Expressway truck management and control method and system based on Leiyu fusion method

Abstract

The embodiment of the invention discloses an expressway truck management and control method and system based on a thunder-vision fusion method. The method comprises the following steps: collecting vehicle types and vehicle flow of different lanes by using a thunder vision device of a highway management and control road section; calculating road travel time of different lane types based on the vehicle types and the traffic flow of the divided lanes; and optimizing the distribution of the truck lanes and the passenger car lanes by adopting a genetic algorithm according to the road travel time of different lane types. According to the embodiment, the truck lane distribution can be optimized, and the truck passing efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of intelligent transportation technology, and in particular to a method and system for controlling trucks on highways based on a radar-vision fusion method. Background Art

[0002] Mixed passenger and freight traffic is one of the major causes of highway congestion and safety accidents. In existing technologies, limited by the characteristics of high-grade highway traffic flow, truck traffic control is usually achieved by improving the standardization of traffic facilities or relying on video capture and fines on sections with limited points. The control means are single and the effect is limited.

[0003] In view of this, it is urgent to propose an intelligent and automated method to effectively control the passage of trucks on highways. Summary of the invention

[0004] The embodiments of the present invention provide a method and system for controlling trucks on highways based on a radar-vision fusion method to solve the above-mentioned technical problems.

[0005] In a first aspect, an embodiment of the present invention provides a method for controlling trucks on a highway based on a radar-visual fusion method, comprising:

[0006] Use radar equipment on controlled sections of highways to collect vehicle types and traffic volumes in different lanes;

[0007] Calculate the travel time on roads of different lane types based on the vehicle types and traffic volume in the lanes;

[0008] According to the road travel time of different lane types, a genetic algorithm is used to optimize the distribution of truck lanes and passenger lanes.

[0009] In a second aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0010] one or more processors;

[0011] a memory for storing one or more programs,

[0012] When the one or more programs are executed by the one or more processors, the one or more processors implement the highway truck control method based on the radar and vision fusion method described in any embodiment.

[0013] In a third aspect, an embodiment of the present invention further provides a highway truck control system based on a radar-visual fusion method, comprising: a radar-visual device deployed on a controlled section of a highway, and a control module;

[0014] The radar vision equipment is used to collect vehicle types and vehicle flow in different lanes;

[0015] The control module is used to calculate the road travel time of different lane types based on the vehicle types and traffic flow of the lanes; and according to the road travel time of different lane types, a genetic algorithm is used to optimize the distribution of truck lanes and passenger car lanes.

[0016] The embodiment of the present invention proposes a highway truck control method based on a radar-vision fusion method. Through the advanced radar-vision fusion technology, the vehicles and traffic flow in the lanes are accurately measured, and the road travel time of different lane types is calculated; then, based on the road travel time, the optimization objectives of the lanes are constructed, and the fitness function is constructed according to the objectives. The optimal lane type distribution is obtained through a genetic algorithm to optimize the traffic efficiency of mixed passenger and freight traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a method for controlling trucks on highways based on a radar-visual fusion method provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship 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 orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0022] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be 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.

[0023] Figure 1 1 is a flowchart of a highway truck control method based on a radar-visual fusion method provided by an embodiment of the present invention. The method is executed by an electronic device. Figure 1 As shown, the method specifically includes:

[0024] S110. Use radar equipment on controlled sections of expressways to collect data on vehicle types and traffic volumes in different lanes.

[0025] Radar vision equipment includes radars and cameras, which can collect information about vehicle targets from different angles to improve the accuracy of the information.

[0026] Specifically, during the acquisition process, the radar device and the camera device must first be time synchronized. Optionally, the NTP service can be used to synchronize the clocks of the radar and camera in the radar vision equipment of the highway control section. Under the same clock reference, a timestamp is generated for each frame of data of the radar device and the camera device respectively. Since the acquisition frequencies of the two devices are different, the timestamps of the data frames cannot be completely aligned. At this time, the timestamps of the radar data and the camera data can be aligned through the interpolation method. Finally, motion information prediction is used to eliminate the timestamp error caused by jittery streaming.

[0027] After time synchronization, continue to use spatial coordinate conversion to synchronize the time-synchronized radar and camera. Optionally, multiple special corner points in the outer contour of each target vehicle can be extracted from the radar data of any timestamp after time synchronization; at the same time, multiple special corner points in the detection frame of each target vehicle can be extracted from the camera data of the same timestamp after time synchronization; then, the spatial coordinate conversion matrix of the radar data and the camera data is obtained according to the coordinates of each pair of special corner points in their respective coordinate systems. This conversion matrix can be applied to all pixels to achieve spatial alignment of radar data and camera data.

[0028] Step 3: Match and fuse the spatially synchronized radar data and camera data. Optionally, this embodiment adopts a 2D plane fusion method to project the spatially synchronized radar point cloud data onto a 2D plane and match and fuse it with the spatially synchronized camera data to ensure that the vehicle targets in the two data are consistent.

[0029] Step 4: Count the vehicle types and traffic volume in different lanes based on the fused data. Optionally, two traffic flow trip wires can be set at intervals, and the vehicles crossing the two trip wires within the statistical time period can be counted, and traffic flow statistics can be performed by lane and vehicle type.

[0030] S120: Calculate the road travel time of different lane types based on the vehicle types and traffic volume in the lanes.

[0031] This embodiment uses the road travel time as the basis for evaluating the lane distribution. Therefore, this step first calculates the current road travel time. Optionally, for each lane, the number of vehicles of different types can be converted into standard car flow, and then the road travel time of different lane types is calculated based on each standard car flow.

[0032] S130. Optimize the distribution of truck lanes and passenger car lanes using a genetic algorithm according to the road travel time of different lane types.

[0033] In a specific implementation, the lane types of each lane from left to right can be encoded as a chromosome sequence as an object of continuous optimization. At the same time, according to the road travel time of different lane types, the following fitness function is set:

[0034] T=a×t1+b×t2+c×t3

[0035] Among them, T represents the total road travel time of the controlled section, a and t1 represent the number of truck-only lanes and the road travel time respectively, b and t2 represent the number of passenger-freight mixed lanes and the road travel time respectively, and c and t3 represent the number of passenger-only lanes and the road travel time respectively.

[0036] Based on the above chromosome definition and fitness function, with minimization of the fitness function as the optimization goal, a genetic algorithm is used to optimize the distribution of truck lanes, passenger-freight mixed lanes, and passenger-bus lanes. The process specifically includes the following steps:

[0037] Step 1: Based on multiple initial chromosomes.

[0038] Step 2: Perform selection, crossover and mutation operations on each chromosome.

[0039] Selection: According to any of the above fitness functions, select individuals with better performance (smaller fitness value), and these individuals are given priority for crossover and mutation operations.

[0040] Crossover: Exchange some genes of two chromosomes to generate lane distribution.

[0041] Mutation: Randomly adjust the gene values ​​of certain chromosomes to simulate changes in different lanes and explore potential optimization solutions.

[0042] Step 3: Repeat the above operations based on the new chromosome and continuously optimize the road distribution until the fitness value converges or reaches the maximum number of iterations.

[0043] In summary, the embodiment of the present invention proposes a highway truck control method based on a radar-vision fusion method. Through advanced radar-vision fusion technology, the vehicles and traffic flow in the lanes are accurately measured, and the road travel time of different lane types is calculated; then, based on the road travel time, the optimization objectives of the lanes are constructed, and the fitness function is constructed according to the objectives. The optimal lane type distribution is obtained through a genetic algorithm to optimize the traffic efficiency of mixed passenger and freight traffic.

[0044] Based on the same inventive concept, an embodiment of the invention also provides a highway truck control system based on a radar and vision fusion method, including: a radar and vision device deployed on a controlled section of the highway, and a control module.

[0045] The radar vision equipment is used to collect vehicle types and vehicle flow in different lanes;

[0046] The control module is used to calculate the road travel time of different lane types based on the vehicle types and traffic flow of the lanes; and according to the road travel time of different lane types, a genetic algorithm is used to optimize the distribution of truck lanes and passenger car lanes.

[0047] Any features in the above method embodiments are applicable to this embodiment and can achieve the same beneficial effects as in the above method embodiments.

[0048] Figure 2 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 2 A processor 60 is taken as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0049] The memory 61 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the highway truck control method based on the radar-visual fusion method in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, realizing the above-mentioned highway truck control method based on the radar-visual fusion method.

[0050] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0051] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0052] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the highway truck control method based on the radar-vision fusion method of any embodiment.

[0053] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0054] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0055] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0056] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0057] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A highway truck control method based on a radar-vision fusion method, characterized in that: include: Use radar equipment on controlled sections of highways to collect vehicle types and traffic volumes in different lanes; Calculate the travel time on roads of different lane types based on the vehicle types and traffic volume in the lanes; According to the road travel time of different lane types, a genetic algorithm is used to optimize the distribution of truck lanes and passenger lanes.

2. The method according to claim 1, characterized in that: The above-mentioned use of radar vision equipment on the controlled section of the expressway to collect vehicle types and traffic volumes in different lanes includes: Time synchronization of radars and cameras in radar vision equipment on controlled sections of highways; Use spatial coordinate conversion to spatially synchronize the time-synchronized radar and camera; Match and fuse the spatially synchronized radar data and camera data; Based on the fused data, the types of vehicles and traffic flow in different lanes are counted.

3. The method according to claim 2, characterized in that The time synchronization of the radar and camera in the radar vision equipment of the highway control section includes: Use NTP services to synchronize the clocks of radars and cameras in the radar vision equipment on the highway control section; Align the timestamps of radar data and camera data through interpolation method; Motion information prediction is used to eliminate timestamp errors caused by jittery streaming.

4. The method according to claim 2, characterized in that: The method of utilizing spatial coordinate conversion to spatially synchronize the time-synchronized radar and camera includes: Extract multiple special corner points in the outer contour of each target vehicle from the radar data of any time stamp after time synchronization; Extract multiple special corner points in the detection frame of each target vehicle from the camera data with the same timestamp after time synchronization; According to the coordinates of each pair of special corner points in their respective coordinate systems, the spatial coordinate transformation matrix of the radar data and the camera data is obtained; The spatial coordinate conversion matrix is ​​used to perform spatial synchronization on the time-synchronized radar and camera.

5. The method according to claim 2, characterized in that: The matching and fusing of the spatially synchronized radar data and camera data includes: The spatially synchronized radar point cloud data is projected onto a 2D plane and matched and fused with the spatially synchronized camera data.

6. The method according to claim 1, characterized in that The method of calculating the road travel time of different lane types based on the vehicle type and traffic flow of the lanes includes: For each lane, the vehicles of different types are converted into standard car flow; Calculate the road travel time for different lane types based on the standard car flow.

7. The method according to claim 1, characterized in that The method of optimizing the distribution of truck lanes and passenger lanes by using a genetic algorithm according to the road travel time of different lane types includes: According to the road travel time of different lane types, the following fitness function is set: T=a×t1+b×t2+c×t3 Wherein, T represents the total road travel time of the controlled road section, a and t1 represent the number of truck dedicated lanes and the road travel time, b and t2 represent the number of passenger and freight mixed lanes and the road travel time, and c and t3 represent the number of passenger car dedicated lanes and the road travel time, respectively. Taking minimization of the fitness function as the optimization goal, a genetic algorithm is used to optimize the distribution of truck lanes, passenger-freight mixed lanes and passenger-bus lanes.

8. The method according to claim 1, characterized in that The method of optimizing the distribution of truck lanes, passenger-freight mixed lanes, and bus lanes by using a genetic algorithm with minimization of the fitness function as the optimization goal includes: Encode the lane type of each lane from left to right as a chromosome sequence; According to the fitness function, a chromosome sequence with a small fitness value is selected to perform crossover and mutation operations; Repeat the above operations based on the new chromosome to continuously optimize the type of each lane.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the highway truck control method based on the radar and vision fusion method as described in any one of claims 1-8.

10. A highway truck control system based on a radar-visual fusion method, characterized in that: include: Radar vision equipment and control modules deployed on expressway control sections; The radar vision equipment is used to collect vehicle types and vehicle flow in different lanes; The control module is used to calculate the road travel time of different lane types based on the vehicle types and traffic flow in the lanes; According to the road travel time of different lane types, a genetic algorithm is used to optimize the distribution of truck lanes and passenger lanes.

Images