises the steps that whether traffic indication equipment exists at the position of a target marking line can be determined by setting a threshold value, the traffic indication equipment is arranged at the geographical position where the target marking line such as a zebra crossing is arranged, the arranged traffic indication equipment can be arranged at the position close to the zebra crossing, and if the coordinate distance between the geographical coordinates of the traffic indication equipment and the geographical coordinates of the target marking line is larger than the set threshold value, the fact that the traffic indication equipment is not arranged at the geographical position of the target marking line can be determined. The threshold may be determined according to the layout rules of the traffic indicating devices of each city.
And step 104, if the coordinate distance is smaller than a set threshold value, acquiring state data of the traffic indicating equipment, and adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment, the human body characteristics and the relation characteristics of the target marked line and the human body characteristics.
Specifically, the edge computing device can acquire the geographic coordinates of the target marking line under the real-time road condition by using the image recognition model, then acquire the state data of the traffic indicating device laid by the target marking line correspondingly, and then adjust the state of the traffic indicating device based on the state data of the traffic indicating device, the human body characteristics and the relation characteristics of the target marking line and the human body characteristics.
In one example, the status data of the traffic indicating device includes an indication status of the traffic indicating device, a status change rule of the traffic indicating device, and indication status runtime data of the traffic indicating device. For example, the traffic indicating device may be a traffic signal light, which includes three indication states of red, yellow and green, the red light is a no-go light, the green light is a go-ahead light, and the yellow light is a warning light, or a "transition light"; the three state change rules indicating the states are: the method comprises the following steps of (1) setting state setting time for each indication state, namely setting the red light state for 60s, setting the yellow light state for 3s and setting the green light state for 30 s; if the indication state of the traffic signal lamp is a red light state, the set time of the red light state is 60s, and the running time is 30s, the switching time for switching the red light state to a green light state can be determined to be 30 s.
Adjusting the state of the traffic indicating device includes: adjusting the indication state of the traffic light, such as adjusting the green light state to the red light state; adjusting the set time of a traffic signal lamp, for example, adjusting the set time of a red light state to be 30s to be 60 s; the switching time of the traffic signal light is adjusted, for example, the switching time for switching the determined red light state to the green light state is 30s, and the switching time 30s is adjusted to 45s, and the like. Here, only the centralized adjustment example is given, and in a specific implementation, other adjustment states may be added, and multiple states may be selected to be adjusted simultaneously.
In the embodiment of the invention, the image recognition model is used for recognizing the real-time road surface image, so that the geographic coordinates of the target marking can be obtained, whether the traffic indicating equipment exists at the position of the target marking is judged according to the geographic coordinate information of the target marking, and if the traffic indicating equipment exists, the state of the traffic indicating equipment is adjusted according to the state data of the traffic indicating equipment and the relation characteristic between the target marking and the human body characteristic. In the embodiment of the invention, based on the edge calculation, the traffic control method is executed by the edge calculation device, so that the state adjustment of the traffic indication device and the driving state adjustment of the traffic device can be controlled according to the position relation between the road marked lines and the pedestrians, and the problem of data communication delay in the conventional traffic control realized in a cloud manner is solved.
Based on the foregoing embodiment, a schematic flow chart of a traffic control method based on edge calculation according to another embodiment of the present invention is shown in fig. 2, where the traffic control method based on edge calculation according to another embodiment of the present invention includes:
200, generating an image recognition model through training, wherein the image recognition model is used for recognizing the road surface image and acquiring the human body characteristics in the road surface image, the geographic coordinates of the marking line and the relation characteristics of the marking line and the human body characteristics; the geographic coordinates of the marked lines are determined based on at least one of road identification, road relation and associated marks contained in the images;
Specifically, the image recognition model is generated by repeatedly learning the road surface image in the sample data set based on a machine learning algorithm. The generated image recognition model can recognize human body characteristics, various traffic marking lines, road marks, road relations, road indicating signs, associated signs (such as some symbolic buildings), position relations between human bodies and the traffic marking lines and the like which are included in the road surface image; in order to improve the accuracy of model identification, the acquired road surface images in the sample data set should include a certain number of: the road surface image includes an image of a human body feature, the road surface image includes images of various traffic markings, the road surface image includes an image of one or more markings in relation to the human body feature, the road surface image includes an image of a road indicator, and the like.
In one example, the relationship between the reticle and the human body feature specifically includes at least one of the following relationship: the number of the human body features in the scale line range of the scale line, the human body features in the scale line range of the scale line and the human body features in the scale line range of the scale line.
and 204, if the coordinate distance is smaller than a set threshold value, acquiring the state data of the traffic indicating equipment, and adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment, the human body characteristics and the relation characteristics of the target marking and the human body characteristics.
In one example, the training of step 200 generates an image recognition model, which specifically includes:
(1-1) acquiring a sample data set, wherein the sample data set comprises a plurality of road surface images, and each road surface image corresponds to a human body characteristic label, a geographic coordinate label and a relation characteristic label of a marked line and a human body characteristic;
specifically, the road surface image collected by the collection device can be acquired from the cloud, and the collection device can comprise cameras arranged at different geographic positions, cameras installed in the vehicle and other devices. And adding labels to the acquired road surface image according to the content in the image, wherein the labels can comprise human body feature labels, geographic coordinate labels and relation feature labels of the marked lines and the human body features, and the label adding can be completed through a labeling tool. For example, if the road surface image includes pedestrians, a human body feature label is added to the road surface image; and if the road surface image comprises the zebra stripes and the zebra stripes comprise pedestrians, adding a label with human body characteristics in the marked line range of the marked line to the road surface image.
(1-2) dividing the sample data set into a training data set and a testing data set;
specifically, some road surface images are randomly selected from the acquired sample data set to serve as a test data set, and the rest of the sample data sets serve as training data sets.
(1-3) inputting the training data set serving as an input parameter into a neural network model for learning, and taking a model obtained through repeated learning operation as an initial image recognition model;
(1-4) inputting the test data set into an initial image recognition model, and when the matching accuracy of the human body characteristics, the geographic coordinate marks and the relation signs of the marked lines and the human body characteristics of the road surface image recognized by the image recognition model is larger than a preset value, finishing model training and determining the initial image recognition model as the image recognition model.
Specifically, initializing a neural network model, inputting a road surface image in a training data set into the neural network model as an input parameter, comparing an output parameter of the neural network model with a label of the output road surface image, adjusting a weight parameter in the neural network model based on a comparison result, and taking the neural network model obtained through repeated learning operation as an initial image recognition model; and then inputting the road surface image in the test data set into an initial image recognition model, finishing model training when the matching accuracy of the output parameter of the initial image recognition model and the label value corresponding to the input road surface image is greater than a preset value, and determining the initial image recognition model as the image recognition model. In the embodiment of the present invention, the termination condition for terminating the model training may be set to be that the matching accuracy of the model output is greater than a predetermined value, and the termination condition for terminating the model training may also be set to be that the number of iterations of the model is greater than a predetermined value.
In one example, the adjusting the state of the traffic indicating device according to the state data of the traffic indicating device, the human body characteristics and the relationship characteristics between the target marking and the human body characteristics in step 204 specifically includes:
if the relation characteristics of the target marking and the human body characteristics comprise that the human body characteristics exist in the marking range of the target marking, the state of the traffic indicating equipment is adjusted based on the number of the human body characteristics in the marking range of the target marking and according to the state data of the traffic indicating equipment;
and if the relation characteristics of the target marking and the human body characteristics comprise that no human body characteristics exist in the marking range of the target marking, keeping the state of the traffic indicating equipment.
Specifically, the status data of the traffic indicating device includes an indication status of the traffic indicating device, a status change rule of the traffic indicating device, and indication status runtime data of the traffic indicating device. For example, the traffic indicating device may be a traffic signal light, which includes three indication states of red, yellow and green, the red light is a no-go light, the green light is a go-ahead light, and the yellow light is a warning light, or a "transition light"; the three state change rules indicating the states are: the method comprises the following steps of (1) setting state setting time for each indication state, namely setting the red light state for 60s, setting the yellow light state for 3s and setting the green light state for 30 s; if the indication state of the traffic light is a red light state, the set time of the red light state is 60s, and the running time is 30s, it is determined that the switching time for switching the red light state to a green light state is 30 s.
The edge computing equipment adjusts the state of the traffic indicating equipment according to the acquired state data of the traffic indicating equipment and the relation characteristics of the target marking and the human body characteristics identified by the image identification model, and if the relation characteristics of the target marking and the human body characteristics comprise the human body characteristics existing in the marking range of the target marking, the number of the human body characteristics in the marking range of the target marking is based on the state data of the traffic indicating equipment; and if the relation characteristics of the target marking and the human body characteristics comprise that no human body characteristics exist in the marking range of the target marking, keeping the state of the traffic indicating equipment. For example, the relationship characteristic between the target reticle and the human body characteristic identified by the image identification model is that the human body characteristic exists in the reticle range of the target reticle, and the number of the human body characteristics is M (setting the state adjustment threshold of the traffic indication device to be M, for example, adjusting the state of a green light to be a red light state), the state of the traffic indication device is the green light state, the setting time of the green light state is 60s, and the running time of the green light state is 30 s. For another example, the characteristic of the relationship between the target marking and the human body feature identified by the image identification model is that no human body feature exists in the marking range of the target marking, the state of the traffic indicating device is a green light state, the set time of the green light state is 60s, and the operating time of the green light state is 30 s.
The traffic control method, the traffic control device, the electronic equipment and the storage medium based on the edge calculation provided by the embodiment of the invention can identify a real-time road surface image through the image identification model, can acquire the geographic coordinates of a target marking, then judge whether a traffic indicating device exists at the target marking according to the geographic coordinate information of the target marking, and adjust the state of the traffic indicating device according to the state data of the traffic indicating device and the relation characteristic of the target marking and the human body characteristic if the traffic indicating device exists. According to the invention, based on the edge calculation, the above traffic control method is executed by the edge calculation equipment, so that the state adjustment of the traffic indication equipment and the driving state adjustment of the traffic equipment can be controlled according to the position relation between the road marked line and the pedestrian, the problem of data communication delay existing in the conventional traffic control realized in a cloud mode is solved, meanwhile, the problem that the signal lamp system cannot be flexibly controlled according to the real-time state of the pedestrian and the real-time state of the vehicle is solved, the traffic is convenient to pass, and the traffic passing safety is improved.
Based on the foregoing embodiment, a schematic flow chart of a traffic control method based on edge calculation according to another embodiment of the present invention is shown in fig. 3, and a traffic control method based on edge calculation according to another embodiment of the present invention includes:
and 305, if the coordinate distance is larger than a set threshold, acquiring the driving data of the traffic equipment in the preset area range of the target marking, and generating a control signal for the traffic equipment according to the driving data of the traffic equipment.
Specifically, in step 303, the edge computing device compares the acquired geographic coordinates of all the traffic indicating devices with the geographic coordinates of the target marking one by one, calculates the coordinate distance between the geographic coordinates of the traffic indicating devices and the geographic coordinates of the target marking, and determines the size relationship between the coordinate distance and a set threshold value, and for a traffic indicating device whose coordinate distance is greater than the set threshold value, it is determined that no traffic indicating device exists at the target marking, and the traffic indicating device cannot be used to perform traffic control on the target marking, and at this time, the edge computing device may acquire the driving data of the traffic devices within the preset area range of the target marking. The preset area range can be set as a first distance value in a first direction, the first direction is a passable direction of the traffic equipment at the coordinate position of the target marking, and the first distance value can be set as 0.5km or other distance values.
The traffic equipment is intelligent traffic equipment which has environment perception capability and can realize environment data acquisition. The traffic equipment of the embodiment of the invention can monitor environmental data in the driving process, such as a shooting image (including two environments in the equipment space and out of the equipment space) shot by a camera, position data acquired by a GPS system, equipment state data monitored by a central control system and the like. The traffic device uploads the traffic data to the edge computing device via the edge device.
In one example, the travel data of the transportation device includes: the travel geographical coordinates of the transportation device, the travel speed of the transportation device, the travel state (including a travel state, a stop state) of the transportation device, and the like. Specifically, in step 305, generating a control signal for the traffic equipment according to the driving data of the traffic equipment specifically includes:
calculating a distance value between the running geographic coordinates of the traffic equipment and a target marking and an estimated time of the traffic equipment running to the target marking based on the running geographic coordinates of the traffic equipment, the running speed of the traffic equipment and the running state of the traffic equipment;
and according to the distance and the estimated time, if the distance is less than the distance threshold or the estimated time is less than the time threshold, generating a control signal for the traffic equipment.
The distance between the traffic equipment and the target marking can be determined based on the running geographic coordinates of the traffic equipment, and the specific distance calculation mode can refer to the calculation of the coordinate distance between the target marking and the traffic indicating equipment. For example, when the geographical coordinates of the zebra crossing determined by the image recognition model are the position a, the geographical coordinates of the traffic equipment for traveling are the position B, the traffic equipment travels from the position B to the position a of the zebra crossing along the direction C, and the distance between the traffic equipment and the target marked line is determined, the straight-line distance value between the position B and the position a along the direction C should be calculated. The estimated time of the traffic equipment driving to the target marking can be determined by combining the driving speed calculation of the traffic equipment according to the calculated distance value, if the current driving state of the traffic equipment is a stop state, the historical driving speed average value of the traffic equipment is calculated, and then the estimated time of the traffic equipment driving to the target marking is calculated based on the speed average value.
The control signal of the traffic equipment can be generated by calculating the distance value from the traffic equipment to the target marked line in the preset area range of the target marked line and the estimated time for driving to the target marked line. Specifically, the control signal of the traffic device may be to send a prompt signal to the traffic device whose distance value is less than the distance threshold or whose estimated time is less than the time threshold, so as to prompt the pedestrian information at the position of the target marking; for example, when the distance value is smaller than the distance threshold, the edge computing device generates prompt information containing the number of the human body features according to the relationship between the human body features identified by the image identification model and the target marked line and the human body features and sends the prompt information to the traffic device when the human body features exist.
According to the traffic control method and device based on edge calculation, the electronic device and the storage medium, the real-time pavement image is identified through the image identification model, the geographic coordinates of the target marking can be obtained, whether the traffic indicating device exists at the target marking is judged according to the geographic coordinate information of the target marking, and for the traffic indicating device, the state of the traffic indicating device is adjusted according to the state data of the traffic indicating device and the relation characteristics of the target marking and the human body characteristics; and if no traffic indicating equipment exists, acquiring the driving data of the traffic equipment within the preset area range of the target marking line, and generating a control signal for the traffic equipment according to the driving data of the traffic equipment. According to the invention, based on the edge calculation, the above traffic control method is executed by the edge calculation equipment, so that the state adjustment of the traffic indication equipment and the driving state adjustment of the traffic equipment can be controlled according to the position relation between the road marked line and the pedestrian, the problem of data communication delay existing in the conventional traffic control realized in a cloud mode is solved, meanwhile, the problem that the signal lamp system cannot be flexibly controlled according to the real-time state of the pedestrian and the real-time state of the vehicle is solved, the traffic is convenient to pass, and the traffic passing safety is improved.
Based on any of the above embodiments, fig. 4 is a schematic diagram of a traffic control device based on edge calculation according to an embodiment of the present invention, as shown in fig. 4, the traffic control device provided in an embodiment of the present invention is used in an edge calculation device, and the traffic control device includes:
the identification module 401 is configured to identify the acquired real-time road surface image by using an image identification model to acquire a human body feature, a geographic coordinate of a target marking, and a relationship feature between the target marking and the human body feature;
an obtaining module 402, configured to obtain geographic coordinates of the traffic indication device stored in the cloud;
the processing module 403 is configured to determine, based on the geographic coordinate of the target marking, whether a coordinate distance between the geographic coordinate of the traffic indication device and the geographic coordinate of the target marking is smaller than a set threshold, and if so, obtain status data of the traffic indication device;
the control module 404 is configured to obtain state data of the traffic indication device when the coordinate distance is smaller than a set threshold, and adjust the state of the traffic indication device according to the state data of the traffic indication device, the human body characteristics, and the relationship characteristics between the target marking and the human body characteristics.
The traffic control device provided by the embodiment of the invention can acquire the geographic coordinates of the target marking by identifying the real-time pavement image by using the image identification model, then judge whether the traffic indicating equipment exists at the target marking according to the geographic coordinate information of the target marking, and adjust the state of the traffic indicating equipment according to the state data of the traffic indicating equipment and the relation characteristic between the target marking and the human body characteristic if the traffic indicating equipment exists. Based on the edge calculation, the traffic control method is executed through the edge calculation device, so that the state adjustment of the traffic indication device and the driving state adjustment of the traffic device can be controlled according to the position relation between the road marked lines and the pedestrians, and the problem of data communication delay existing in the conventional traffic control through a cloud mode is solved.
Fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor)510, a communication Interface (Communications Interface)520, a memory (memory)530 and a communication bus 540, wherein the processor 510, the communication Interface 520 and the memory 530 communicate with each other via the communication bus 540. Processor 510 may call logic instructions in memory 530 to perform the following method: identifying the acquired real-time pavement image by using an image identification model so as to acquire human body characteristics in the image, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics; acquiring geographic coordinates of traffic indicating equipment stored in a cloud terminal; judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking, and if so, acquiring state data of the traffic indicating equipment; and adjusting the state of the traffic indicating equipment according to the state data and the human body characteristics of the traffic indicating equipment and the relation characteristics of the target marking and the human body characteristics.
It should be noted that, when being implemented specifically, the electronic device in this embodiment may be a server, a PC, or other devices, as long as the structure includes the processor 510, the communication interface 520, the memory 530, and the communication bus 540 shown in fig. 5, where the processor 510, the communication interface 520, and the memory 530 complete mutual communication through the communication bus 540, and the processor 510 may call the logic instructions in the memory 530 to execute the above method. The embodiment does not limit the specific implementation form of the electronic device.
In addition, the logic instructions in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
Further, embodiments of the present invention disclose a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, comprising: identifying the acquired real-time pavement image by using an image identification model to acquire human body characteristics, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics in the image; acquiring geographic coordinates of traffic indicating equipment stored in a cloud; judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking, and if so, acquiring state data of the traffic indicating equipment; and adjusting the state of the traffic indicating equipment according to the state data and the human body characteristics of the traffic indicating equipment and the relation characteristics of the target marking and the human body characteristics.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to perform the method provided by the foregoing embodiments, for example, including: identifying the acquired real-time pavement image by using an image identification model so as to acquire human body characteristics in the image, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics; acquiring geographic coordinates of traffic indicating equipment stored in a cloud; judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking, and if so, acquiring state data of the traffic indicating equipment; and adjusting the state of the traffic indicating equipment according to the state data and the human body characteristics of the traffic indicating equipment and the relation characteristics of the target marking and the human body characteristics.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium that stores computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in purely computer readable program code means, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be regarded as a hardware component and the means for performing the various functions included therein may also be regarded as structures within the hardware component. Or even means for performing the functions may be conceived to be both a software module implementing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in a plurality of software and/or hardware when implementing the invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (8)
1. An edge-computing-based traffic control method for an edge computing device, the traffic control method comprising:
identifying the acquired real-time pavement image by using an image identification model so as to acquire human body characteristics in the image, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics;
acquiring geographic coordinates of traffic indicating equipment stored in a cloud terminal;
Judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking;
if the coordinate distance is smaller than a set threshold value, acquiring state data of the traffic indicating equipment, and adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment, the human body characteristics and the relation characteristics of the target marking and the human body characteristics;
after judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking, the method further comprises the following steps:
if the coordinate distance is larger than a set threshold value, acquiring the driving data of the traffic equipment in the preset area range of the target marking, and generating a control signal for the traffic equipment according to the driving data of the traffic equipment;
the driving data is driving data acquired by the traffic equipment and received by the edge computing equipment;
the relation feature body of the target marking line and the human body feature comprises at least one of the following relation features: the number of the human body features in the marking range of the target marking, the human body features in the marking range of the target marking and the human body features in the marking range of the target marking are not included.
2. The edge-computing-based traffic control method of claim 1, further comprising:
through training, generating the image recognition model, wherein the image recognition model is used for recognizing the road surface image and acquiring the human body characteristics in the road surface image, the geographic coordinates of the marking line and the relation characteristics of the marking line and the human body characteristics; and determining the geographic coordinates of the marked lines based on at least one of road identification, path relation and associated marks contained in the image.
3. The traffic control method based on edge calculation according to claim 2, wherein the generating the image recognition model through training specifically comprises:
acquiring a sample data set, wherein the sample data set comprises a plurality of road surface images, and each road surface image corresponds to a human body characteristic label, a geographic coordinate label and a relation characteristic label of a marking line and a human body characteristic;
dividing the sample data set into a training data set and a testing data set;
inputting the training data set as an input parameter into a neural network model for learning, and taking a model obtained through repeated learning operation as an initial image recognition model;
Inputting the test data set into the initial image recognition model, finishing model training when the matching accuracy of the human body characteristics, the geographic coordinates, the relational signs of the marking lines and the human body characteristics of the road surface images in the test data set recognized by the initial image recognition model and the labels of the road surface images is larger than a preset value, and determining the initial image recognition model as the image recognition model.
4. The traffic control method based on edge calculation according to claim 1, wherein the adjusting the state of the traffic indicating device according to the state data of the traffic indicating device, the human body feature and the relationship feature between the target marking and the human body feature specifically comprises:
if the relation characteristics of the target marking and the human body characteristics comprise that the human body characteristics exist in the marking range of the target marking, adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment on the basis of the number of the human body characteristics in the marking range of the target marking;
and if the relation characteristics of the target marking and the human body characteristics comprise that no human body characteristics exist in the marking range of the target marking, keeping the state of the traffic indicating equipment.
5. The edge-computing-based traffic control method of claim 1, wherein the travel data of the traffic device comprises: the driving geographic coordinates of the traffic equipment, the driving speed of the traffic equipment and the driving state of the traffic equipment;
the generating of the control signal to the traffic equipment according to the driving data of the traffic equipment comprises:
calculating a distance value between the running geographic coordinate of the traffic equipment and the target marking and an estimated time of the traffic equipment running to the target marking based on the running geographic coordinate of the traffic equipment, the running speed of the traffic equipment and the running state of the traffic equipment;
and generating a control signal for the traffic equipment according to the distance and the estimated time if the distance is less than a distance threshold or the estimated time is less than a time threshold.
6. An edge-computation-based traffic control apparatus for an edge-computation-device, the traffic control apparatus comprising:
the identification module is used for identifying the acquired real-time pavement image by using the image identification model so as to acquire human body characteristics, geographic coordinates of the target marking and relation characteristics of the target marking and the human body characteristics;
The acquisition module is used for acquiring the geographic coordinates of the traffic indicating equipment stored in the cloud;
the processing module is used for judging whether the coordinate distance between the geographic coordinate of the traffic indicating equipment and the geographic coordinate of the target marking is smaller than a set threshold value or not based on the geographic coordinate of the target marking;
the control module is used for acquiring the state data of the traffic indicating equipment when the coordinate distance is smaller than a set threshold value, and adjusting the state of the traffic indicating equipment according to the state data of the traffic indicating equipment, the human body characteristics and the relation characteristics of the target marking and the human body characteristics;
the control module is further used for acquiring the driving data of the traffic equipment in the preset area range of the target marking when the coordinate distance is larger than a set threshold value, and generating a control signal for the traffic equipment according to the driving data of the traffic equipment;
the driving data is driving data acquired by the traffic equipment and received by the edge computing equipment;
the relation feature body of the target marking line and the human body feature comprises at least one of the following relation features: the number of the human body features in the marking range of the target marking, the human body features in the marking range of the target marking and the human body features in the marking range of the target marking are not included.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the edge computing-based traffic control method according to any one of claims 1 to 5.
8. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the edge-computation-based traffic control method according to any one of claims 1 to 5.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2020102313190 | 2020-03-27 | ||
| CN202010231319 | 2020-03-27 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111681435A CN111681435A (en) | 2020-09-18 |
| CN111681435B true CN111681435B (en) | 2022-06-28 |
Family
ID=72433980
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010358960.0A Active CN111681435B (en) | 2020-03-27 | 2020-04-29 | Traffic control method and device based on edge calculation, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111681435B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117173896B (en) * | 2023-11-02 | 2024-01-16 | 深圳市地铁集团有限公司 | Visual traffic control method based on ARM technology system |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104299426B (en) * | 2014-09-19 | 2016-05-11 | 辽宁天久信息科技产业有限公司 | A kind of traffic signal control system and method based on to pedestrian detection counting statistics |
| US10099637B1 (en) * | 2017-10-09 | 2018-10-16 | Denso International America, Inc. | System and method for dynamic vehicle customization and tuning |
| CN108492601A (en) * | 2018-04-13 | 2018-09-04 | 济南浪潮高新科技投资发展有限公司 | A kind of vehicle DAS (Driver Assistant System) and method |
| CN109598951A (en) * | 2019-01-03 | 2019-04-09 | 京东方科技集团股份有限公司 | A kind of control system and control method of traffic lights |
| CN110118661B (en) * | 2019-05-09 | 2024-03-26 | 腾讯科技（深圳）有限公司 | Method and device for processing driving simulation scene and storage medium |
| CN110276964B (en) * | 2019-05-27 | 2020-11-13 | 八维通科技有限公司 | Method for intelligently adjusting time of traffic light |
| CN110415541A (en) * | 2019-07-19 | 2019-11-05 | 重庆长安汽车股份有限公司 | A kind of crossing prevailing state reminding method and system |
| CN110689747B (en) * | 2019-10-12 | 2021-04-06 | 北京百度网讯科技有限公司 | Control method and device of automatic driving vehicle and automatic driving vehicle |
| CN110895884B (en) * | 2019-11-18 | 2023-05-23 | 腾讯科技（深圳）有限公司 | Traffic signal lamp control method and device |
-
2020
- 2020-04-29 CN CN202010358960.0A patent/CN111681435B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 计算机视觉算法与智能车应用;刘宏哲 等;《计算机视觉算法与智能车应用》;电子工业出版社;20151231;第1-6页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111681435A (en) | 2020-09-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110674723B (en) | Method and device for determining driving track of unmanned vehicle | |
| CN111680362B (en) | Automatic driving simulation scene acquisition method, device, equipment and storage medium | |
| CN111582189B (en) | Traffic signal lamp identification method and device, vehicle-mounted control terminal and motor vehicle | |
| CN108053690B (en) | Method for identifying driving lane, curve early warning method, device, medium and equipment | |
| CN112309233B (en) | Road boundary determining and road segmenting method and device | |
| CN110530398B (en) | Method and device for detecting precision of electronic map | |
| CN111413973A (en) | Lane change decision method and device for vehicle, electronic equipment and storage medium | |
| CN112327864A (en) | Control method and control device of unmanned equipment | |
| CN111912423B (en) | Method and device for predicting obstacle trajectory and training model | |
| CN112180923A (en) | Automatic driving method, intelligent control equipment and automatic driving vehicle | |
| CN112614365A (en) | Electronic map processing method and device | |
| WO2019239477A1 (en) | Map generation device and map generation system | |
| CN111681435B (en) | Traffic control method and device based on edge calculation, electronic equipment and storage medium | |
| CN113012261B (en) | Method and device for automatically constructing map | |
| CN114485706A (en) | Route planning method and device, storage medium and electronic equipment | |
| US11694544B2 (en) | Traffic safety control method and vehicle-mounted device | |
| CN112837555A (en) | Test route selection method and device, computer equipment and storage medium | |
| CN112883871A (en) | Model training and unmanned vehicle motion strategy determining method and device | |
| JP2019164602A (en) | Reverse drive warning system, reverse drive warning method, and reverse drive warning program | |
| CN113945222A (en) | Method and device for identifying road information, electronic equipment, vehicle and medium | |
| KR20120071750A (en) | Electronic apparatus, method and recording medium for providing lane-changing information | |
| CN112684720A (en) | Simulation test method and device | |
| CN111258312A (en) | Movable model, control method, device, system, equipment and storage medium thereof | |
| CN112859884B (en) | Method and device for controlling unmanned equipment | |
| CN115985124B (en) | Vehicle running control method and device, storage medium and electronic device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |