Airport ground traffic virtual signal management method based on LTE-V

Abstract

The invention relates to an airport ground traffic virtual signal management method based on LTE-V. The method belongs to the technical field of intelligent traffic, and comprises the following steps: step 1, monitoring road vehicles in real time through sensing equipment, and identifying the directions of the vehicles; 2, when it is monitored that the traffic flow of a certain phase of the road is larger than a threshold value, calculating the time required for vehicles in the direction to pass through the intersection; step 3, simulating each phase timing at the LTE-V base station, and generating simulation message data for broadcasting; and 4, acquiring the broadcast message through the vehicle-mounted intelligent network connection equipment, calculating a guide vehicle speed interval, and sending a suggested vehicle speed prompt to a driver. According to the invention, low-delay communication service can be provided, real-time transmission and processing of airport ground traffic information can be ensured, traffic flow can be optimized, congestion and waiting time can be reduced and traffic efficiency can be improved through communication and cooperation among vehicles and the LTE-V technology.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to an airport ground transportation virtual signal management method based on LTE-V. Background Art

[0002] With the development of the global aviation industry, the number of airport flights has increased year by year, resulting in a continuous increase in airport ground traffic. At the same time, the number of passengers at airports is also increasing, especially during the peak tourist season or holidays, and the pressure on airport ground traffic is further increased. Airport ground traffic involves a variety of vehicles, including airport buses, freight vehicles, etc., which are numerous and prone to congestion. Passengers may have to wait for a long time at airport ground traffic, which affects their travel experience and may also affect the overall operation efficiency of the airport, such as flight delays and obstructed cargo transportation. Therefore, a virtual signal management method for airport ground traffic based on LTE-V came into being, which is of great significance to improving the overall operation efficiency of the airport. Summary of the invention

[0003] In order to solve the above-mentioned problems in the prior art, the present invention provides an airport ground traffic virtual signal management method based on LTE-V.

[0004] A method for managing virtual signals of airport ground traffic based on LTE-V technology comprises the following steps:

[0005] Step 1: Use sensing equipment to monitor road vehicles in real time and identify the direction of the vehicle;

[0006] Step 2: When the traffic volume at a certain phase of the road is detected to be greater than the threshold, the time required for vehicles in that direction to pass through the intersection is calculated;

[0007] Step 3: Simulate the timing of each phase at the LTE-V base station and generate simulated message data for broadcasting;

[0008] Step 4: Obtain broadcast messages through the on-board intelligent network device, calculate the guidance speed range, and issue a recommended speed reminder to the driver.

[0009] Furthermore, the sensing equipment includes cameras and millimeter-wave radar equipment, which are installed at ground intersections at the airport, and the cameras are used to monitor vehicles on lanes in all directions of the intersection in real time.

[0010] Furthermore, the camera performs target detection based on the YOLO4 algorithm and identifies the direction of the vehicle.

[0011] Furthermore, the direction of the identified vehicle is specifically:

[0012] Confirm the heading angle of the entire lane;

[0013] Collect images of vehicles from multiple angles, including different directions, using cameras;

[0014] Use the annotation tool to annotate the bounding boxes of the vehicles in the image and add direction classification labels;

[0015] Set the target category configuration parameters of the YOLO4 algorithm model according to the road direction;

[0016] Each bounding box output by the model contains a confidence score, and the category with the highest confidence score is taken as the prediction result;

[0017] The model predicts the result with the highest confidence and compares it with the heading angle of the entire lane to determine whether the vehicle's direction is correct.

[0018] Furthermore, before the target detection is performed, the image is preprocessed for data enhancement, specifically:

[0019] Photometric transformation: adjust brightness, contrast, hue, saturation and noise;

[0020] Geometric transformations: random scaling, cropping, flipping, and rotation;

[0021] Simulate target occlusion and image fusion, including: replace image areas with random values ​​or average pixel values ​​of the training set through random erasing technology; set random hidden patches according to probability through Hide-and-Seek technology.

[0022] Furthermore, the step 3 is specifically as follows: the LTE-V base station receives the time results of vehicles on the same straight line passing through the intersection, and generates a SPAT traffic light message according to the LTE_V standard message set to perform dynamic simulation timing, generate simulation message data, and broadcast the simulation message notification to the driver's on-board intelligent network device.

[0023] Furthermore, the step 4 is specifically as follows:

[0024] Step 401: The vehicle-mounted intelligent network device receives SPAT time information sent by the LTE-V base station;

[0025] Step 402: The vehicle determines whether the vehicle is in the road section according to its own vehicle information, and determines whether the lane phase of the road section is consistent with the SPAT signal light message;

[0026] Step 403: If the vehicle is on the road section, and the lane phase of the road section is consistent with the SPAT signal light message; then obtain the virtual signal light state and calculate the guiding vehicle speed range;

[0027] Step 404: Output signal light information and guiding vehicle speed range, and issue a recommended vehicle speed reminder to the driver.

[0028] Furthermore, the vehicle-mounted intelligent network device consists of an LCD display and a terminal host, which is adsorbed in front of the center console; the terminal host includes a C-V2X module, an antenna and a speaker, and displays the recommended vehicle speed information through the LCD display, and the speaker broadcasts the recommended vehicle speed prompt sound.

[0029] The beneficial effects of the present invention are embodied in:

[0030] (1) Real-time: LTE-V technology can provide low-latency communication services, ensuring real-time transmission and processing of airport ground traffic information.

[0031] (2) Efficiency: Through communication and collaboration between vehicles, LTE-V technology can optimize traffic flow, reduce congestion and waiting time, and improve traffic efficiency.

[0032] (3) Safety: LTE-V technology can sense and predict traffic conditions in real time, issue warnings and avoidance instructions in a timely manner, and reduce the risk of accidents.

[0033] (4) Intelligence: The airport ground traffic management system based on LTE-V technology can realize automated and intelligent management, improving management level and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of the method of the present invention;

[0035] Figure 2 It is the structure diagram of the overall equipment data flow;

[0036] Figure 3 This is a data processing flow chart for vehicle-mounted intelligent networked devices. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 are within the scope of protection of the present invention.

[0039] In this embodiment: refer to Figure 1 As shown, a method for managing airport ground traffic virtual signals based on LTE-V according to an embodiment of the present invention includes:

[0040] Step 1: Use sensing equipment to monitor road vehicles in real time and identify the direction of the vehicle;

[0041] Step 2: When the traffic volume at a certain phase of the road is monitored to be greater than the threshold, calculate the time required for vehicles in that direction to pass through the intersection; for example: through intersection data training, determine the time required for vehicles to pass through the intersection. The default intersection speed is approximately between 5-10 kilometers per hour, and the threshold is set to pass through time of 100 meters at a distance of 15 seconds. When the queue of vehicles at the intersection exceeds 100 meters, increase the passing time in that direction.

[0042] Step 3: Simulate the timing of each phase at the LTE-V base station and generate simulated message data for broadcasting;

[0043] Step 4: Obtain broadcast messages through the on-board intelligent network device, calculate the guidance speed range, and issue a recommended speed reminder to the driver.

[0044] In this embodiment, the devices involved mainly include sensing devices (cameras, millimeter wave radars), LTE-V base stations, and vehicle-mounted intelligent network devices, such as Figure 2 As shown:

[0045] 1. Sensing devices

[0046] A set of sensing equipment is installed at the ground intersection of the airport, mainly including cameras and millimeter-wave radar equipment. The cameras and millimeter-wave radars monitor the vehicles on the four lanes of the intersection in real time, count the traffic volume in the four directions, and calculate the time required for vehicles in the four directions to pass through the intersection based on the traffic volume. The required time results are sent to the LTE-V base station via the network cable.

[0047] 1.1. Camera YOLOv4 algorithm detects targets:

[0048] In order to improve the accuracy and efficiency of traffic monitoring, the camera uses the YOLOv4 algorithm for target detection. The data enhancement method is used to significantly improve the accuracy by adding a small amount of training cost without affecting the inference speed, which can make the model generalization performance and robustness better.

[0049] Data augmentation methods:

[0050] Photometric Transform: Adjust brightness, contrast, hue, saturation and noise.

[0051] Geometric transformations: random scaling, cropping, flipping, and rotation.

[0052] Simulating target occlusion and image fusion:

[0053] Random Erase: Replaces regions of an image with random values ​​or the average pixel value of a training set.

[0054] Hide and Seek: Randomly hide some patches according to the probability setting.

[0055] Mosaic data augmentation: Refer to four images stitched into one for training.

[0056] 1.2. YOLOv4 algorithm for target detection: Taking the intersection as an example,

[0057] 1) First confirm the heading angle of the entire lane;

[0058] 2) Collect vehicle images from multiple angles using cameras, including different directions (front, back, left, right);

[0059] 3) Use the annotation tool to annotate the bounding box of the vehicle in the image and add direction classification labels (front, back, left, right);

[0060] 4) According to the road direction, set the target category configuration parameter classes of the YOLO4 algorithm model to 4 (corresponding to the four directions of front, back, left, and right);

[0061] 5) Each bounding box output by the model has a confidence score, and the category with the highest confidence score is taken as the prediction result.

[0062] 6) Compare the model’s prediction result with the highest confidence level with the entire lane heading angle to determine whether the vehicle’s direction is correct.

[0063] GIoU loss function improvement:

[0064] IoU loss function: 1-IoU, when IoU is equal to 0, the gradient disappears and cannot be calculated.

[0065] Improved 1-GIoU loss:

[0066]

[0067] Among them, C is the minimum enclosing box, B is the predicted box, and B gt is the real box, and IOU represents the difference between the predicted box A and the real box B.

[0068] The minimum closed shape C is introduced (C can include the prediction box and the label box). In the case of non-overlapping, the prediction box can move as close to the real box as possible. Although this solves the gradient vanishing problem, it cannot be judged when overlapping because the values ​​of IoU and GIoU are the same.

[0069] DIoU loss function improvement:

[0070]

[0071] Among them, b is the coordinate of the center point of the prediction box, b gtis the coordinate of the center point of the real box, c is the diagonal length of the minimum enclosing box, and ρ() represents the Euclidean distance calculation; the DIoU loss function can directly optimize the distance, which is faster and solves the GIoU problem.

[0072] Finally, the CIoU loss function is used:

[0073]

[0074] in, w gt and h gt Represent the width and height of the real box respectively, w and h represent the width and height of the predicted box respectively. The CIoU loss function must consider three geometric factors: overlapping area, center point distance, and aspect ratio v, where α can be used as a weight parameter.

[0075] 2. LTE-V base station

[0076] The LTE-V base station receives the time results of north-south (east-west) vehicles passing through the intersection, dynamically simulates the timing of the SPAT signal light message (Signal Phase and Timing Message) according to the LTE_V standard message set, generates simulated message data, broadcasts the simulated message notification to the driver, and gives the driver a recommended speed range so that the vehicle can pass through the intersection economically and comfortably (without stopping and waiting).

[0077] 3. In-vehicle intelligent network equipment

[0078] The vehicle-mounted intelligent network device consists of an LCD display, a terminal host (with an embedded C-V2X module, antenna, and speaker), and is attached to the front of the center console. When the SPAT time information sent by the LTE-V base station is received, combined with the vehicle's location information and speed information, the recommended speed information is displayed on the LCD display, and the speaker broadcasts the recommended speed prompt tone. The flow chart is as follows: Figure 3 shown.

[0079] In the description of the embodiments of the present invention, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0080] In the description of the embodiments of the present invention, the term "and / or" herein only describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0081] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A virtual signal management method for airport ground traffic based on LTE-V, characterized in that: The following steps are involved: Step 1: Use sensing equipment to monitor road vehicles in real time and identify the direction of the vehicle; Step 2: When the traffic volume at a certain phase of the road is detected to be greater than the threshold, the time required for vehicles in that direction to pass through the intersection is calculated; Step 3: Simulate the timing of each phase at the LTE-V base station and generate simulated message data for broadcasting; Step 4: Obtain broadcast messages through the on-board intelligent network device, calculate the guidance speed range, and issue a recommended speed reminder to the driver.

2. The airport ground traffic virtual signal management method based on LTE-V according to claim 1 is characterized in that: The sensing equipment includes cameras and millimeter-wave radar equipment, which are installed at the ground intersection of the airport. The cameras are used to monitor vehicles on lanes in all directions of the intersection in real time.

3. The airport ground traffic virtual signal management method based on LTE-V according to claim 2 is characterized in that: The camera performs target detection based on the YOLO4 algorithm and identifies the direction of the vehicle.

4. The airport ground traffic virtual signal management method based on LTE-V according to claim 3 is characterized in that: The direction of the identified vehicle is specifically: Confirm the heading angle of the entire lane; Collect images of vehicles from multiple angles, including different directions, using cameras; Use the annotation tool to annotate the bounding boxes of the vehicles in the image and add direction classification labels; Set the target category configuration parameters of the YOLO4 algorithm model according to the road direction; Each bounding box output by the model contains a confidence score, and the category with the highest confidence score is taken as the prediction result; The model predicts the result with the highest confidence and compares it with the heading angle of the entire lane to determine whether the vehicle's direction is correct.

5. The airport ground traffic virtual signal management method based on LTE-V according to claim 2 is characterized in that: Before the target detection is performed, the image is also preprocessed for data enhancement, specifically: Photometric transformation: adjust brightness, contrast, hue, saturation and noise; Geometric transformations: random scaling, cropping, flipping, and rotation; Simulating object occlusion and image fusion, including: replacing regions of the image with random values ​​or the average pixel value of the training set through random erasing techniques; Hide-and-Seek technique is used to set random hidden patches according to probability.

6. The airport ground traffic virtual signal management method based on LTE-V according to claim 1 is characterized in that: The specific steps of step 3 are as follows: the LTE-V base station receives the time results of vehicles on the same straight line passing through the intersection, generates a SPAT signal light message according to the LTE_V standard message set for dynamic simulation timing, generates simulation message data, and broadcasts the simulation message notification to the driver's on-board intelligent network device.

7. The airport ground traffic virtual signal management method based on LTE-V according to claim 1 is characterized in that: The step 4 is specifically as follows: Step 401: The vehicle-mounted intelligent network device receives SPAT time information sent by the LTE-V base station; Step 402: The vehicle determines whether the vehicle is in the road section according to its own vehicle information, and determines whether the lane phase of the road section is consistent with the SPAT signal light message; Step 403: If the vehicle is on the road section, and the lane phase of the road section is consistent with the SPAT signal light message; then obtain the virtual signal light state and calculate the guiding vehicle speed range; Step 404: Output signal light information and guiding vehicle speed range, and issue a recommended vehicle speed reminder to the driver.

8. The airport ground traffic virtual signal management method based on LTE-V according to claim 1 is characterized in that: The vehicle-mounted intelligent network device consists of an LCD display and a terminal host, which is adsorbed in front of the center console; the terminal host includes a C-V2X module, an antenna and a speaker, and displays the recommended vehicle speed information through the LCD display, and the speaker broadcasts the recommended vehicle speed prompt tone.

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