Tunnel emergency parking strip induction emergency system and method

Abstract

The invention relates to the technical field of tunnel parking guidance, and discloses a tunnel emergency parking strip guidance emergency system and method, and the system comprises a data collection module, an analysis decision module, and an emergency indication module. Wherein the data acquisition module is used for acquiring environment data and vehicle data in a tunnel; the analysis decision module divides the tunnel into different emergency sections based on the environment data, sets an emergency indication strategy for each emergency section, and adjusts the emergency indication strategies based on the vehicle data; the emergency indication module performs emergency guidance on an emergency parking strip in the tunnel based on the emergency indication strategy; the analysis decision module also performs abnormal vehicle detection based on the vehicle data and generates an emergency guidance strategy; the emergency indication module performs emergency stop guidance on the abnormal vehicle based on the emergency guidance strategy; the indication and guidance modes of the emergency parking strip in the tunnel can be reasonably set and adjusted, and tunnel traffic safety can be guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel parking induction, and in particular to a tunnel emergency parking strip induction emergency system and method. Background Art

[0002] At present, emergency stop strips in tunnels have many deficiencies in terms of emergency induction. In terms of accident risk assessment, traditional methods mainly rely on simple accident statistics, which lack comprehensiveness and foresight. Some newly built tunnels may not have enough accident data to conduct reasonable risk assessment due to their short operation time, resulting in the inability to take effective preventive measures in advance. This inaccurate risk assessment may lead to a waste of resources or the neglect of safety hazards.

[0003] In addition, in terms of emergency indication strategies, the existing tunnel emergency parking lane guidance methods are relatively simple. Most tunnels only set fixed light brightness and flashing frequency, which cannot be dynamically adjusted according to actual traffic conditions and vehicle needs. The current technical means lack effective vehicle anomaly detection, cannot timely detect vehicles with abnormal risks, and cannot make guidance plans for potential abnormal vehicles in advance, resulting in the accident rate being difficult to control.

[0004] For example, the patent application with publication number CN115472028A discloses an intelligent warning and induction method and system for emergency parking strips in tunnels, including: computing terminal, parking strip, display module, voice reminder module, rear-view detection module, event detection module, person recognition module, person analysis module, emergency phone, visual module, tunnel broadcast, indicator light, flashing induction sign, and flashing light strip. This application can warn the rear vehicle after the vehicle enters the parking strip of the tunnel, can display the rear road conditions in real time, and effectively guide the vehicle to safely leave the parking strip of the tunnel.

[0005] For example, the patent application with publication number CN114613172A discloses an intelligent sensing warning system for emergency parking strips in tunnels, including a vehicle detection device, a yellow flashing induction sign, a yellow flashing light strip, a full-color information board, an integrated telephone booth, an induction broadcast, a camera, and an information board in the tunnel. The vehicle detection device is set in the emergency parking strip, the yellow flashing induction sign is set on the edge of the road surface of the emergency parking strip, the yellow flashing light strip is set on the side wall, the full-color information board is set at the front end, and the integrated telephone booth, induction broadcast, camera, and the information board in the tunnel near the emergency parking strip are set at the rear end. This application can quickly detect vehicles in the emergency parking strip of the tunnel, effectively establish contact with the faulty vehicle, and timely warn the vehicles behind the vehicles entering and exiting the emergency parking strip.

[0006] The above technical solutions all have the problems raised by this background technology: lack of effective vehicle abnormality detection, inability to timely discover vehicles with abnormal risks, and inability to conduct induction planning for potential abnormal vehicles in advance, resulting in difficulty in controlling the accident rate.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the application and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in the field. Summary of the invention

[0008] The technical problem to be solved by the present application is to overcome the defects of the prior art and provide a tunnel emergency stop lane induction emergency system and method, so as to reasonably set and adjust the indication and induction mode of the emergency stop lane in the tunnel, which is conducive to ensuring tunnel traffic safety.

[0009] In order to solve the above technical problems, this application provides the following technical solutions:

[0010] On the one hand, the present application provides a tunnel emergency stop strip induction emergency system, including a data acquisition module, an analysis and decision module, and an emergency indication module; wherein:

[0011] The data acquisition module is used to collect environmental data and vehicle data in the tunnel;

[0012] The analysis and decision module divides the tunnel into different emergency sections based on the environmental data; the analysis and decision module is also used to set an emergency indication strategy for each emergency section and adjust the emergency indication strategy based on the vehicle data;

[0013] The emergency indication module performs emergency guidance on the emergency parking strip in the tunnel based on the emergency indication strategy;

[0014] The analysis and decision module also performs abnormal vehicle detection based on vehicle data; when an abnormal vehicle is detected, the analysis and decision module generates an emergency induction strategy; and the emergency instruction module performs emergency parking induction on the abnormal vehicle based on the emergency induction strategy.

[0015] As a preferred solution of the tunnel emergency stop strip induction emergency system described in the present application, wherein: the environmental data includes distance data, curvature data, slope data, and accident frequency of each emergency stop strip in the tunnel;

[0016] The vehicle data includes the traffic volume in the tunnel, the travel speed of each vehicle, monitoring images, and temperature distribution diagram;

[0017] The data acquisition module includes a data query unit, a traffic flow detection unit, a video monitoring unit, and a thermal imaging unit; the data query unit is used to obtain the distance data, curvature data, slope data, and accident frequency of each emergency parking strip in the tunnel; wherein the distance data is obtained by the following method: obtaining the position information of each emergency parking strip in the tunnel, and calculating the distance between each emergency parking strip and the adjacent emergency parking strip based on the position information as the distance data of each emergency parking strip;

[0018] The traffic flow detection unit is used to collect the traffic flow in the tunnel and the driving speed of each vehicle; the video monitoring unit is used to collect the monitoring video of each vehicle in the tunnel; and the thermal imaging unit is used to collect the temperature distribution map of each vehicle in the tunnel.

[0019] As a preferred solution of the tunnel emergency stop zone induction emergency system described in the present application, wherein: the analysis and decision-making module includes an emergency strategy unit; the emergency strategy unit is used to divide the tunnel into different emergency sections and set an emergency indication strategy for each emergency section; specifically includes:

[0020] Encode the distance data, curvature data, slope data, and accident frequency of each emergency parking strip into a feature vector corresponding to the emergency parking strip;

[0021] The characteristic vector of each emergency parking strip is clustered by a clustering algorithm; the number of clustering results is set to N, and the emergency parking strips corresponding to the characteristic vectors in the N clustering results are respectively extracted to form N emergency sections;

[0022] Calculate the mean accident frequency of all emergency parking strips in each emergency strip; classify the accident risk level for each emergency strip based on the mean accident frequency;

[0023] An emergency indication strategy is set for each emergency section based on the accident risk level; the emergency indication strategy includes the light brightness and light flashing frequency of each emergency parking strip.

[0024] As a preferred solution of the tunnel emergency stop zone induction emergency system described in the present application, the analysis and decision module further includes a monitoring and adjustment unit; the monitoring and adjustment unit is used to adjust the emergency indication strategy, specifically including:

[0025] Determine the specific conditions of vehicle speed and traffic volume in the current tunnel;

[0026] If the vehicle speed in the tunnel is high and the traffic volume is low, the light brightness and flashing frequency of each emergency stop strip will be increased;

[0027] If the vehicle speed and traffic volume in the tunnel are low, the light brightness and flashing frequency of each emergency stop strip remain unchanged;

[0028] If the vehicle speed in the tunnel is low and the traffic volume is high, the light brightness and flashing frequency of each emergency stop strip will be reduced;

[0029] If the vehicle speed and traffic volume in the current tunnel are high, the light brightness and flashing frequency of each emergency parking strip will remain unchanged.

[0030] As a preferred solution of the tunnel emergency stop lane induction emergency system described in the present application, the method in which the monitoring and adjustment unit determines the specific conditions of the vehicle speed and traffic volume in the current tunnel is as follows:

[0031] The average driving speed of the vehicles in the tunnel is calculated based on the driving speed of each vehicle; the monitoring and adjusting unit is configured with a speed threshold, and if the average driving speed is greater than the speed threshold, it is determined that the vehicle speed in the current tunnel is high, otherwise, it is determined that the vehicle speed in the current tunnel is low;

[0032] The monitoring and adjusting unit is configured with a vehicle flow threshold; if the vehicle flow is higher than the vehicle flow threshold, it is determined that the current vehicle flow in the tunnel is high, otherwise, it is determined that the current vehicle flow in the tunnel is low.

[0033] As a preferred solution of the tunnel emergency stop strip induction emergency system described in the present application, wherein: the emergency indication module includes a lighting indication unit and a dimming drive unit; wherein the lighting indication unit includes an LED lamp installed in each emergency stop strip; the LED lamp is used to indicate the position of the corresponding emergency stop strip;

[0034] The dimming drive unit includes a control device matched with each LED lamp; the control device is used to execute the emergency indication strategy and control the light brightness and light flashing frequency of the LED lamp of each emergency parking strip.

[0035] As a preferred solution of the tunnel emergency parking strip induction emergency system described in the present application, wherein: the analysis and decision-making module also includes an abnormality detection unit, and the abnormality detection unit is used to detect abnormal vehicles; the abnormality detection unit is configured with a target detection algorithm and a multi-target tracking algorithm; the method for detecting abnormal vehicles is as follows:

[0036] Continuously acquiring monitoring images of each vehicle in the tunnel from the video monitoring unit;

[0037] Detecting vehicles from the monitoring image using a target detection algorithm; tracking and detecting each vehicle in continuous monitoring images using a multi-target tracking algorithm;

[0038] Extract two frames of surveillance images to perform random inspection on any vehicle and calculate the random inspection speed of the vehicle; continue to perform random inspection on each vehicle, and if the difference between the random inspection speed and the average driving speed of any vehicle exceeds a preset error range in m consecutive random inspections, the vehicle is marked as a risky vehicle;

[0039] Acquire a thermal image of the risk vehicle based on a thermal imaging unit; acquire the position coordinates of the risk vehicle; and acquire curvature data and slope data corresponding to the position coordinates of the risk vehicle;

[0040] The anomaly detection unit is also configured with an anomaly detection model, which inputs the average driving speed and the sampling speed of any risky vehicle, the thermal imaging image, the curvature data and the slope data corresponding to the position coordinates of the risky vehicle into the anomaly detection model, and outputs the abnormal vehicle detection result.

[0041] As a preferred solution of the tunnel emergency stop strip induction emergency system described in the present application, wherein: the analysis and decision-making module also includes an emergency induction unit; the emergency induction unit is used to generate an emergency induction strategy; specifically includes:

[0042] The position coordinates of the abnormal vehicle are obtained; the emergency parking strip closest to the abnormal vehicle in the direction of the abnormal vehicle's advance is selected as the induced parking strip; the emergency induced unit sends an emergency induced instruction to the dimming drive unit, and the dimming drive unit responds to the emergency induced instruction to increase the light brightness and light flashing frequency of the induced parking strip.

[0043] As a preferred solution of the tunnel emergency parking strip induction emergency system described in the present application, wherein: the emergency indication module also includes an induction identification unit; the emergency induction strategy also includes: the emergency induction unit sends a parking induction instruction to the induction identification unit; the induction identification unit responds to the parking induction instruction and performs emergency parking induction on the abnormal vehicle; the induction identification unit includes a display screen installed along the tunnel; the emergency parking induction for the abnormal vehicle includes: indicating the direction of the induction parking strip by an arrow on the display screen, and marking the distance of the induction parking strip from the display screen.

[0044] In a second aspect, the present application provides a method for inducing emergency response in an emergency parking zone in a tunnel, specifically comprising: collecting environmental data in the tunnel; dividing the tunnel into different emergency sections based on the environmental data, and setting an emergency indication strategy for each emergency section;

[0045] Collecting vehicle data in the tunnel; adjusting the emergency indication strategy based on the vehicle data;

[0046] Provide emergency guidance for emergency parking strips in tunnels based on emergency indication strategies;

[0047] Identify risky vehicles in the tunnel based on vehicle data; perform anomaly detection on risky vehicles and identify abnormal vehicles;

[0048] The position coordinates of the abnormal vehicle are obtained; an emergency induction strategy is generated for the abnormal vehicle based on the position coordinates, and emergency parking induction is performed on the abnormal vehicle according to the emergency induction strategy.

[0049] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0050] By analyzing environmental data such as the distance, curvature, slope and accident frequency of each emergency parking strip in the tunnel, the tunnel is divided into different emergency sections and the accident risk levels are reasonably divided, avoiding the one-sidedness of relying solely on accident risk data. It can better predict potential high-incidence areas of accidents and provide a basis for the formulation of subsequent emergency instruction strategies.

[0051] The emergency indication strategy for each emergency section is dynamically adjusted based on the number of vehicles. When the vehicle speed is high and the traffic volume is low, the brightness and frequency are increased to facilitate the rapid vehicle attention. When the vehicle speed is low and the traffic volume is high, the brightness and frequency are reduced to reduce the visual burden on the driver, so that the emergency indication is more in line with the actual traffic conditions and improves the guidance effect.

[0052] By utilizing target detection algorithms, multi-target tracking algorithms and anomaly detection models, combined with vehicle monitoring images, sampling speeds, thermal imaging images, and location-related curvature and slope data, abnormal vehicles can be accurately detected. By increasing the light brightness and flashing frequency of the induced parking strip, and providing induced information on the display screen, abnormal vehicles can be effectively guided to the nearest emergency parking strip to ensure tunnel traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0054] Figure 1 A schematic diagram of the structure of a tunnel emergency stop strip induction emergency system provided in this application;

[0055] Figure 2 A functional schematic diagram of a tunnel emergency stop strip induction emergency system provided for this application;

[0056] Figure 3 A flow chart of the method for dividing emergency sections and setting emergency indication strategies provided in this application. DETAILED DESCRIPTION

[0057] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0058] Example 1

[0059] This embodiment introduces a tunnel emergency stop zone induction emergency system. Figure 1 The system includes data acquisition module, analysis and decision module, and emergency instruction module; the functions of each module are as follows: Figure 2 shown.

[0060] The data acquisition module is used to collect environmental data and vehicle data in the tunnel;

[0061] The environmental data includes distance data, curvature data, slope data, and accident frequency of each emergency stop strip in the tunnel;

[0062] The vehicle data includes the traffic volume in the tunnel, the travel speed of each vehicle, monitoring images, and temperature distribution diagram;

[0063] The data acquisition module includes a data query unit, a traffic flow detection unit, a video monitoring unit, and a thermal imaging unit; wherein the data query unit is used to obtain the distance data, curvature data, slope data, and accident frequency of each emergency parking strip in the tunnel; wherein the distance data is obtained by the following method: obtaining the position information of each emergency parking strip in the tunnel, and calculating the distance between each emergency parking strip and the adjacent emergency parking strip based on the position information as the distance data of each emergency parking strip. For the emergency parking strip closest to either end of the tunnel, it has only one adjacent emergency parking strip, and the distance between the two is its distance data; for any emergency parking strip located in the middle of the tunnel, it has adjacent emergency parking strips on both sides, and the distance between it and the emergency parking strips on both sides can be calculated and the average can be calculated as its distance data. Distance data is one of the indicators to measure the importance of each emergency parking strip. For an emergency parking strip with a large distance data, when a vehicle needs to make an emergency stop near it, the parking pressure of the parking strip is relatively large due to the fact that other emergency parking strips nearby are located far away.

[0064] The curvature data is the curvature radius of the emergency parking strip; the slope data is the slope of the emergency parking strip, with positive values ​​for uphill and negative values ​​for downhill. Obtain historical accident records in the tunnel, including the location of the accident (specifically a certain location or interval in the tunnel); divide each emergency parking strip into associated intervals, such as the range within 10 meters on both sides of the emergency parking strip as its associated interval, perform statistical analysis on historical accident records, and calculate the accident frequency of each associated interval of the emergency parking strip, such as the number of accidents within the associated interval within a year or a quarter as the accident frequency of the corresponding emergency parking strip.

[0065] The traffic flow detection unit is used to collect the traffic volume in the tunnel and the driving speed of each vehicle; the traffic flow detection unit is equipped with geomagnetic sensors, microwave radars and other equipment installed above or on both sides of the lanes in the tunnel, which can accurately detect the traffic volume in the tunnel and the driving speed of each vehicle.

[0066] The video surveillance unit is used to collect surveillance videos of each vehicle in the tunnel; the video surveillance unit includes a high-definition camera installed on the top or side wall of the tunnel. The monitoring range covers the entire tunnel and all emergency parking lanes, and the real-time location of each vehicle can be obtained through the monitoring screen.

[0067] The thermal imaging unit is used to collect the temperature distribution map of each vehicle in the tunnel. The thermal imaging unit includes a thermal imaging camera installed near each emergency parking strip, which can detect the temperature distribution of each vehicle in the associated interval of the corresponding emergency parking strip and generate a temperature distribution map for each vehicle.

[0068] The analysis and decision module divides the tunnel into different emergency sections based on the environmental data; the analysis and decision module is also used to set an emergency indication strategy for each emergency section and adjust the emergency indication strategy based on the vehicle data;

[0069] The environmental data of each emergency parking strip is different, so the emergency parking requirements are also different. Therefore, emergency indication strategies are set for each emergency parking strip according to the specific environmental data. For example, emergency parking is often required at bends or accident-prone areas, so it is necessary to strengthen the lighting indication and guidance of the emergency parking strips at these locations.

[0070] The analysis and decision-making module includes an emergency strategy unit and a monitoring and adjustment unit; wherein:

[0071] The emergency strategy unit is used to divide the tunnel into different emergency sections and set an emergency indication strategy for each emergency section. Figure 3 , as follows:

[0072] Performing data cleaning on the environmental data, and normalizing the distance data, curvature data, slope data, and accident frequency respectively;

[0073] Encode the distance data, curvature data, slope data, and accident frequency of each emergency parking strip into a feature vector corresponding to the emergency parking strip;

[0074] The characteristic vector of each emergency stop strip is clustered by a clustering algorithm; let the number of clustering results be N, where N is a positive integer, and extract the emergency stop strips corresponding to the characteristic vectors in the N clustering results to form N emergency sections; the characteristic vectors of the emergency stop strips in any emergency section correspond to the same clustering result; according to the characteristics of the data and the purpose of the analysis, the K-Means clustering algorithm can be selected here. The elbow rule can be used to determine the appropriate number of clusters, that is, the number of clustering results, and the relationship curve between the number of clusters and the sum of squared errors (SSE) within the clusters can be plotted. As the number of clusters increases, the SSE will gradually decrease. When the number of clusters starts from a certain value, the speed at which the curve decreases begins to slow down, then the number of clusters can be initially set to this value. The emergency stop strips are divided into different clustering results, and the emergency stop strips in each clustering result have similar terrain and accident characteristics. For example, the emergency stop strips in one cluster may all be located in a long straight tunnel section with a low accident frequency, while the emergency stop strips in another cluster may all be located in a curve with a high accident frequency.

[0075] Calculate the mean of the accident frequency of all emergency parking strips in each emergency strip; divide the accident risk level for each emergency strip based on the mean of the accident frequency; for example, the clustering result data N is equal to 3, that is, three emergency sections are divided; among the three emergency sections, the emergency section with the highest mean of accident frequency has a high risk level, the emergency section with the second highest mean of accident frequency has a medium risk level, and the emergency section with the lowest mean of accident frequency has a low risk level;

[0076] Relying solely on accident risk data may not fully reflect the characteristics of different areas within the tunnel. Some newer tunnels may not have enough data to make a reasonable risk level classification, or accident data in certain areas may be biased due to accidental factors. For example, several abnormal accidents occurred near an emergency parking strip in a short period of time (such as sudden vehicle failures or sudden illness of the driver), but from a long-term and actual road condition perspective, this area is not a high-risk area. Therefore, this application selects distance data, curvature data, and slope data that are highly correlated with the frequency of accidents, and clusters them together with the frequency of accidents, so as to better predict potential high-incidence areas of accidents.

[0077] An emergency indication strategy is set for each emergency section based on the accident risk level; the emergency indication strategy includes the light brightness and light flashing frequency of each emergency parking strip. For example, for an emergency section with a low accident risk level, the light brightness of each emergency parking strip used to indicate the parking position is 40 to 60 lm (lumens), and the light flashing frequency is 10 to 20 times per minute. The brightness and flashing frequency of the light can be relatively low, providing basic indication lighting without affecting the normal vision of the driver. For an emergency section with a medium accident risk level, the light brightness of each emergency parking strip is 60 to 80 lm, and the light flashing frequency is 30 to 50 times per minute. The medium brightness and flashing frequency enable the driver to effectively identify the facilities and signs of the emergency parking strip. For an emergency section with a low accident risk level, the light brightness of each emergency parking strip is 80 to 100 lm, and the light flashing frequency is 60 to 90 times per minute. A higher light brightness is set to ensure that the emergency parking strip is clearly visible under various light conditions, helping the driver to better judge the distance and position of the emergency parking strip. The high-frequency flashing light mode allows the driver to notice the location of the emergency parking strip from a long distance, making it easier for the driver to prepare for parking in advance.

[0078] The monitoring adjustment unit is used to adjust the emergency indication strategy, specifically including:

[0079] Determine the specific conditions of vehicle speed and traffic volume in the current tunnel;

[0080] The average driving speed of the vehicles in the tunnel is calculated based on the driving speed of each vehicle; the monitoring and adjusting unit is configured with a speed threshold, and if the average driving speed is greater than the speed threshold, it is determined that the vehicle speed in the current tunnel is high, otherwise, it is determined that the vehicle speed in the current tunnel is low;

[0081] The monitoring and adjusting unit is configured with a vehicle flow threshold; if the vehicle flow is higher than the vehicle flow threshold, it is determined that the vehicle flow in the current tunnel is high; otherwise, it is determined that the vehicle flow in the current tunnel is low;

[0082] If the current vehicle speed in the tunnel is high and the traffic volume is low, the light brightness and flashing frequency of each emergency stop strip are increased; for example, the light brightness and flashing frequency are both increased by 20%, so that fast-moving vehicles can pay attention to the emergency stop strip in time.

[0083] If the current vehicle speed and traffic volume in the tunnel are low, the light brightness and flashing frequency of each emergency parking strip will remain unchanged; at this time, the traffic conditions in the tunnel are good, and the driver has enough time and vision to observe the location of the emergency parking strip, and there is no need to adjust the emergency indication strategy.

[0084] If the current vehicle speed in the tunnel is low and the traffic volume is high, the light brightness and flashing frequency of each emergency parking strip will be reduced; for example, the light brightness and flashing frequency are both reduced by 10%; higher light brightness will increase the visual burden on the driver; reducing the light flashing frequency can also reduce the stimulation to drivers in congested and slow-moving situations.

[0085] If the current vehicle speed and traffic volume in the tunnel are high, the light brightness and flashing frequency of each emergency stop strip should be kept unchanged; when the vehicle speed is high, the emergency stop strip should be more eye-catching, but in the case of high traffic volume, in order to prevent excessive brightness from causing glare that interferes with the driver's vision, and to prevent high-frequency flashing from causing visual confusion, the emergency indication strategy will not be adjusted.

[0086] The emergency indication module performs emergency guidance on the emergency parking strip in the tunnel based on the emergency indication strategy;

[0087] The emergency indication module includes a lighting indication unit and a dimming drive unit; wherein the lighting indication unit includes an LED lamp installed in each emergency parking strip; the LED lamp is used to indicate the position of the corresponding emergency parking strip; each LED lamp has an independent control function and can flexibly adjust the brightness and flashing frequency.

[0088] The dimming drive unit includes a control device matched with each LED lamp; the control device is used to execute the emergency indication strategy and control the light brightness and light flashing frequency of each LED lamp in the emergency parking strip. The control device can accurately control the current and voltage of the matching LED lamp to achieve fast and stable adjustment of the brightness and flashing mode of the lamp.

[0089] The analysis and decision module also performs abnormal vehicle detection based on vehicle data; when an abnormal vehicle is detected, the analysis and decision module generates an emergency induction strategy; and the emergency instruction module performs emergency parking induction on the abnormal vehicle based on the emergency induction strategy.

[0090] The analysis and decision module also includes an abnormality detection unit and an emergency induction unit; wherein the abnormality detection unit is used to detect abnormal vehicles; the abnormality detection unit is configured with a target detection algorithm and a multi-target tracking algorithm; the method for detecting abnormal vehicles is as follows:

[0091] Continuously acquiring monitoring images of each vehicle in the tunnel from the video monitoring unit;

[0092] Vehicles are detected from the surveillance image through a target detection algorithm, and each detected vehicle is numbered; target detection algorithms based on deep learning, such as the YOLO series models, Faster R-CNN, etc., are pre-trained on large-scale data sets and fine-tuned on task-specific data sets, and can accurately identify vehicles in images.

[0093] Through a multi-target tracking algorithm, each vehicle is tracked and detected in continuous monitoring images based on the number; multi-target tracking algorithms such as DeepSORT can achieve continuous tracking of vehicles in monitoring images of different frames based on appearance features (such as the color and shape of the vehicle).

[0094] Extract two frames of surveillance images to perform random inspections on any vehicle and calculate the vehicle's random inspection speed; continue to perform random inspections on each vehicle. If the difference between the random inspection speed and the average driving speed of any vehicle in m consecutive random inspections exceeds the preset error range, the vehicle is marked as a risky vehicle; m is a positive integer; the target detection algorithm can provide bounding box information for each vehicle, and the bounding box information can determine the two-dimensional coordinates of the vehicle in the surveillance image. Through the intrinsic parameters (such as focal length, principal point coordinates, etc.) and extrinsic parameters (such as rotation matrix, translation vector, etc.) of the surveillance camera, coordinate conversion can be performed to convert the two-dimensional coordinates in the surveillance image into the real position coordinates of the vehicle in the tunnel. Calculate the difference (i.e., distance) between the position coordinates of the same vehicle in two frames of surveillance images, divide it by the time difference between the two frames of surveillance images, and obtain the random inspection speed of the corresponding vehicle.

[0095] Acquire a thermal image of the risk vehicle based on a thermal imaging unit; acquire the position coordinates of the risk vehicle, and acquire curvature data and slope data corresponding to the position coordinates of the risk vehicle;

[0096] The anomaly detection unit is also configured with an anomaly detection model, which inputs the average driving speed and the sampling speed of any risky vehicle, the thermal imaging image, the curvature data and the slope data corresponding to the position coordinates of the risky vehicle into the anomaly detection model, and outputs the abnormal vehicle detection result.

[0097] The anomaly detection model includes an input layer, a feature extraction layer, a feature combination layer, and an output layer;

[0098] The input layer is used to receive the average driving speed and the sampling speed of any risky vehicle, the thermal image, the curvature data and the slope data corresponding to the position coordinates of the risky vehicle as model inputs; the input layer includes an image input layer and a numerical input layer, wherein the image input layer is used to receive the thermal image, which is cropped and resized, with a length and width of 224 pixels; the numerical input layer is used to receive the average driving speed, sampling speed, curvature and slope, and these values ​​are encoded and input into the numerical input layer in the form of feature vectors.

[0099] The feature extraction layer extracts the first feature based on the thermal image of the risky vehicle, and extracts the second feature based on the average driving speed, the sampling speed of the risky vehicle, the curvature data corresponding to the position coordinates of the risky vehicle, and the slope data; the feature extraction layer includes an image feature extraction part and a numerical feature extraction part; wherein the image feature extraction part is composed of a plurality of convolutional layers and pooling layers, and the features of the thermal image are gradually extracted as the first feature through convolution and pooling operations, and the first feature is flattened. The numerical feature extraction part includes a plurality of fully connected layers, and maps the input feature vector to a high-dimensional space as the second feature.

[0100] The feature combination layer concatenates the first feature with the second feature to obtain a fused feature; the concatenation of the first feature with the second feature enables the model to comprehensively consider multiple types of data features.

[0101] The output layer maps the fused features to the abnormal probability value of the risk vehicle; the output layer uses Sigmoid as the activation function to map the spliced ​​fused features to an output value ranging from 0 to 1, representing the abnormal probability of the risk vehicle. An abnormal threshold is set based on actual needs. If the abnormal probability threshold of any risk vehicle is higher than the abnormal threshold, the risk vehicle is marked as an abnormal vehicle.

[0102] The thermal image of each vehicle is collected by a thermal imaging camera located in front of the vehicle in the direction of the vehicle's advance, ensuring that the collection direction of the thermal images of different vehicles is consistent and comparable. Thermal images can provide temperature distribution information of the vehicle. Normal vehicles have their normal temperature distribution. For example, the engine, brakes, tires and other components have a certain temperature range when working. If the thermal image shows that the temperature of a component is abnormally high or abnormally low, it may indicate that the component is faulty. Using the characteristics of convolutional neural networks, features are automatically extracted from thermal images, which may reflect the fault status of the vehicle. By processing numerical data through multiple fully connected layers, the relationship between these numerical features can be learned, such as mining data features such as the speed of the vehicle at different curvatures and slopes, and data features such as the consistency of risk vehicles with the average driving speed in the tunnel, in order to discover potential abnormalities.

[0103] The emergency induction unit is used to generate an emergency induction strategy; specifically includes:

[0104] The position coordinates of the abnormal vehicle are obtained; the emergency parking strip closest to the abnormal vehicle in the direction of the abnormal vehicle's advance is selected as the induced parking strip; the emergency induced unit sends an emergency induced instruction to the dimming drive unit, and the dimming drive unit responds to the emergency induced instruction to increase the light brightness and light flashing frequency of the induced parking strip; for example, a maximum light brightness and a maximum light flashing frequency are set for each emergency parking strip, and when the dimming drive unit receives the emergency induced instruction, the light brightness of the corresponding emergency parking strip is switched to the maximum light brightness, and the light flashing frequency is switched to the maximum light flashing frequency.

[0105] The emergency indication module also includes an induction identification unit; the emergency induction strategy also includes: the emergency induction unit sends a parking induction instruction to the induction identification unit; the induction identification unit responds to the parking induction instruction and performs emergency parking induction on the abnormal vehicle; the induction identification unit includes a display screen installed along the tunnel; the emergency parking induction for the abnormal vehicle includes: indicating the direction of the induction parking strip by an arrow on the display screen, and marking the distance of the induction parking strip from the display screen; by arranging high-brightness and high-contrast display screens near each emergency parking strip and at key positions along the tunnel, it is ensured that the abnormal vehicle can be clearly and accurately guided to the emergency parking strip closest to it in various environments.

[0106] Example 2

[0107] This embodiment is the second embodiment of the present application. Based on the same inventive concept as that of the first embodiment, this embodiment introduces a tunnel emergency stop strip induction emergency method, which specifically includes:

[0108] Collect environmental data in the tunnel; specifically, collect distance data, curvature data, slope data, and accident frequency of each emergency parking strip in the tunnel; divide the tunnel into different emergency sections based on the environmental data, and set an emergency indication strategy for each emergency section; the emergency indication strategy includes the light brightness and light flashing frequency of each emergency parking strip.

[0109] Collect vehicle data in the tunnel; adjust the emergency indication strategy based on the vehicle data; and adjust the emergency indication strategy based on the specific conditions of vehicle speed and traffic volume in the tunnel.

[0110] Emergency guidance is carried out for the emergency parking strips in the tunnel based on the emergency indication strategy; the working parameters of the LED lamps in each emergency parking strip are adjusted according to the light brightness and light flashing frequency specified by the emergency indication strategy, so as to realize personalized emergency guidance for each emergency parking strip.

[0111] Identify risky vehicles in the tunnel based on vehicle data; perform anomaly detection on risky vehicles and identify abnormal vehicles; use target detection algorithms and multi-target tracking algorithms to determine whether the speed of any vehicle in the detection is synchronized with the average speed of all vehicles. Unsynchronized vehicles are risky vehicles. Use the trained anomaly detection model to determine whether each risky vehicle is an abnormal vehicle.

[0112] The position coordinates of the abnormal vehicle are obtained; an emergency induction strategy is generated for the abnormal vehicle based on the position coordinates, and the abnormal vehicle is guided to an emergency stop according to the emergency induction strategy. The display screen is used to remind the abnormal vehicle of the direction and distance of the nearest emergency stop strip, thereby accurately guiding the abnormal vehicle to the nearest emergency stop strip.

[0113] The specific functions of the above steps are realized by referring to the relevant contents of the tunnel emergency parking strip induction emergency system described in Example 1, which will not be elaborated here.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0115] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose and scope of protection of the present application, all of which are within the protection of the present application.

Claims

1. A tunnel emergency stop lane induction emergency system, characterized by: It includes data acquisition module, analysis and decision-making module, and emergency instruction module; among which: The data acquisition module is used to collect environmental data and vehicle data in the tunnel; The analysis and decision module divides the tunnel into different emergency sections based on the environmental data; the analysis and decision module is also used to set an emergency indication strategy for each emergency section and adjust the emergency indication strategy based on the vehicle data; The emergency indication module performs emergency guidance on the emergency parking strip in the tunnel based on the emergency indication strategy; The analysis and decision module also performs abnormal vehicle detection based on vehicle data; when an abnormal vehicle is detected, the analysis and decision module generates an emergency induction strategy; and the emergency instruction module performs emergency parking induction on the abnormal vehicle based on the emergency induction strategy.

2. A tunnel emergency stop zone induction emergency system as claimed in claim 1, characterized in that: The environmental data includes distance data, curvature data, slope data, and accident frequency of each emergency stop strip in the tunnel; The vehicle data includes the traffic volume in the tunnel, the travel speed of each vehicle, monitoring images, and temperature distribution diagram; The data acquisition module includes a data query unit, a traffic flow detection unit, a video monitoring unit, and a thermal imaging unit; the data query unit is used to obtain the distance data, curvature data, slope data, and accident frequency of each emergency parking strip in the tunnel; wherein the distance data is obtained by the following method: obtaining the position information of each emergency parking strip in the tunnel, and calculating the distance between each emergency parking strip and the adjacent emergency parking strip based on the position information as the distance data of each emergency parking strip; The traffic flow detection unit is used to collect the traffic flow in the tunnel and the driving speed of each vehicle; the video monitoring unit is used to collect monitoring videos of each vehicle in the tunnel; and the thermal imaging unit is used to collect the temperature distribution map of each vehicle in the tunnel.

3. A tunnel emergency stop zone induction emergency system as claimed in claim 2, characterized in that: The analysis and decision module includes an emergency strategy unit; the emergency strategy unit is used to divide the tunnel into different emergency sections and set an emergency indication strategy for each emergency section; Specifically include: Encode the distance data, curvature data, slope data, and accident frequency of each emergency parking strip into a feature vector corresponding to the emergency parking strip; The characteristic vector of each emergency parking strip is clustered by a clustering algorithm; the number of clustering results is set to N, and the emergency parking strips corresponding to the characteristic vectors in the N clustering results are respectively extracted to form N emergency sections; Calculate the mean accident frequency of all emergency parking strips in each emergency strip; classify the accident risk level for each emergency strip based on the mean accident frequency; An emergency indication strategy is set for each emergency section based on the accident risk level; the emergency indication strategy includes the light brightness and light flashing frequency of each emergency parking strip.

4. A tunnel emergency stop zone induction emergency system as claimed in claim 3, characterized in that: The analysis and decision module also includes a monitoring and adjustment unit; the monitoring and adjustment unit is used to adjust the emergency indication strategy, specifically including: Determine the specific conditions of vehicle speed and traffic volume in the current tunnel; If the vehicle speed in the tunnel is high and the traffic volume is low, the light brightness and flashing frequency of each emergency stop strip will be increased; If the vehicle speed and traffic volume in the tunnel are low, the light brightness and flashing frequency of each emergency stop strip remain unchanged; If the vehicle speed in the tunnel is low and the traffic volume is high, the light brightness and flashing frequency of each emergency stop strip will be reduced; If the vehicle speed and traffic volume in the current tunnel are high, the light brightness and flashing frequency of each emergency parking strip will remain unchanged.

5. A tunnel emergency stop zone induction emergency system as claimed in claim 4, characterized in that: The method for the monitoring adjustment unit to determine the specific conditions of the vehicle speed and vehicle flow in the current tunnel is as follows: The average driving speed of the vehicles in the tunnel is calculated based on the driving speed of each vehicle; the monitoring and adjusting unit is configured with a speed threshold, and if the average driving speed is greater than the speed threshold, it is determined that the vehicle speed in the current tunnel is high, otherwise, it is determined that the vehicle speed in the current tunnel is low; The monitoring and adjustment unit is configured with a vehicle flow threshold; If the traffic volume is higher than the traffic volume threshold, it is determined that the current traffic volume in the tunnel is high; otherwise, it is determined that the current traffic volume in the tunnel is low.

6. A tunnel emergency stop zone induction emergency system as claimed in claim 5, characterized in that: The emergency indication module includes a lighting indication unit and a dimming drive unit; wherein the lighting indication unit includes an LED lamp installed in each emergency parking strip; the LED lamp is used to indicate the position of the corresponding emergency parking strip; The dimming drive unit includes a control device matched with each LED lamp; the control device is used to execute the emergency indication strategy and control the light brightness and light flashing frequency of the LED lamp of each emergency parking strip.

7. A tunnel emergency stop zone induction emergency system as claimed in claim 6, characterized in that: The analysis and decision module also includes an abnormality detection unit, which is used to detect abnormal vehicles; the abnormality detection unit is configured with a target detection algorithm and a multi-target tracking algorithm; the method for detecting abnormal vehicles is as follows: Continuously acquiring monitoring images of each vehicle in the tunnel from the video monitoring unit; Detecting vehicles from the monitoring image using a target detection algorithm; tracking and detecting each vehicle in continuous monitoring images using a multi-target tracking algorithm; Extract two frames of surveillance images to perform random inspection on any vehicle and calculate the random inspection speed of the vehicle; Continuously perform random inspections on each vehicle. If the difference between the random inspection speed and the average driving speed of any vehicle exceeds a preset error range in m consecutive random inspections, the vehicle is marked as a risky vehicle; Acquire a thermal image of the risk vehicle based on a thermal imaging unit; acquire the position coordinates of the risk vehicle; and acquire curvature data and slope data corresponding to the position coordinates of the risk vehicle; The anomaly detection unit is also configured with an anomaly detection model, which inputs the average driving speed and the sampling speed of any risky vehicle, the thermal imaging image, the curvature data and the slope data corresponding to the position coordinates of the risky vehicle into the anomaly detection model, and outputs the abnormal vehicle detection result.

8. A tunnel emergency stop zone induction emergency system as claimed in claim 7, characterized in that: The analysis and decision-making module also includes an emergency induction unit; the emergency induction unit is used to generate an emergency induction strategy; specifically includes: The position coordinates of the abnormal vehicle are obtained; the emergency parking strip closest to the abnormal vehicle in the direction of the abnormal vehicle's advance is selected as the induced parking strip; the emergency induced unit sends an emergency induced instruction to the dimming drive unit, and the dimming drive unit responds to the emergency induced instruction to increase the light brightness and light flashing frequency of the induced parking strip.

9. A tunnel emergency stop zone induction emergency system as claimed in claim 8, characterized in that: The emergency indication module also includes an induction identification unit; the emergency induction strategy also includes: the emergency induction unit sends a parking induction instruction to the induction identification unit; the induction identification unit responds to the parking induction instruction and performs emergency parking induction on the abnormal vehicle; the induction identification unit includes a display screen installed along the tunnel; the emergency parking induction for the abnormal vehicle includes: indicating the direction of the induction parking strip by an arrow on the display screen, and marking the distance of the induction parking strip from the display screen.

10. A tunnel emergency stop strip induction emergency method, which is implemented based on a tunnel emergency stop strip induction emergency system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collect environmental data inside the tunnel; Dividing the tunnel into different emergency sections based on the environmental data, and setting an emergency indication strategy for each emergency section; Collecting vehicle data in the tunnel; adjusting the emergency indication strategy based on the vehicle data; Provide emergency guidance for emergency parking strips in tunnels based on emergency indication strategies; Identify risky vehicles in tunnels based on vehicle data; Perform anomaly detection on risky vehicles and identify abnormal vehicles; The position coordinates of the abnormal vehicle are obtained; an emergency induction strategy is generated for the abnormal vehicle based on the position coordinates, and emergency parking induction is performed on the abnormal vehicle according to the emergency induction strategy.

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