Signal lamp auxiliary method and system based on image depth identification

Abstract

The invention discloses a signal lamp assisting method and system based on image depth recognition. The signal lamp assisting method comprises the steps that an image collecting module collects real-time road and vehicle images; the image processing module obtains real-time road condition information, vehicle information and traffic flow information by analyzing and processing the obtained road image information and combining a database; various kinds of obtained real-time data information are obtained, a suggested vehicle speed planning module is developed according to the various kinds of real-time information, and the optimal suggested vehicle speed of various kinds of vehicles passing through the road section under the safe driving condition is obtained; and various display signals are dynamically output in real time according to the partial road environment information obtained by the image processing module and the vehicle speed information obtained by the suggested vehicle speed planning module. The purpose that the vehicle can pass through the intersection without braking is achieved by prompting the vehicle to run at the optimal suggested speed, the optimal suggested speed of the fast lane and the slow lane is provided by considering the dynamic property difference of different types of vehicles, the intersection passing efficiency is improved, and meanwhile resource waste caused by frequent start and stop is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of smart transportation, and in particular to a signal light auxiliary method and system based on image depth recognition. Background Art

[0002] Many times, when the traffic light changes from red to green, vehicles waiting at the intersection cannot start quickly and pass through the intersection smoothly. In some cases, the vehicle behind misses the green light because the vehicle in front fails to start in time and has to wait for the next pass. This phenomenon has aggravated traffic congestion at the intersection to a certain extent and affected the efficiency of road traffic.

[0003] Traditional traffic light systems play a vital role in managing urban traffic. However, with the acceleration of urbanization and the surge in the number of vehicles, traditional traffic light systems have gradually exposed some problems in dealing with increasingly complex traffic conditions. For example, traffic congestion, energy waste, environmental pollution, etc. Traditional traffic light auxiliary systems mainly rely on sensors and infrared technology, which have certain limitations, such as high cost and susceptibility to environmental influences. In addition, the existing traffic light auxiliary systems still have certain shortcomings in providing traffic light information, such as failing to fully consider factors such as the speed of vehicles near traffic lights. The traffic light auxiliary system based on image depth recognition technology has the advantages of low cost, high accuracy, and strong robustness, and has gradually become the research focus of intelligent transportation systems. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a signal light auxiliary method and system based on image depth recognition to solve the problems of road congestion caused by frequent starting and stopping of vehicles at intersections, low traffic efficiency at intersections, and waste of resources caused by frequent starting and stopping.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a signal light auxiliary method based on image depth recognition, comprising:

[0009] Collect road and vehicle image information in real time, and perform real-time analysis and processing on the road and vehicle image information to obtain various real-time data information for vehicle speed planning;

[0010] According to the various types of real-time data information, the vehicle speed is planned to generate the optimal recommended speed for vehicles in each lane to pass through the current intersection;

[0011] Dynamically display the optimal recommended speed for each lane, current traffic light status, weather information and other real-time road traffic prompt information to assist vehicle driving.

[0012] As a preferred solution of the signal light auxiliary method based on image depth recognition of the present invention, the real-time analysis and processing of the road and vehicle image information includes:

[0013] Preprocess the image to be identified according to the target detection algorithm, extract features from the preprocessed image, and identify the vehicle in the image; identify and classify the identified vehicle, track the position and movement trajectory of the vehicle between different frames through computer vision algorithms, and count the vehicle information and traffic flow information on the road;

[0014] When identifying and classifying vehicles, the vehicle logo and vehicle model are combined to compare and match with the vehicle type images in the database to obtain the vehicle brand and model as well as the vehicle's power performance information.

[0015] As a preferred solution of the signal light auxiliary method based on image depth recognition described in the present invention, it also includes: realizing accurate positioning and color recognition of traffic lights by training deep neural networks, and combining existing signal light facilities at intersections, selecting signal cycle and phase information parameters of the current signal light for vehicle speed planning;

[0016] Analyze and process the collected image information, determine road conflicts and obstacles as well as weather conditions, obtain real-time road conditions and meteorological information under the influence of weather, and provide forecasts and warnings for various meteorological disasters.

[0017] As a preferred solution of the signal light auxiliary method based on image depth recognition of the present invention, wherein: performing vehicle speed planning to generate the optimal recommended speed for each lane of vehicles passing through the current intersection includes:

[0018] Based on the vehicle dynamics information and the differences in dynamics of different types of vehicles, the vehicle speed model will generate the recommended speeds for vehicles traveling in the fast lane and slow lane, and obtain the specific recommended speed range of the current traffic light;

[0019] Based on the specific recommended speed range of the current traffic light, compare whether it exceeds the speed limit of the current road section; if the generated recommended speed range exceeds the speed limit of the current road section, adjust the traffic light positioning and traffic light information, add the input current green light remaining time plus a green light interval time, and readjust the generated speed to pass the next street light stage.

[0020] As a preferred solution of the signal light auxiliary method based on image depth recognition described in the present invention, it also includes: the mathematical model or algorithm for generating the vehicle speed also includes the phase information of the existing signal light. If the vehicle cannot pass the intersection at a safe speed in time within the remaining time of the current signal light cycle, the updated corresponding data information is re-input in combination with the signal light cycle to obtain the corresponding recommended vehicle speed.

[0021] In a second aspect, the present invention provides a signal light auxiliary system based on image depth recognition, comprising: an image acquisition module, an image processing module, a recommended vehicle speed planning module, a signal identification display module and a server terminal;

[0022] Image acquisition module, real-time acquisition of road and vehicle image information;

[0023] Image processing module, which analyzes and processes collected road and vehicle image information in real time, and obtains various real-time data information for vehicle speed planning;

[0024] The recommended speed planning module generates the optimal recommended speed for vehicles in each lane passing through the current intersection in real time based on the various types of real-time data information;

[0025] The signal sign display module displays the recommended speed, signal light status, weather information and meteorological information of each lane in real time;

[0026] The server terminal is used to store and call the data of each module.

[0027] As a preferred solution of the signal light auxiliary system based on image depth recognition described in the present invention, the image processing module includes: a vehicle speed and traffic flow detection unit, a signal light positioning and recognition unit, a weather information unit, a real-time road condition information unit and a database;

[0028] The vehicle speed and traffic flow detection unit receives the image to be identified acquired by the image acquisition module, pre-processes the image to be identified according to the target detection algorithm, extracts features from the pre-processed image, and identifies the vehicle in the image;

[0029] Identify and classify the identified vehicles, track the position and movement trajectory of the vehicles between different frames through computer vision algorithms, and count the vehicle information and traffic flow information on the road and synchronize them to the server terminal;

[0030] When identifying and classifying a vehicle, the vehicle speed and traffic flow detection unit compares and matches the vehicle type image in the database according to a method combining the vehicle logo and the vehicle model to obtain the vehicle brand and model as well as the vehicle's power performance information.

[0031] As a preferred solution of the signal light auxiliary system based on image depth recognition according to the present invention, the image processing module further includes:

[0032] The signal light positioning and recognition unit realizes accurate positioning and color recognition of traffic lights by training deep neural networks, and at the same time, in combination with existing signal light facilities at intersections, selects the signal cycle and phase information parameters of the current signal light and substitutes them into the recommended speed planning module;

[0033] The meteorological information unit determines the weather conditions and obtains the road conditions and meteorological information under the influence of real-time weather according to the image information collected by the image acquisition module in combination with database analysis and processing, and provides forecasts and warnings for various meteorological disasters;

[0034] The real-time road condition information unit determines road conflicts and obstacles based on the image information collected by the image acquisition module in combination with database analysis and processing;

[0035] The database is used to store the road environment images, various vehicle model images, various accident images, various weather images, and various road obstacle images;

[0036] The recommended vehicle speed planning module receives various types of real-time data information from the image processing module.

[0037] As a preferred solution of the signal light auxiliary system based on image depth recognition according to the present invention, the recommended vehicle speed planning module includes:

[0038] When the vehicle dynamic performance information is input into the recommended speed planning module, in order to consider the differences in dynamic performance of different types of vehicles, the recommended speed planning module will generate recommended speeds for vehicles traveling in the fast lane and the slow lane respectively through the vehicle speed model;

[0039] After generating a specific recommended speed interval that can pass the current traffic light, the recommended speed planning module compares whether it exceeds the speed limit of the current road section; if the generated recommended speed interval exceeds the speed limit of the current road section, the signal light information input by the signal light positioning and recognition unit is adjusted, and the input current green light remaining time is added to a green light interval time, and the generated speed is readjusted to pass the next street light stage;

[0040] When generating a mathematical model or algorithm for vehicle speed, the recommended speed planning module considers the phase information of existing traffic lights and combines the signal light positioning and recognition unit. If the vehicle cannot pass the intersection in time at a safe speed within the remaining time of the current traffic light cycle, the updated corresponding data information is re-input in combination with the traffic light signal cycle to obtain the corresponding recommended speed, and determines the optimal speed based on various road information collected by the server terminal;

[0041] The recommended speed planning module also considers the actual traffic flow and road environment, weather conditions, vehicle starting speed, and braking distance when generating a mathematical model or intelligent algorithm about the vehicle speed, and the generated recommended speed must comply with current road traffic regulations, that is, be less than the current road maximum speed limit.

[0042] As a preferred solution of the signal light auxiliary system based on image depth recognition described in the present invention, wherein: the signal identification display module comprises: the display signal of the signal identification display module includes one or more of the recommended speed information of each lane, the real-time signal light information ahead, the current weather information, the weather warning and other real-time road traffic prompt information;

[0043] The signal identification display module cooperates with the staff to conduct road command and evacuation. The distance between the setting position of the signal identification display module and the signal light position should be included in the planning range of the recommended vehicle speed planning module as one of the conditions for limiting the number of vehicles;

[0044] The signal identification display module is an electronic information display screen set up at a certain distance in front of the road traffic light. The information display screen will prompt the recommended speed information of each lane, real-time information of the traffic light ahead, current weather information, meteorological warnings and other types of prompt information that are beneficial to traffic at the intersection.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: the signal light auxiliary device provided by the present invention can solve the road congestion caused by the frequent starting and stopping of vehicles at intersections to a certain extent, and prompt the vehicle with the best recommended speed through the signal display device, so that the vehicle can pass through the intersection without braking. In addition, considering the differences in the dynamic performance of different types of vehicles, the best recommended speeds for the fast lane and the slow lane are proposed respectively, which improves the traffic efficiency of the intersection and reduces the waste of resources caused by frequent starting and stopping. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0047] Figure 1 A method flow chart of a signal light auxiliary method and system based on image depth recognition according to an embodiment of the present invention;

[0048] Figure 2 A system framework diagram of a signal light auxiliary method and system based on image depth recognition according to an embodiment of the present invention;

[0049] Figure 3 This is a structural diagram of an image processing module of a signal light auxiliary method and system based on image depth recognition according to an embodiment of the present invention;

[0050] Figure 4 A schematic diagram of a vehicle speed and vehicle flow detection process of a signal light auxiliary method and system based on image depth recognition according to an embodiment of the present invention;

[0051] Figure 5 A flowchart of vehicle speed planning of a signal light auxiliary method and system based on image depth recognition according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0055] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0056] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0057] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] Example 1

[0059] Reference Figure 1 to Figure 5 , is an embodiment of the present invention, which provides a signal light auxiliary method based on image depth recognition, including:

[0060] S1: Real-time collection of road and vehicle image information, and real-time analysis and processing of the road and vehicle image information to obtain various real-time data information for vehicle speed planning;

[0061] In the embodiment of the present application, real-time analysis and processing of road and vehicle image information includes:

[0062] Preprocess the image to be identified according to the target detection algorithm, extract features from the preprocessed image, and identify the vehicle in the image; identify and classify the identified vehicle, track the position and movement trajectory of the vehicle between different frames through computer vision algorithms, and count the vehicle information and traffic flow information on the road;

[0063] When identifying and classifying vehicles, the vehicle logo and vehicle model are combined to compare and match with the vehicle type images in the database to obtain the vehicle brand and model as well as the vehicle's power performance information.

[0064] In the embodiment of the present application, it also includes: realizing accurate positioning and color recognition of traffic lights by training a deep neural network, and combining existing traffic light facilities at the intersection, selecting the signal cycle and phase information parameters of the current traffic light for speed planning;

[0065] Analyze and process the collected image information, determine road conflicts and obstacles as well as weather conditions, obtain real-time road conditions and meteorological information under the influence of weather, and provide forecasts and warnings for various meteorological disasters.

[0066] S2: Based on various real-time data information, the vehicle speed is planned to generate the optimal recommended speed for vehicles in each lane to pass through the current intersection;

[0067] In the embodiment of the present application, the vehicle speed planning is performed to generate the optimal recommended speed for each lane of the vehicle passing through the current intersection, including:

[0068] Based on the vehicle dynamics information and the differences in dynamics of different types of vehicles, the vehicle speed model will generate the recommended speeds for vehicles traveling in the fast lane and slow lane, and obtain the specific recommended speed range of the current traffic light;

[0069] Based on the specific recommended speed range of the current traffic light, compare whether it exceeds the speed limit of the current road section; if the generated recommended speed range exceeds the speed limit of the current road section, adjust the traffic light positioning and traffic light information, add the input current green light remaining time plus a green light interval time, and readjust the generated speed to pass the next street light stage.

[0070] In an embodiment of the present application, it also includes: the mathematical model or algorithm for generating the vehicle speed also includes the phase information of the existing traffic lights. If the vehicle cannot pass the intersection at a safe speed in time within the remaining time of the current traffic light cycle, the updated corresponding data information is re-input in combination with the traffic light signal cycle to obtain the corresponding recommended vehicle speed.

[0071] S3: Dynamically displays the optimal recommended speed for each lane, current traffic light status, weather information and other real-time road traffic information to assist vehicle driving.

[0072] The above is a schematic scheme of a signal light auxiliary method based on image depth recognition in this embodiment. It should be noted that the technical scheme of the signal light auxiliary system based on image depth recognition and the technical scheme of the signal light auxiliary method based on image depth recognition described above belong to the same concept. For details not described in detail in the technical scheme of the signal light auxiliary system based on image depth recognition in this embodiment, please refer to the description of the technical scheme of the signal light auxiliary method based on image depth recognition described above.

[0073] In this embodiment, a traffic light auxiliary system based on image depth recognition includes: an image acquisition module 100, an image processing module 200, a recommended vehicle speed planning module 300, a signal identification display module 400 and a server terminal 500;

[0074] Image acquisition module 100, real-time acquisition of road and vehicle image information;

[0075] Image processing module 200, real-time analysis and processing of collected road and vehicle image information, obtaining various real-time data information of vehicle speed planning;

[0076] The recommended speed planning module 300 generates the optimal recommended speed for vehicles in each lane passing through the current intersection in real time based on various real-time data information;

[0077] The signal indicator display module 400 displays the recommended speed, signal light status, weather information and meteorological information of each lane in real time;

[0078] The server terminal 500 is used to store and call the data of each module.

[0079] Specifically, the server terminal 500 is connected to the image acquisition module 100, the image processing module 200, the recommended speed planning module 300, and the signal identification display module 400; the image acquisition module 100 is connected to the image processing module 200 for real-time analysis and processing of collected road and vehicle image information to obtain various real-time data information for speed planning; the recommended speed planning module 300 is connected to the image processing module 200, and the signal identification display module 400 is connected to the recommended speed planning module 300.

[0080] In the embodiment of the present application, the image processing module 200 includes: a vehicle speed and vehicle flow detection unit 201, a signal light positioning and recognition unit 202, a weather information unit 203, a real-time road condition information unit 204 and a database 205;

[0081] The vehicle speed and traffic flow detection unit 201 receives the image to be identified collected by the image acquisition module 100, pre-processes the image to be identified according to the target detection algorithm, extracts features from the pre-processed image, and identifies the vehicle in the image;

[0082] The identified vehicles are identified and classified, and the position and movement trajectory of the vehicles between different frames are tracked through computer vision algorithms. The vehicle information and traffic flow information on the road are counted and synchronized to the server terminal 500.

[0083] Specifically, the database 205 is respectively communicated with the vehicle speed and traffic flow detection unit 201, the traffic light positioning and recognition unit 202, the meteorological information unit 203, and the real-time road condition information unit 204, and is used to store road environment images, various vehicle model images, various accident images, various weather images, and various road obstacle images that need to be analyzed and processed.

[0084] It should be noted that the image acquisition module 100 collects real-time road and traffic image information through cameras and other devices; the image processing module 200 obtains various information parameters required by the traffic light auxiliary system based on the image information collected by the image acquisition module 100 and database analysis.

[0085] In the embodiment of the present application, the image processing module 200 further includes:

[0086] When identifying and classifying a vehicle, the vehicle speed and traffic flow detection unit 201 compares and matches the vehicle type image in the database according to a method combining the vehicle logo and the vehicle model, and obtains the vehicle brand and model as well as the vehicle power performance information.

[0087] Specifically, the vehicle's power performance information includes at least the vehicle's maximum speed and the vehicle's acceleration time.

[0088] It should be noted that the vehicle type identified on the current road and the vehicle power performance information obtained by matching the database 205 are output to the recommended speed planning module 300 and synchronized to the server terminal 500 for standby, and different types of vehicles, such as large freight vehicles and small passenger vehicles, are distinguished according to the vehicle type and vehicle performance information, thereby serving as one of the restriction conditions for generating the corresponding optimal speed for the fast and slow lanes in the recommended speed planning module 300.

[0089] In addition, by identifying and tracking the position and movement trajectory of the vehicle between different frames, real-time traffic information and vehicle information on the current road can be obtained, including traffic volume, traffic density, vehicle spacing, and current vehicle speed.

[0090] In the embodiment of the present application, the image processing module 200 further includes:

[0091] The signal light positioning and recognition unit 202 realizes accurate positioning and color recognition of traffic lights by training a deep neural network, and at the same time, in combination with existing signal light facilities at the intersection, selects the signal cycle and phase information parameters of the current signal light and substitutes them into the recommended speed planning module 300;

[0092] It should be noted that the image processing module 200 obtains the current road section signal light information, such as the phase information, cycle duration, green-to-signal ratio, etc. of the current signal light, through the database 205 and the signal light positioning and recognition unit 202, using the existing signal light facilities at the intersection and processing the road image information.

[0093] In the embodiment of the present application, the meteorological information unit 203 determines the weather conditions and obtains the road conditions and meteorological information under the influence of real-time weather according to the image information collected by the image acquisition module 100 and the database 205 for analysis and processing, and provides forecasts and warnings for various meteorological disasters;

[0094] The real-time traffic information unit 204 determines road conflicts and obstacles based on the image information collected by the image acquisition module 100 and the analysis and processing by the database 205;

[0095] The database 205 is used to store road environment images, various vehicle model images, various accident images, various weather images, and various road obstacle images.

[0096] Specifically, meteorological parameters include visibility, wind direction, wind speed, precipitation conditions, and road surface temperature; precipitation conditions include precipitation type or precipitation intensity.

[0097] In the embodiment of the present application, it is recommended that the vehicle speed planning module 300 receives various types of real-time data information from the image processing module 200 .

[0098] It should be noted that after the image processing module 200 analyzes the collected images, if the output indicates that a traffic accident, road construction and maintenance or other situations that may cause road conflicts and affect vehicle traffic efficiency have occurred, it is recommended that the speed planning module 300 take the current situation into consideration when generating the speed model.

[0099] In the embodiment of the present application, the suggested vehicle speed planning module 300 includes:

[0100] When the vehicle dynamic performance information is input into the recommended vehicle speed planning module 300, in order to consider the differences in the dynamic performance of different types of vehicles, the recommended vehicle speed planning module 300 will generate the recommended speeds for vehicles traveling in the fast lane and the slow lane respectively through the vehicle speed model;

[0101] After generating a specific recommended speed interval that can pass the current traffic light, the recommended speed planning module 300 compares whether it exceeds the speed limit of the current road section; if the generated recommended speed interval exceeds the speed limit of the current road section, the traffic light information input by the traffic light positioning and identification unit 202 is adjusted, and the input current green light remaining time is added with a green light interval time, and the generated speed is readjusted to pass the next street light stage.

[0102] The optimal vehicle speed is determined according to various types of road information collected by the server terminal 500 .

[0103] It should be noted that the recommended speed planning module 300 is based on the purpose of generating a mathematical model or intelligent algorithm about the vehicle speed based on various parameters obtained by the image processing module 200 when the vehicle reaches the intersection at an optimal recommended speed and the traffic light is in a green state indicating direct passage.

[0104] In the embodiment of the present application, the suggested vehicle speed planning module 300 further includes:

[0105] When generating a mathematical model or algorithm about the vehicle speed, the recommended speed planning module 300 needs to consider the phase information of the existing traffic lights, combined with the traffic light positioning and identification unit 202. If the vehicle cannot pass the intersection at a safe speed in time within the remaining time of the current traffic light cycle, then in combination with the traffic light signal cycle, the updated corresponding data information is re-input to obtain the corresponding recommended speed.

[0106] In the embodiment of the present application, it also includes: the recommended vehicle speed planning module 300 also needs to consider the actual traffic flow and road environment, weather conditions, vehicle starting speed, braking distance influencing factors when generating a mathematical model or intelligent algorithm about the vehicle speed, and the generated recommended vehicle speed must comply with the current road traffic regulations, that is, it must be less than the current road maximum speed limit.

[0107] For example, for roads without a center line, the maximum driving speed is 30 kilometers per hour on urban roads and 40 kilometers per hour on highways; for roads with only one motor vehicle lane in the same direction, the maximum driving speed is 50 kilometers per hour on urban roads and 70 kilometers per hour on highways.

[0108] In the embodiment of the present application, the signal identification display module 400 includes: the display signal of the signal identification display module 400 includes one or more of the recommended vehicle speed information of each lane, the real-time traffic light information ahead, the current weather information, the weather warning and other real-time road traffic prompt information;

[0109] The signal sign display module 400 cooperates with the staff to conduct road command and evacuation. The distance between the set position of the signal sign display module 400 and the signal light position should be included in the planning range of the recommended vehicle speed planning module 300 as one of the conditions for limiting the number of vehicles.

[0110] In the embodiment of the present application, the signal identification display module 400 also includes: the signal identification display module 400 is an electronic information display screen arranged at a certain distance in front of the road traffic light. The information display screen will prompt the recommended speed information of each lane, the real-time information of the traffic light ahead, the current weather information, meteorological warnings and other types of prompt information that are conducive to traffic at the intersection.

[0111] For example, when a motor vehicle is driving in fog, rain, snow, dust, hail and visibility is less than 50 meters, or when driving on icy, snowy or muddy roads, the maximum speed shall not exceed 30 kilometers per hour. Among them, tractors, electric vehicles and wheeled special-purpose mechanical vehicles shall not exceed 15 kilometers per hour.

[0112] This embodiment further provides a computing device, applicable to a traffic light auxiliary system based on image depth recognition, including:

[0113] A memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement signal light assistance based on image depth recognition as proposed in the above embodiment.

[0114] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the signal light assistance based on image depth recognition as proposed in the above embodiment is implemented.

[0115] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0116] Example 2

[0117] Reference Figure 1 to Figure 5 , is an embodiment of the present invention. This embodiment is different from the first embodiment in that it provides an exemplary solution to verify the beneficial effects of the present invention.

[0118] Step 1. Input the specific parameter information obtained by the image processing module in combination with the server terminal, namely the current road condition information, vehicle flow information, signal light information, etc., into the recommended speed planning module, and generate a recommended speed range that can pass the current intersection;

[0119] Step 2. After generating a specific recommended speed range that can pass the current traffic light, the recommended speed planning module compares whether it exceeds the speed limit of the current road section;

[0120] Step 3. If the generated recommended speed range exceeds the current speed limit of the road section, it is necessary to return to step 2 to adjust the input signal light information, that is, add the current green light remaining time input in step 2 to the green light interval time, and readjust the generated speed to pass the next street light stage;

[0121] Step 4. Determine the optimal vehicle speed based on various road information collected by the server terminal;

[0122] Step 5. The signal sign display module is an electronic information display screen set at a certain distance in front of the road signal light. The information display screen will combine the information obtained by the image processing module and the recommended speed planning module to prompt the recommended speed information of each lane, the real-time information of the front signal light, the traffic conditions on the road ahead, the current weather information, weather warnings and other types of real-time road traffic prompt information that are conducive to traffic at the intersection. For example:

[0123] "The traffic light n meters ahead will turn red in t seconds (you can pass after t seconds)";

[0124] “The recommended speed for the fast lane is x km / h, and the recommended speed for the slow lane is y km / h”;

[0125] "Current weather: heavy fog, visibility: 8 km";

[0126] In case of an accident or extreme weather:

[0127] "Road construction / accident has occurred y km ahead, please avoid or take a detour";

[0128] "Local heavy rain is expected in k hours."

[0129] When necessary, the signal identification display device will cooperate with the server terminal to cooperate with relevant departments for evacuation or notification.

[0130] The present invention mainly obtains the current road environment and vehicle-related parameters by processing road image information through an image processing module, and develops a recommended speed planning module according to various real-time information to obtain the best recommended speed for various types of vehicles to smoothly pass through the road section under safe driving conditions. The speed information generated by the recommended speed planning module is synchronized in real time to the signal identification display module, which is an electronic information display screen set at a certain distance before the road signal light, and outputs various display signals to the signal identification display system in real time and dynamically with part of the road environment information obtained by the image acquisition module and the speed information obtained by the recommended speed planning module. It can solve the road congestion caused by the frequent start and stop of vehicles at the intersection to a certain extent, and prompt the vehicle to drive at the best recommended speed through the signal display device, so as to achieve the purpose that the vehicle can pass through the intersection without braking. In addition, considering the difference in the dynamic performance of different types of vehicles, the best recommended speeds for the fast lane and the slow lane are proposed respectively, which improves the traffic efficiency of the intersection while reducing the waste of resources caused by frequent start and stop.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A traffic light auxiliary method based on image depth recognition, characterized in that: include: Collect road and vehicle image information in real time, and perform real-time analysis and processing on the road and vehicle image information to obtain various real-time data information for vehicle speed planning; According to the various types of real-time data information, the vehicle speed is planned to generate the optimal recommended speed for vehicles in each lane to pass through the current intersection; Dynamically display the optimal recommended speed for each lane, current traffic light status, weather information and other real-time road traffic prompt information to assist vehicle driving.

2. The signal light auxiliary method based on image depth recognition according to claim 1, characterized in that: The real-time analysis and processing of the road and vehicle image information includes: Preprocess the image to be identified according to the target detection algorithm, extract features from the preprocessed image, and identify the vehicle in the image; identify and classify the identified vehicle, track the position and movement trajectory of the vehicle between different frames through computer vision algorithms, and count the vehicle information and traffic flow information on the road; When identifying and classifying vehicles, the vehicle logo and vehicle model are combined to compare and match with the vehicle type images in the database to obtain the vehicle brand and model as well as the vehicle's power performance information.

3. The signal light auxiliary method based on image depth recognition according to claim 2, characterized in that: Also includes: By training deep neural networks, the precise positioning and color recognition of traffic lights can be achieved. At the same time, the signal cycle and phase information parameters of the current traffic lights are selected for speed planning in combination with the existing traffic light facilities at the intersection. Analyze and process the collected image information, determine road conflicts and obstacles as well as weather conditions, obtain real-time road conditions and meteorological information under the influence of weather, and provide forecasts and warnings for various meteorological disasters.

4. The signal light auxiliary method based on image depth recognition according to claim 3, characterized in that: The vehicle speed planning generates the optimal recommended speed for each lane of vehicles passing through the current intersection, including: Based on the vehicle dynamics information and the differences in dynamics of different types of vehicles, the vehicle speed model will generate the recommended speeds for vehicles traveling in the fast lane and slow lane, and obtain the specific recommended speed range of the current traffic light; Based on the specific recommended speed range of the current traffic light, compare whether it exceeds the speed limit of the current road section; if the generated recommended speed range exceeds the speed limit of the current road section, adjust the traffic light positioning and traffic light information, add the input current green light remaining time plus a green light interval time, and readjust the generated speed to pass the next street light stage.

5. The signal light auxiliary method based on image depth recognition according to claim 4, characterized in that: Also includes: The mathematical model or algorithm for generating vehicle speed also includes the phase information of the existing traffic lights. If the vehicle cannot pass the intersection at a safe speed in time within the remaining time of the current traffic light cycle, the updated corresponding data information is re-input in combination with the traffic light signal cycle to obtain the corresponding recommended vehicle speed.

6. A traffic light auxiliary system based on image depth recognition, characterized in that: include: An image acquisition module (100), an image processing module (200), a recommended vehicle speed planning module (300), a signal identification display module (400), and a server terminal (500); An image acquisition module (100) for acquiring road and vehicle image information in real time; An image processing module (200) analyzes and processes the collected road and vehicle image information in real time to obtain various real-time data information for vehicle speed planning; A recommended vehicle speed planning module (300) generates in real time the optimal recommended vehicle speed for vehicles in each lane passing through the current intersection based on the various types of real-time data information; A signal indicator display module (400) displays the recommended vehicle speed, signal light status, weather information and meteorological information of each lane in real time; The server terminal (500) is used to store and call the data of each module.

7. The signal light auxiliary system based on image depth recognition according to claim 6, characterized in that: The image processing module (200) comprises: a vehicle speed and vehicle flow detection unit (201), a signal light positioning and recognition unit (202), a weather information unit (203), a real-time road condition information unit (204) and a database (205); The vehicle speed and traffic flow detection unit (201) receives the image to be identified acquired by the image acquisition module (100), pre-processes the image to be identified according to the target detection algorithm, extracts features from the pre-processed image, and identifies the vehicle in the image; Identify and classify the identified vehicles, track the position and movement trajectory of the vehicles between different frames through computer vision algorithms, and count the vehicle information and traffic flow information on the road and synchronize them to the server terminal (500); The vehicle speed and traffic flow detection unit (201) compares and matches the vehicle type image in the database according to the method of combining the vehicle logo and the vehicle model when identifying and classifying the vehicle, so as to obtain the vehicle brand and model as well as the vehicle power performance information.

8. The signal light auxiliary system based on image depth recognition according to claim 7, characterized in that: The image processing module (200) further comprises: The signal light positioning and recognition unit (202) realizes accurate positioning and color recognition of traffic lights by training a deep neural network, and at the same time selects the signal cycle and phase information parameters of the current signal light and substitutes them into the recommended vehicle speed planning module (300) in combination with the existing signal light facilities at the intersection; The meteorological information unit (203) determines the weather conditions and obtains the road conditions and meteorological information under the influence of real-time weather conditions based on the image information collected by the image collection module (100) and combined with the database (205) for analysis and processing, and provides forecasts, prompts and warnings for various meteorological disasters; The real-time road condition information unit (204) determines road conflicts and obstacles based on the image information collected by the image collection module (100) and combined with the database (205) for analysis and processing; The database (205) is used to store the road environment images, various vehicle model images, various accident images, various weather images, and various road obstacle images; The recommended vehicle speed planning module (300) receives various types of real-time data information from the image processing module (200).

9. The signal light auxiliary system based on image depth recognition according to claim 8, characterized in that: The suggested vehicle speed planning module (300) comprises: When the vehicle power performance information is input into the recommended vehicle speed planning module (300), in order to take into account the differences in the power performance of different types of vehicles, the recommended vehicle speed planning module (300) will generate recommended speeds for vehicles traveling in the fast lane and the slow lane respectively through a vehicle speed model; After generating a specific recommended speed interval that can pass the current traffic light, the recommended speed planning module (300) compares whether it exceeds the speed limit of the current road section; if the generated recommended speed interval exceeds the speed limit of the current road section, the traffic light information input by the traffic light positioning and recognition unit (202) is adjusted, and the input current green light remaining time is added with a green light interval time, and the generated speed is readjusted to pass the next road light stage; The recommended vehicle speed planning module (300) takes into account the phase information of the existing traffic lights when generating a mathematical model or algorithm for the vehicle speed, and combines the traffic light positioning and recognition unit (202). If the vehicle cannot pass the intersection in time at a safe speed within the remaining time of the current traffic light cycle, the updated corresponding data information is re-input in combination with the traffic light signal cycle to obtain the corresponding recommended vehicle speed, and determines the optimal vehicle speed based on various types of road information collected by the server terminal (500); The recommended vehicle speed planning module (300) also considers actual vehicle flow and road environment, weather conditions, vehicle starting speed, braking distance influencing factors when generating a mathematical model or intelligent algorithm for vehicle speed, and the generated recommended vehicle speed must comply with current road traffic regulations, that is, be less than the current maximum road speed limit.

10. The signal light auxiliary system based on image depth recognition according to claim 9, characterized in that: The signal identification display module (400) comprises: the display signal of the signal identification display module (400) comprises one or more of the recommended vehicle speed information of each lane, the real-time signal light information ahead, the current weather information, the weather warning and other real-time road traffic prompt information; The signal indicator display module (400) cooperates with the staff to conduct road command and evacuation, and the distance between the setting position of the signal indicator display module (400) and the signal light position should be included in the planning range of the recommended vehicle speed planning module (300) as one of the conditions for limiting the number of vehicles; The signal indicator display module (400) is an electronic information display screen arranged at a certain distance in front of the road signal light. The information display screen will prompt the recommended speed information of each lane, the real-time information of the signal light ahead, the current weather information, weather warnings and other various prompt information that are beneficial to traffic at the intersection.

Images