A system and method for preventing red-green signal light obstruction

Abstract

This invention belongs to the field of traffic equipment technology and discloses a system and method for preventing traffic lights from being obstructed. The system includes: a camera installed at the front of a large vehicle to observe the lane the vehicle is in and the traffic light status therein; a traffic light analysis device connected to an image enhancement device to analyze in real time the traffic light information of the lane the vehicle is in or the traffic light information displayed on the display device at the rear of the vehicle, and synchronize this information to the display device; and a display device installed at the rear of the vehicle to provide traffic light indication for the lane the vehicle is in. This invention effectively solves the technical problem of traffic lights being obstructed by vehicles in front, uses universal traffic light indicators for easy observation by drivers of following vehicles, and does not require modification of existing traffic light systems, avoiding construction difficulties and confusion in traffic indication positions.

Description

Technical Field

[0001] This invention belongs to the field of traffic equipment technology, and in particular relates to a system and method for preventing traffic lights from being obscured. Background Technology

[0002] Drivers sometimes encounter tall vehicles such as buses or vans ahead of them. These vehicles are typically quite tall, and when following closely behind, they can obstruct traffic lights, causing the driver to misjudge the light status. For example, if the traffic light turns red just as the large vehicle enters the intersection, and the driver follows closely behind, crossing the intersection directly through the red light, they are running a red light. This kind of mistakenly running a red light while following another vehicle can lead to traffic accidents.

[0003] The existing technical solution 1, "A Traffic Light Obstruction Prevention System Based on a Forward Vision System," application number 2022101042792, mainly involves: a forward vision system installed at the front of the vehicle, facing forward, collecting visual data containing road information ahead and sending it to the system master controller; the system master controller continuously receives the visual data containing road information ahead from the forward vision system in real time, judges and generates braking control signals, and sends them to the braking system module; the braking system module receives the braking control signals from the system master controller and automatically controls the vehicle to decelerate according to the braking control signals. This invention addresses the problem of obstructed traffic lights at intersections when following large vehicles, making it impossible to accurately determine whether it is safe to pass. It employs automatic vehicle control measures to avoid violations and safety accidents caused by limited visibility.

[0004] The existing technical solution 2, "A Method and System for Preventing a Large Vehicle from Obstructing the Driver's View When Following Another Vehicle," application number CN202310345543.6, has the following key idea: When a large vehicle in front obstructs the driver's view and the map navigation function is normal, the system analyzes the map navigation to determine whether the vehicle is about to run a red light and issues an alarm or warning; when a large vehicle in front obstructs the driver's view and the map navigation function is malfunctioning, the system uses a camera to identify whether the vehicle is about to run a red light and issues an alarm or warning. The system acquires camera data to determine if the traffic light in the current lane can be identified; if the traffic light in the current lane can be identified, the system determines whether the vehicle is about to run a red light and issues an alarm or warning; if the traffic light in the current lane cannot be identified, the system provides a warning to increase the following distance and takes appropriate action.

[0005] Existing technical solution 3, "Method and System for Preventing Small Vehicles Following Large Vehicles from Accidentally Running Red Lights," application number CN201711111761.4, describes a method and system for preventing small vehicles following large vehicles from accidentally running red lights. The system includes a traffic light, a red light signal transmitter located on the opposite side of the traffic light, and a vehicle detector. This detector detects the height of vehicles stopped on the same side of the red light signal transmitter and receives the red light status signal from the traffic light. When the height of the vehicle exceeds a preset value and the traffic light is red, the vehicle detector controls the red light signal transmitter to send a red light warning signal to the vehicles stopped on the same side of the red light signal transmitter. This method has the advantage of not requiring modifications to every large vehicle and simultaneously alerting drivers of small vehicles following large vehicles to the traffic light status, thus preventing small vehicles from running red lights.

[0006] The disadvantages of the existing technical solution 1 are: adding a camera (to observe traffic lights) and control components to the car requires significant modifications, is costly, and is technically complex, making it unsuitable for existing automobiles;

[0007] The disadvantages of the existing technical solution 2 are: it requires integration with navigation software, adding a camera (to observe traffic lights) and control unit to the car, which involves significant modifications to the car, high costs, and complex technology, making it unsuitable for existing automobiles;

[0008] The disadvantages of the existing technical solution 3 are: the cost of modifying the existing traffic light facilities is high, and the modification will affect the traffic light indication and cause confusion. Summary of the Invention

[0009] To address the problems existing in the prior art, the present invention provides a system and method for preventing traffic lights from being obstructed.

[0010] This invention is implemented as follows: a system for preventing traffic lights from being obstructed, the system comprising:

[0011] The camera is mounted high up in front of the truck to observe the lane in which the truck is located and the traffic light status of that lane.

[0012] The traffic light analysis device is used to analyze the traffic light information and countdown information of the lane where the large vehicle is located in real time and synchronize it to the display device.

[0013] The display device is located at the rear of the truck and serves as the traffic light indicator for the lane in which the truck is located.

[0014] Another object of the present invention is to provide a method for preventing traffic lights from being blocked by implementing the aforementioned anti-blocking traffic light system, the method comprising:

[0015] S21: Set up the camera at a high position in front of the truck to observe the lane where the truck is located and the traffic light status and countdown status of the lane.

[0016] S22: Using a traffic light analysis device, the traffic light information and countdown status of the lane where the large vehicle is located are analyzed in real time, and the traffic light and countdown information are synchronized to the display device; the display device is set at the rear of the large vehicle to complete the function of indicating the traffic light and countdown information of the lane where the large vehicle is located.

[0017] Another object of the present invention is to provide a computer device, characterized in that the computer device includes a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method for preventing traffic lights from being blocked.

[0018] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for preventing traffic lights from being blocked.

[0019] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0020] First, the system and method for preventing traffic lights from being obscured provided by this invention are simple to modify for large vehicles, do not involve the vehicle control system, and only include a camera, a traffic light analysis device and a display device. Furthermore, it uses universal traffic light indicators, which are easy for drivers of following vehicles to observe. It does not require modification of the existing traffic light system, thus avoiding construction difficulties and confusion in traffic indicator positions.

[0021] This invention effectively solves the technical problem of traffic lights being blocked by vehicles in front, and adopts universal traffic light indicators, making it easy for drivers of following vehicles to observe; it does not require modification of the existing traffic light system, avoiding construction difficulties and confusion in traffic indicator positions.

[0022] The expected benefits and commercial value of the technical solution of this invention after transformation are: reducing fines caused by drivers accidentally running red lights; and reducing the probability of accidents.

[0023] Secondly, the traffic light obstruction prevention system provided by this invention brings about some significant technological advancements, mainly reflected in the following aspects:

[0024] 1. Improved visibility: In urban traffic, large vehicles often obstruct the view of drivers behind, especially at traffic lights. This system effectively solves this problem by displaying the traffic light status at the rear of large vehicles, providing all drivers with a clear view of traffic signals.

[0025] 2. Improved Road Safety: By ensuring the visibility of traffic signals, this system significantly improves road safety. Drivers can react in time, avoiding accidents caused by not seeing traffic lights.

[0026] 3. Utilizing advanced image processing technology: The system employs advanced image processing technology (such as YOLO) to analyze traffic signals, which not only improves the accuracy of signal detection but also demonstrates the application potential of modern technology in the traditional transportation field.

[0027] 4. Enhance traffic flow: By providing accurate traffic light information to all drivers in a timely manner, this system helps improve traffic flow and reduce congestion caused by waiting or unnecessary stops.

[0028] 5. Promoting the development of intelligent transportation systems: The implementation of such systems demonstrates the enormous potential of intelligent transportation systems in improving urban traffic efficiency. It paves the way for more intelligent and automated traffic management systems in the future.

[0029] The traffic light obstruction prevention system provided by this invention represents a significant advancement in the field of traffic technology by offering an innovative vision solution, utilizing advanced image recognition technology, and improving road safety and traffic flow.

[0030] Third, this invention applies computer vision and deep learning technologies to a camera system in a car to analyze lane and traffic light status as well as countdown information, resulting in the following significant technological advancements:

[0031] 1) Improve safety: By accurately detecting lane markings and traffic light status in real time, the system can provide drivers with immediate feedback to help avoid dangerous behaviors such as deviating from the lane or running red lights, thereby significantly improving road driving safety.

[0032] 2) Facilitating autonomous driving technology: Cars integrating these technologies are able to understand their surroundings more accurately, providing important information for autonomous vehicle decision-making and enabling autonomous driving technology to take another step towards full automation.

[0033] 3) Traffic flow optimization: Applying these technologies in intelligent transportation systems can monitor traffic conditions in real time, adjust traffic light cycles based on lane usage and traffic light status, optimize traffic flow, and reduce traffic congestion.

[0034] 4) Improved response speed: Compared with traditional sensors, systems based on computer vision and deep learning can identify and respond to changing traffic signals and lane information faster, thus improving driving response speed.

[0035] 5) Enhanced environmental adaptability: Deep learning models learn through a large amount of training data, enabling them to adapt to lane and traffic light detection under various weather and lighting conditions, thus enhancing the system's environmental adaptability and robustness.

[0036] 6) Information Integration and Decision Support: Integrating lane line detection, traffic light status recognition, and countdown information extraction into a single system provides more comprehensive environmental information and decision support for advanced driver assistance systems (ADAS) and autonomous vehicles.

[0037] 7) Improve energy efficiency: By optimizing traffic light management and vehicle driving strategies, unnecessary stops and accelerations can be reduced, thereby reducing fuel consumption and exhaust emissions and improving energy efficiency.

[0038] The technological advancements provided by this invention not only promote the development of intelligent transportation and autonomous vehicle technologies, but also offer effective means to improve road safety, reduce traffic congestion, and enhance energy efficiency. Attached Figure Description

[0039] Figure 1 This is a structural diagram of the system for preventing traffic lights from being blocked, provided in an embodiment of the present invention;

[0040] Figure 2 This is a flowchart of a method for enhancing image signals transmitted from a camera using an image enhancement device provided in an embodiment of the present invention;

[0041] Figure 3 This is a flowchart of the method for preventing traffic lights from being blocked, provided in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] The present invention provides an improved version of the system for preventing traffic lights from being obstructed, which aims to improve the safety of large vehicles such as trucks or buses in urban traffic, especially when traffic lights are obstructed by these large vehicles.

[0044] 1. Camera: Installed at a high position in front of the large vehicle. It monitors the lane in which the large vehicle is located and the traffic lights.

[0045] 2. Traffic light analysis device: Real-time analysis of the traffic light status and countdown information of the lane where the large vehicle is located.

[0046] Advanced image processing techniques, such as deep learning algorithms (e.g., YOLO), can be used to identify lanes and their corresponding traffic light states.

[0047] 3. Display device: Located at the rear of the vehicle. It displays the traffic light status and countdown information for the lane in which the vehicle is located, so that drivers behind can clearly see the traffic signals.

[0048] Working principle:

[0049] 1. Traffic light observation:

[0050] The camera at the front of the vehicle captures real-time images of the traffic lights ahead, including red, green, yellow lights and countdown times (if available).

[0051] 2. Signal Analysis:

[0052] The traffic light analysis device receives image data from cameras and uses image recognition technology to analyze the status of the traffic lights. In this process, the analysis device can handle complex traffic scenarios and identify the traffic light corresponding to the lane where large vehicles are located.

[0053] 3. Information Display:

[0054] Based on the analyzed data, the display device at the rear of the large vehicle shows the current traffic light status and countdown information in real time. This ensures that even when the large vehicle obstructs the driver's view, the driver behind can still clearly see the traffic signals and make reasonable driving decisions.

[0055] System Advantages: This system solves the problem of large vehicles obstructing traffic lights, reducing the risk of traffic accidents. Real-time display ensures that drivers behind can promptly receive traffic signal information. The system can be widely applied to various large vehicles, especially at busy urban intersections. In this way, the system plays a vital role in improving traffic efficiency and road safety.

[0056] Example 1: Application in urban public transport vehicles

[0057] 1. Camera installation: Install a high-resolution camera at the top front of the bus to ensure clear capture of traffic lights ahead.

[0058] 2. Traffic light analysis device configuration: A small processing unit equipped with advanced image processing software (such as a YOLO-based deep learning model) is installed inside the bus to analyze the traffic light status captured by the camera in real time.

[0059] 3. Display device installation: Install a large LED display screen at the rear of the bus, especially above the rear or near the windows, to display the traffic light status and countdown information.

[0060] 4. System Integration and Operation: The camera is connected to the traffic light analysis and display device via a high-speed data transmission line. The system analyzes the traffic light status in real time and displays the information synchronously on the LED screen at the rear of the vehicle for drivers behind to view.

[0061] Expected results: Improve the visibility of traffic lights to drivers behind urban buses, reducing traffic congestion and accident risks.

[0062] Example 2: Application of long-haul freight trucks

[0063] 1. Camera installation:

[0064] A camera is installed on the top of the front cab of the truck to ensure that the line of sight to the traffic lights ahead is covered.

[0065] 2. Traffic light analysis device configuration:

[0066] A traffic light analysis device is installed in the driver's cab, using image processing algorithms (such as machine learning-based traffic light recognition technology) to analyze traffic light information in real time.

[0067] 3. Display device settings:

[0068] Install a clear display screen at the rear of the truck, especially in a position easily visible to drivers behind.

[0069] 4. System integration and operation:

[0070] The camera, traffic light analysis device, and display device are connected via an in-vehicle network.

[0071] The analysis device processes the traffic light data in real time and synchronizes it to the display screen at the rear of the vehicle.

[0072] Expected results:

[0073] Provide timely traffic signal information to drivers behind, especially in complex or congested traffic conditions, to improve the safety of long-haul trucks.

[0074] In both embodiments, the system utilizes advanced image processing technology and real-time data display to solve the problem of large vehicles obstructing traffic lights, thereby improving the overall safety and efficiency of road traffic.

[0075] like Figure 1 As shown, this embodiment of the invention provides a system for preventing traffic lights from being obstructed. The system includes:

[0076] The camera, installed at the front of the large vehicle, is used to observe the lane in which the large vehicle is located and the traffic light status of that lane;

[0077] An image enhancement device, connected to a camera, is used to enhance the image signal transmitted by the camera using an image enhancement algorithm;

[0078] The traffic light analysis device, connected to the image enhancement device, is used to analyze the traffic light information and countdown information of the lane where the large vehicle is located in real time and synchronize it to the display device.

[0079] The display device, located at the rear of the truck, serves as a traffic light and countdown indicator for the lane in which the truck is located.

[0080] like Figure 3 As shown, this embodiment of the invention provides a method for preventing traffic lights from being blocked by the aforementioned traffic light blocking system. The method includes:

[0081] S21: Place a camera at the front of the vehicle to observe the lane the vehicle is in and the traffic light conditions in that lane; use an image enhancement device to enhance the image signal transmitted by the camera.

[0082] S22: Using a traffic light analysis device, the traffic light information of the lane where the large vehicle is located or the traffic light information displayed on the display device at the rear of the large vehicle in front is analyzed in real time and synchronized to the display device; the display device is set at the rear of the large vehicle to complete the traffic light indication function of the lane where the large vehicle is located.

[0083] S23: When there are multiple large vehicles, the large vehicle B behind observes the display device at the rear of the large vehicle A in front, extracts the traffic light information, and synchronizes it to the display device at the rear of the large vehicle B.

[0084] This invention provides a computer device, characterized in that the computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method for preventing traffic lights from being blocked.

[0085] This invention involves installing cameras on a car to analyze lane and traffic light status as well as countdown information, requiring the use of computer vision and deep learning technologies.

[0086] Object detection: Used to detect and locate lane lines and traffic lights in image or video frames. YOLO is one option, but other object detection algorithms such as Faster R-CNN and SSD (Single Shot MultiBox Detector) can also be considered.

[0087] Semantic segmentation: Used to divide an image into different regions, such as lane regions, non-lane regions, and traffic light regions. Semantic segmentation can help determine the position and shape of lane lines more accurately.

[0088] Instance segmentation: Used to divide each instance object (such as a lane or traffic light area) in an image into independent regions. This can help to better understand the boundaries and locations of different objects in an image.

[0089] Semantic segmentation aims to divide an image into multiple semantically meaningful regions, that is, to label each pixel in the image as belonging to a different semantic category. This means that for each pixel, we need to predict its category, such as road, vehicle, pedestrian, building, etc. Semantic segmentation not only focuses on the segmentation of different regions, but also emphasizes the semantic understanding of different objects in the image.

[0090] Instance segmentation aims to divide each pixel in an image into distinct object instances, labeling each object in the image as an independent instance. Unlike semantic segmentation, instance segmentation distinguishes not only different categories but also different object instances. For example, in a road image, instance segmentation can segment each vehicle and pedestrian into independent instances and assign a unique identifier to each instance.

[0091] The countdown information for the traffic lights is already displayed digitally on the LED screen, so target detection technology can be used to detect and locate the LED screen. Once the LED screen is detected and located, further image processing and character recognition technologies can be used to extract the digital information on the screen, namely the countdown time of the traffic lights.

[0092] Another approach is to use image segmentation techniques to divide the traffic light area in the image, and then apply character recognition technology to identify the countdown numbers. Image segmentation helps separate the traffic lights from the background, making character recognition more accurate.

[0093] Deep learning models: These are models that use convolutional neural networks (CNNs) or other deep learning models for image processing and analysis tasks. These models can be used for tasks such as object detection, semantic segmentation, and instance segmentation.

[0094] Datasets and Training: A large amount of labeled image and video data is needed to train and optimize the model. These datasets should contain variations in road scenes, lane markings, traffic light states, and countdown information.

[0095] Example 1: Lane and traffic light status detection in an intelligent traffic management system

[0096] 1) System configuration: Install high-resolution vehicle-mounted cameras at key locations at traffic intersections to capture real-time images and video data of the traffic intersections.

[0097] 2) Lane Detection: The captured images are processed in real time using an improved YOLO algorithm to detect and locate lane lines. The algorithm is optimized to adapt to different lighting conditions and complex backgrounds, ensuring accurate lane line detection even in complex environments such as rain and nighttime.

[0098] 3) Traffic light status recognition: The SSD algorithm is used to detect traffic lights, and image segmentation technology is used to distinguish the specific status of the traffic lights (red, green, yellow). The model is trained to recognize the status of traffic lights from different angles and distances.

[0099] 4) Countdown Information Extraction: Building upon traffic light detection, optical character recognition (OCR) technology is further applied to extract the countdown information of the traffic lights. The OCR model is optimized to improve the accuracy of recognizing numbers on the LED display screen.

[0100] 5) Data processing and transmission: The processed lane information, traffic light status and countdown information are transmitted to the traffic management center in real time for traffic flow control and signal light adjustment to improve traffic efficiency.

[0101] Example 2: Lane keeping and traffic light response system in autonomous vehicles

[0102] 1) System configuration: Multiple small high-definition cameras are installed on the front and sides of the autonomous vehicle to capture the road conditions around the vehicle from all angles.

[0103] 2) Lane Keeping Assist:

[0104] The Faster RCNN algorithm is used to detect lane lines in real time, and lane regions are accurately identified through semantic segmentation.

[0105] Based on the vehicle's real-time location and direction of travel, the system automatically adjusts the vehicle's path to keep it centered in the lane.

[0106] 3) Traffic light response:

[0107] An improved SSD algorithm is applied to detect and identify traffic lights and their status. Combined with instance segmentation technology, the accuracy and robustness of traffic light detection are improved.

[0108] The vehicle automatically adjusts its speed based on the detected traffic light status, such as automatically slowing down and stopping at a red light, and resuming normal speed at a green light.

[0109] 4) Countdown information processing:

[0110] Based on traffic light detection, OCR technology is applied to identify countdown information, providing vehicles with more accurate predictions of traffic light changes.

[0111] Based on the countdown information, optimize the vehicle's acceleration and deceleration strategies to improve driving efficiency and safety.

[0112] 5) System integration and feedback: Integrate the processed lane and traffic light information into the autonomous driving system to enable real-time response and intelligent decision support for autonomous vehicles.

[0113] These two examples demonstrate how computer vision and deep learning technologies can be used in different application scenarios to detect lane and traffic light status and extract countdown information, providing support for intelligent traffic management and the safety and efficiency of autonomous vehicles.

[0114] This invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for preventing traffic lights from being blocked.

[0115] It should be noted that embodiments of the present invention can be implemented using hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented using hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or using software executed by various types of processors, or using a combination of the above-described hardware circuitry and software, such as firmware.

[0116] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for detecting lane and traffic light status for automobiles, characterized in that, An object detection algorithm is employed to detect and locate lane lines and traffic lights from images or video frames captured by an onboard camera. This method trains the model on a large amount of labeled image and video data, enabling it to accurately identify the status of lane lines and traffic lights in various road scenarios, thereby providing crucial navigation information for the vehicle.

2. The lane and traffic light status detection method as described in claim 1, characterized in that, A method for extracting countdown information from traffic lights is proposed: First, target detection technology is used to detect and locate the LED display screen in the image. Then, image segmentation technology is applied to separate the traffic light area from the background, and character recognition technology is used to extract the countdown numbers from the LED display screen. This method can accurately identify the countdown status of traffic lights in complex road environments, providing important information for driving decisions.

3. The lane and traffic light status detection method as described in claim 1, characterized in that, Deep learning models are used for image analysis and processing, employing convolutional neural networks (CNNs). This method uses other deep learning architectures to perform object detection, semantic segmentation, and instance segmentation tasks. By training and optimizing a large labeled dataset containing various road scenes, lane lines, traffic light states, and countdown information changes, the deep learning model can efficiently process and analyze image and video data from vehicle cameras, thereby improving the performance of the vehicle's automatic navigation and driver assistance systems.

4. A system for preventing traffic lights from being obstructed, characterized in that, The system includes: The camera is mounted high up in front of the truck to observe the lane in which the truck is located and the traffic light status of that lane. The traffic light analysis device is used to analyze the traffic light information and countdown information of the lane where the large vehicle is located in real time and synchronize it to the display device. The display device is located at the rear of the truck and serves as the traffic light indicator for the lane in which the truck is located.

5. A method for preventing traffic light obstruction by implementing the anti-traffic light obstruction system as described in any one of claims 1 to 3, characterized in that, The method includes: S21: Place a camera at the front of the vehicle to observe the lane the vehicle is in and the traffic light conditions in that lane; use an image enhancement device to enhance the image signal transmitted by the camera. S22: Using a traffic light analysis device, the traffic light information of the lane where the large vehicle is located or the traffic light information displayed on the display device at the rear of the large vehicle in front is analyzed in real time and synchronized to the display device; the display device is set at the rear of the large vehicle to complete the traffic light indication function of the lane where the large vehicle is located.

6. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for preventing traffic lights from being blocked as described in any one of claims 1 to 3.

7. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for preventing traffic lights from being blocked as described in any one of claims 1 to 3.

Images