Road safety monitoring method, device and equipment and storage medium

Abstract

The invention relates to a road safety monitoring method and device, equipment and a storage medium. During road safety monitoring, road detection information sent by an unmanned aerial vehicle needs to be acquired; the road detection information is information acquired by the vehicle in a detection range of a current driving path; detecting a target object from the road detection information; determining whether a road risk exists according to the road detection information; the road risk is the risk generated by the target object on the current driving path; if yes, generating a processing suggestion corresponding to the road risk; and generating warning information according to the processing suggestion. Therefore, road risk detection is carried out through the road detection information obtained by the unmanned aerial vehicle, various risks existing in the current driving path can be timely and comprehensively found, the safety of the road is improved, and the installation and maintenance cost is greatly reduced.

Description

Technical Field

[0001] The present application relates to the technical field of road safety monitoring, and in particular to a road safety monitoring method, device, equipment and storage medium. Background Art

[0002] Road safety monitoring technology is a technology field that has received much attention in recent years. Road safety monitoring technology refers to: installing monitoring equipment on the road, monitoring road conditions in real time, and promptly discovering and handling various problems on the road, such as natural disasters, weather and environmental impacts, encountering animals on the highway, etc., so as to improve the safety of road driving. However, in traditional road safety monitoring technology, it mainly relies on monitoring equipment such as cameras and sensors installed on the road to collect on-site information. However, these monitoring devices have problems such as inconvenient installation and high maintenance costs. In addition, the monitoring equipment cannot cover all roads, and it is impossible to conduct timely and comprehensive road safety monitoring.

[0003] Therefore, how to conduct road safety monitoring in a timely and comprehensive manner is a problem that those skilled in the art need to solve. Summary of the invention

[0004] The present application provides a road safety monitoring method, device, equipment and storage medium to enable timely and comprehensive road safety monitoring.

[0005] In a first aspect, the present application provides a road safety monitoring method, comprising:

[0006] Obtaining road detection information sent by the drone; the road detection information is information obtained by the vehicle within the detection range of the current driving path;

[0007] detecting a target object from the road detection information;

[0008] Determine whether there is a road risk according to the road detection information; the road risk is the risk generated by the target object on the current driving path;

[0009] If yes, generating treatment suggestions corresponding to the road risk;

[0010] A warning message is generated according to the processing suggestion.

[0011] Optionally, determining whether there is a road risk according to the road detection information includes:

[0012] Using the road detection information to determine whether the target object is in motion;

[0013] If yes, predicting the motion trajectory of the target object using the road detection information; and determining whether there is a first road risk according to the motion trajectory;

[0014] If not, determine whether there is a second road risk based on the road detection information.

[0015] Optionally, determining whether there is a road risk according to the road detection information includes:

[0016] Using the road detection information to detect whether the target object is an animal;

[0017] If so, the basic information of the animal is determined, and whether there is a third road risk is determined based on the basic information and the road detection information.

[0018] Optionally, the generating a processing suggestion corresponding to the road risk includes:

[0019] Generate a road switching suggestion; wherein the road switching suggestion includes a first driving path that avoids the road risk.

[0020] Optionally, after generating the target driving path to be switched, the method further includes:

[0021] Using the road detection information to detect whether the target object is an animal;

[0022] If so, repellent measures options are generated according to the basic information of the animal.

[0023] Optionally, generating a warning message according to the processing suggestion includes:

[0024] The road risk and the handling suggestion are displayed in a head-up display system and / or a display screen of the vehicle.

[0025] Optionally, after displaying the road risk and the handling suggestion, the method further includes:

[0026] Receive a selection instruction triggered by a user;

[0027] If the selection instruction is a path selection instruction, determining a second driving path corresponding to the path selection instruction, and switching the current driving path to the second driving path;

[0028] If the selection instruction is an avoidance measure selection instruction, the avoidance measure selection instruction is sent to the drone, so that the drone executes the avoidance measure corresponding to the avoidance measure selection instruction to drive the animal away from the current driving path.

[0029] In a second aspect, the present application provides a road safety monitoring device, comprising:

[0030] An acquisition module is used to acquire road detection information sent by the drone; the road detection information is information acquired by the vehicle within the detection range of the current driving path;

[0031] A first detection module, used to detect a target object from the road detection information;

[0032] A second detection module is used to determine whether there is a road risk according to the road detection information; the road risk is the risk generated by the target object on the current driving path; if yes, the first generation module is triggered;

[0033] A first generating module, used to generate a processing suggestion corresponding to the road risk;

[0034] The second generating module is used to generate warning information according to the processing suggestion.

[0035] In a third aspect, the present application provides an electronic device, including:

[0036] A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the road safety monitoring method of the present application through the computer program.

[0037] In a fourth aspect, the present application also provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions are used to execute the steps of the above-mentioned road safety monitoring method of the present application.

[0038] The above technical solution provided by the embodiment of the present application has the following advantages over the prior art: when monitoring road safety, the present application needs to obtain road detection information sent by the drone; the road detection information is information obtained by the vehicle within the detection range of the current driving path; the target object is detected from the road detection information; it is determined whether there is a road risk based on the road detection information; the road risk is the risk generated by the target object on the current driving path; if so, a processing suggestion corresponding to the road risk is generated; a warning message is generated based on the processing suggestion. It can be seen that the present application uses the road detection information obtained by the drone to perform road risk detection, which can timely and comprehensively discover various risks existing in the current driving path, improve road safety, and greatly reduce installation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0042] Figure 1 A schematic diagram of a road safety monitoring method flow chart provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of another road safety monitoring method flow chart provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram showing an embodiment of the present application;

[0045] Figure 4 A schematic diagram of the structure of a road safety monitoring device provided in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0048] The disclosure below provides many different embodiments or examples to implement different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0049] The present invention discloses a road safety monitoring method, device, equipment and storage medium to timely and comprehensively monitor road safety.

[0050] See also Figure 1 , Figure 1 A road safety monitoring method flow chart provided in an embodiment of the present application, the method specifically comprises the following steps:

[0051] S101, obtaining road detection information sent by the drone; the road detection information is information obtained by the vehicle within the detection range of the current driving path;

[0052] It should be noted that drone remote sensing technology is a means of collecting spatial remote sensing information using drones. It has the advantages of fast collection speed, wide coverage, and low cost, and can obtain high-resolution remote sensing images and videos in real time. At the same time, with the development of artificial intelligence and machine learning technology, drone remote sensing technology has also made great breakthroughs in data processing, modeling, and analysis, and can realize automated and intelligent remote sensing information acquisition and analysis. Therefore, in this application, in order to monitor road safety in a timely and comprehensive manner, drone remote sensing technology can be used to obtain road detection information obtained by the vehicle within the detection range of the current driving path, realize real-time monitoring of road conditions, and promptly discover and deal with various problems on the road.

[0053] When obtaining road detection information, the drone can use the sensors carried by the drone to obtain road detection information. The sensors can be cameras, LiDAR (Light Laser Detection and Ranging) and other environmental sensing devices. When obtaining road detection information, the detection cycle and detection range can be set. The detection cycle refers to the cycle for obtaining road detection information. The detection range is the detection range determined according to the current driving path. The detection range can be a detection range centered on the vehicle or a detection range starting from the vehicle. It is not specifically limited here. For example, if the detection cycle is set to 5 minutes and the detection range is set to 3 kilometers, the drone will obtain road detection information every 5 minutes. Its detection range is the road range within 3 kilometers along the current driving path starting from the current position of the vehicle.

[0054] Specifically, the road detection information in this embodiment specifically includes road condition information and animal activity information collected in real time. The road condition information can specifically be traffic signs on the road, obstacles on the road, etc. Traffic signs can be speed limit signs, no parking signs, etc., which are helpful to judge the current road restrictions and regulations; obstacles on the road can specifically be other vehicles, pedestrians, bicycles, falling rocks, etc. Animal activity information refers to detected animals, including animal appearance, animal behavior, animal activity range, etc. The types of road detection information can specifically be: picture data, video data, temperature data, humidity data, sound data, etc., which are not specifically limited here.

[0055] After the application obtains the road detection information, the road detection information can be transmitted to the vehicle through the wireless communication module of the drone and the wireless communication module of the vehicle. Among them, the wireless communication module can specifically be a 4G / 5G module, so as to realize real-time data transmission and control command transmission between the drone and the vehicle, ensure that emergencies can be handled in time, and improve the safety of road driving. In addition, the drone is equipped with a GPS (Global Positioning System) module for precise positioning and automatic adjustment of the position when necessary to obtain clearer images and data. Furthermore, the drone in this application can also be automatically maintained through an automatic detection and diagnosis system, including battery charging, sensor calibration, etc., which can greatly reduce maintenance costs and extend the service life of the drone.

[0056] S102, detecting a target object from road detection information;

[0057] It should be noted that the target objects detected by the present application from the road detection information can be: vehicles, people, bicycles, animals, etc., which are not specifically limited here. Here, the detection process is explained by taking the road detection information as an image as an example. The detection process includes four parts: feature extraction, target detection, semantic segmentation and instance segmentation; wherein, feature extraction is used to extract features from the image, such as color, texture, shape, etc.; target detection is to use deep learning technologies such as Graph Convolution Neural Networks (GCN) to identify specific objects in the image, such as vehicles, pedestrians or animals; semantic segmentation is to divide the image into different areas and mark the category labels for each area to help determine the road and non-road areas; instance segmentation is to further distinguish different individuals in the same category on the basis of semantic segmentation to determine the specific object to be tracked.

[0058] Furthermore, when identifying a road, the present application can specifically separate the road from the surrounding environment through image processing techniques, such as edge detection, contour extraction, color segmentation, etc. At the same time, the present application can also use machine learning algorithms to learn and classify the characteristics of the road to improve the accuracy of recognition. The present application can also use image processing techniques, such as template matching, character segmentation, feature extraction, etc., to identify traffic signs on the road.

[0059] S103, determining whether there is a road risk according to the road detection information; the road risk is the risk generated by the target object on the current driving path;

[0060] If yes, execute S104; if no, continue to execute S101;

[0061] In this embodiment, the road risk determined according to the road detection information can specifically be the risk of road congestion caused by the target object, the risk of collision between the target object and the vehicle, and so on. When the present application determines whether there is a road risk through road detection information, different road risks can be detected according to the type of target object. For example: if the target object is a vehicle, then it can be seen from the image information in the road detection information that there are many vehicles on the road ahead and congestion has occurred, so it is determined that there is a risk of road congestion; if the target object is a child running on the side of the road, since children are unstable, it is easy for them to cross the road while running and playing, so it is determined that there is a risk of collision; if the target object is a speed limit sign, then it can be determined whether there is a risk of speeding in combination with the vehicle speed.

[0062] S104, generating treatment suggestions corresponding to road risks;

[0063] It is understandable that the processing suggestions of this application need to be generated according to a pre-set processing strategy, which sets the factors to be considered in generating the processing suggestions, so as to generate the final processing suggestions based on various factors. The factors to be considered may specifically include the type of road risk, the degree of risk, whether to allow the route to be changed, etc. For example: if the road risk is a road congestion risk, and the processing strategy allows the route to be changed, then the generated processing suggestion may be: it is recommended to change to a new route and generate multiple new routes for the user to choose; if the road risk is a collision risk, the risk level is high, but the route change is not allowed, then the generated processing suggestion may be: it is recommended to slow down.

[0064] S105. Generate warning information according to the processing suggestion.

[0065] In this embodiment, the warning information is used to alert the user in the car that there is a road risk ahead. The warning information may include road risks and handling suggestions. The warning information specifically includes at least one implementation form such as voice, text, picture, video, etc. For example: if a collision risk is detected 3 kilometers ahead through analysis of road detection information, the user can be reminded by voice that there is a collision risk 3 kilometers ahead, and it is recommended to change the driving route. A picture of the 3 kilometers ahead and the recommended change of driving route are displayed on the display screen for the user to choose.

[0066] From the above, it can be seen that when monitoring road safety, this application needs to obtain road detection information sent by drones; the road detection information is information obtained by the vehicle within the detection range of the current driving path; detect the target object from the road detection information; determine whether there is a road risk based on the road detection information; the road risk is the risk generated by the target object on the current driving path; if so, generate processing suggestions corresponding to the road risk; generate warning information based on the processing suggestions. It can be seen that this application uses the road detection information obtained by drones to perform road risk detection, which can timely and comprehensively discover various risks existing in the current driving path, improve road safety, and greatly reduce installation and maintenance costs.

[0067] Based on the above method embodiment, in this embodiment, when determining whether there is a road risk based on the road detection information, the determination can be made through the following two aspects:

[0068] 1. Use road detection information to determine whether the target object is in motion;

[0069] If so, the motion trajectory of the target object is predicted using the road detection information; whether a first road risk exists is determined based on the motion trajectory; if not, whether a second road risk exists is determined based on the road detection information.

[0070] In this embodiment, the target object on the road can be identified by image processing technology, and the properties and behaviors of the target object can be predicted and analyzed in combination with machine learning algorithms. Among them, the image processing technology identification can specifically be methods such as target detection, background subtraction, and motion analysis. When judging whether the target object is in motion, the present application can detect the moving target object by comparing the differences between consecutive frames, and predict the future motion trajectory of the target object based on the historical position and speed, and identify the road risk by analyzing the motion trajectory. In this embodiment, the road risk of the target object in motion is called the first road risk, which can be a collision risk, a road congestion risk, etc., which is not specifically limited here. For example: if there is a motorcycle with a faster speed behind the vehicle, the target object is a motorcycle at this time. At this time, the motion trajectory of the motorcycle can be predicted according to the driving speed and driving direction of the motorcycle. If the motion trajectory of the motorcycle is likely to collide with the vehicle, it can be judged that there is a collision risk.

[0071] It should be noted that if the target object is stationary, it can be determined whether there is a second road risk based on the road detection information. This application refers to the road risk of a stationary target object as the second road risk. For example, if there are a lot of fallen rocks in a curve with poor visibility in front of the vehicle, the fallen rocks are the stationary target object. Through detection, it is found that the range of the fallen rocks is large and the vehicle cannot pass. At this time, it can be determined that there is a risk of road congestion.

[0072] Second, using road detection information to detect whether the target object is an animal;

[0073] If so, the basic information of the animal is determined, and based on the basic information and the road detection information, it is determined whether there is a third road risk.

[0074] In this embodiment, animals on the road can be identified by image processing technology, which can be methods such as target detection, feature extraction, and classifier training; and then whether there is a third road risk is determined based on the basic information of the animal. In this embodiment, the basic information refers to the basic information of the animal, such as: the type of animal, protection type, natural enemy, likely behavior, running speed, whether it is aggressive, etc. The basic information of the animal combined with the road detection information can be used to determine whether there is a road risk. For example: the target object is an animal, and after identification, it is found that the animal is a deer, and the basic information records that the deer will suddenly run after being frightened, and the road detection information detects that the deer is close to the vehicle. At this time, it can be determined that there is a collision risk, which refers to the risk of collision between the deer and the vehicle; however, if the road detection information detects that the deer is far away from the road, and according to the running speed of the deer and the driving speed of the vehicle, it is calculated that even if the deer runs, it cannot collide with the vehicle, then it can be determined that there is no collision risk.

[0075] From the above, it can be seen that after the target object is detected through the road detection information, the application can determine whether there is a road risk according to the running state and type of the target object and the road detection information; if there is a road risk, an alarm message is generated in time to remind the driver. In this way, the road safety can be improved.

[0076] See also Figure 2 , Figure 2 A schematic flow chart of another road safety monitoring method provided in an embodiment of the present application, the method specifically comprises the following steps:

[0077] S201, obtaining road detection information sent by the drone; the road detection information is information obtained by the vehicle within the detection range of the current driving path;

[0078] S202, detecting a target object from road detection information;

[0079] S203, determining whether there is a road risk according to the road detection information; the road risk is the risk generated by the target object on the current driving path;

[0080] If yes, execute S204; if no, continue to execute S201;

[0081] S204, generating a road switching suggestion; wherein the road switching suggestion includes a first driving path that avoids road risks;

[0082] S205, using the road detection information to detect whether the target object is an animal;

[0083] If yes, execute S206; if no, execute S207;

[0084] S206, generating repellent measures options according to basic information of the animal;

[0085] S207: Display the road risk and the handling suggestions on the head-up display system and / or display screen of the vehicle.

[0086] In this embodiment, if it is determined that there is a road risk according to the road detection information, a processing suggestion corresponding to the road risk can be generated. In this embodiment, the processing suggestion includes a road switching suggestion, which is used to suggest that the user avoid the road risk by switching to a new path. In this embodiment, the generated new path is called a first driving path, and the number of generated first driving paths can be multiple, so that the user can choose.

[0087] It should be noted that the animal repellent technology in the traditional scheme is mainly achieved through physical isolation, sonic repellent and other means. For example: setting up a fence next to the highway to prevent animals from entering the highway; or setting up a sonic device next to the highway to emit high-frequency sound waves to repel animals. However, these methods often have poor effects and cannot fundamentally solve the problem. Therefore, in this application, if it is detected that the road risk is caused by animals, when generating processing suggestions, targeted repellent measures options can be generated based on the basic information of the animals. The repellent measures options refer to the measures options used to repel animals from hiding. For example: in the basic information of the animal, it is recorded that the type of animal is deer. The repellent measures options generated at this time can be: releasing deer repellent potion, emitting ultrasonic waves, emitting the roar of animal natural enemies (such as lions and tigers), etc., to achieve efficient animal repellent.

[0088] Furthermore, after the present application generates the above-mentioned processing suggestions, a warning message can also be generated according to the processing suggestions. In this embodiment, the processing suggestions can be specifically displayed in the head-up display system and / or display screen of the vehicle so as to warn the user in time and let the user choose the corresponding processing suggestions. Among them, the head-up display system is specifically a HUD (Head Up Display). The head-up display system can project important driving information such as speed and navigation onto the windshield in front of the driver, so that the driver can see important driving information such as speed and navigation without lowering his head or turning his head as much as possible. Therefore, the present application displays road risks and processing suggestions on the head-up display system, which allows the driver to obtain the required information more conveniently and improve the driving experience. For example: there is a deer in front of the road. At this time, it is determined through analysis that there is a collision risk. At this time, the relevant information can be displayed in a picture-in-picture manner on the HUD. The displayed information specifically includes: the 3D model (3Dimensions, three-dimensional model) of the animal, the animal species, the distance between the animal and the vehicle, the existing road risks, and processing suggestions: switch road suggestions and avoidance measures options. Furthermore, the warning information generated by the present embodiment may also be in voice form, alerting the driver through voice that there are road risks on the road ahead, as well as corresponding handling suggestions.

[0089] It should be noted that after the application displays the road risk and treatment suggestions, it is also necessary to receive the selection instruction triggered by the user; if the selection instruction is a path selection instruction, the second driving path corresponding to the path selection instruction is determined, and the current driving path is switched to the second driving path; if the selection instruction is an avoidance measure selection instruction, the avoidance measure selection instruction is sent to the drone, so that the drone executes the avoidance measure corresponding to the avoidance measure selection instruction to drive the animal away from the current driving path. Among them, the path selection instruction can be a selection instruction triggered by the user through the treatment suggestion displayed on the touch screen, or it can be a selection instruction output by the user in the form of voice, or it can be a selection instruction triggered by the user through a gesture, which is not specifically limited here. The user can choose which treatment suggestion to execute according to actual needs. If the user triggers the path selection instruction, the second driving path to be switched can be determined according to the path selection instruction, and the current driving path can be switched to the second driving path. For example: the switching road suggestion includes path 1, path 2 and path 3, and the user can directly issue a voice "switch path 2" to trigger the instruction to select path 2, so the second driving path to be switched determined by the instruction is path 2, and path 2 is used as the current driving path.

[0090] If the user chooses to trigger the repellent measure selection instruction, the repellent measure selection instruction needs to be sent to the drone. The instruction includes the repellent measure selected by the user, and the selected repellent measure can be one or more. After the drone receives the repellent measure selection instruction, it needs to execute the repellent measure corresponding to the repellent measure selection instruction to drive the animal away from the current driving path. For example, the repellent measure options include: 1: Release deer repellent potion, 2: Emit ultrasonic waves, and 3: Emit the roar of the animal's natural enemy. At this time, the user selects option 1 and option 2, and the selection instruction is sent to the drone. The drone can release deer repellent potion and emit ultrasonic waves to repel animals.

[0091] Here, a specific road safety monitoring method flow is provided. In this embodiment, a drone of the DJI Mavic Pro model is specifically selected, and the camera resolution carried by the drone is 4K, the flight altitude can reach 6000 meters, and the flight time can reach up to 27 minutes. The drone is equipped with a GPS module for precise positioning. Wireless communication modules are installed on the drone and the vehicle respectively to realize real-time data transmission and control command transmission between the two. The drone is deployed above the road and precise positioning is achieved through GPS navigation. The drone cruises every 5 minutes, and each cruise covers a range of 3 kilometers. When the drone battery reaches 20%, it returns to the top of the vehicle for wireless charging. During the cruise, the drone collects road conditions and animal activity information in real time through the cameras and sensors carried by the drone, and transmits these road detection information to the vehicle in real time through the wireless communication module. After the vehicle receives the data transmitted by the drone, it automatically processes it through the built-in intelligent algorithm, including image recognition, modeling and analysis. These algorithms can quickly identify road risks and animal morphology, and match corresponding avoidance measures.

[0092] Specifically, if the drone detects an animal in front of it, it collects image data or video data and sends it to the vehicle. The vehicle then performs the following processing using an intelligent algorithm:

[0093] 1. Data preprocessing: Preprocessing specifically includes filtering noise and format conversion. Since drone photography may encounter problems such as lighting changes and occlusions, it is necessary to perform image enhancement, denoising and other operations on the data. The raw data should be converted into an easy-to-process format, such as a common image or video file format.

[0094] 2. Feature extraction: Use computer vision technology to identify the outline, texture and other key features of the animal. In this embodiment, OpenCV (Open Source Computer Vision Library, image and video processing library) can be used for image processing; and deep learning methods can be used to extract features in the image. In this embodiment, features can be extracted through Convolutional Neural Networks (CNN).

[0095] 3. Model training: In this embodiment, labeled animal images are used to train an animal recognition model, which can accurately identify different types of animals and estimate their positions, postures, etc.; the animal recognition model can specifically be a classifier or a detector.

[0096] 4. 3D modeling and rendering (Blender creates 3D models): After identifying a specific animal through the above steps, you can choose a suitable model based on the existing 3D model library, or create a new 3D model using point cloud data and structured light technology. Use rendering technology to place the 3D model in the actual environment to make it look realistic.

[0097] 5. Display: Integrate the animal’s 3D model, basic information about the animal, road hazards, and handling suggestions into virtual reality (VR) or display them directly on the HUD.

[0098] 6. Interactivity: Users can select and execute corresponding processing suggestions through touch screen, gesture recognition or voice commands.

[0099] See also Figure 3 , Figure 3 A schematic diagram showing an embodiment of the present application; Figure 3 It can be seen that this application specifically displays the type, distance, protection type, size, natural enemy, and avoidance measures of animals on the HUD, and the HUD displays the 3D model of the animal, the avoidance route, and the vehicle driving information. At the same time, it will also prompt through voice and the central control display: Animals are found 2 kilometers ahead, please slow down and drive carefully; after playing the above information, it can also prompt through voice and the central control display: It is recommended to select avoidance measure 1 to change the route. If the user triggers the instruction to select avoidance measure 1 at this time, the path will be changed; if the user triggers other solutions at this time, it will remind again: You can change the route to avoid, please change the route. If there is no suitable avoidance route, the user can choose avoidance measures 2 and avoidance measures 3 at this time, and let the drone release deer repellent potion and ultrasonic repellent. Among them, the avoidance measures of this application can be used in combination according to specific circumstances to improve the avoidance effect.

[0100] From the above, it can be seen that in the case of road safety monitoring, the present application can obtain high-resolution remote sensing images and videos of the road in real time through the UAV remote sensing technology, so as to timely discover and deal with various problems on the road, such as natural disasters, animals, etc., thereby improving the safety of road driving. Compared with traditional equipment such as cameras and sensors, UAV remote sensing technology has the advantages of fast acquisition speed, wide coverage, and low cost, so it has broad application prospects in the field of road safety monitoring. Secondly, when the present application is animal repellent, the UAV is used to identify road risks and animal morphology, and to match corresponding repellent measures, such as deer repellent potion, ultrasound, and the roar of animal natural enemies, thereby effectively solving the problems that traditional animal repellent methods are not effective and cannot fundamentally solve the problem. Finally, this case realizes the automatic and intelligent rapid acquisition of spatial remote sensing information, completes remote sensing data processing, modeling and analysis, and displays it on the vehicle HUD, thereby improving the application efficiency and accuracy of UAV remote sensing technology. This technical solution realizes efficient and accurate road safety monitoring and animal repellent through the combination of UAV remote sensing technology, road safety monitoring technology and animal repellent system technology.

[0101] See also Figure 4 , Figure 4 A schematic diagram of the structure of a road safety monitoring device provided in an embodiment of the present application, the device specifically comprises:

[0102] The acquisition module 11 is used to acquire the road detection information sent by the drone; the road detection information is the information acquired by the vehicle within the detection range of the current driving path;

[0103] A first detection module 12, configured to detect a target object from the road detection information;

[0104] The second detection module 13 is used to determine whether there is a road risk according to the road detection information; the road risk is the risk generated by the target object on the current driving path; if so, the first generation module is triggered;

[0105] A first generating module 14, configured to generate a processing suggestion corresponding to the road risk;

[0106] The second generating module 15 is used to generate warning information according to the processing suggestion.

[0107] As an optional embodiment, the second detection module includes:

[0108] A first judgment unit, configured to use the road detection information to judge whether the target object is in motion; if so, trigger the prediction unit; if not, trigger the third judgment unit;

[0109] A prediction unit, configured to predict a motion trajectory of the target object using the road detection information;

[0110] a second judgment unit, configured to judge whether there is a first road risk according to the motion trajectory;

[0111] The third judgment unit is used to judge whether there is a second road risk according to the road detection information.

[0112] As an optional embodiment, the second detection module includes:

[0113] A first detection unit, configured to detect whether the target object is an animal using the road detection information; if so, triggering a determination unit;

[0114] A determination unit, used to determine basic information of the animal;

[0115] The fourth judgment unit is used to judge whether there is a third road risk according to the basic information and the road detection information.

[0116] As an optional embodiment, the first generating module includes:

[0117] The first generating unit is used to generate a road switching suggestion; wherein the road switching suggestion includes a first driving path that avoids the road risk.

[0118] As an optional embodiment, the first generating module further includes:

[0119] A second detection unit, configured to detect whether the target object is an animal using the road detection information; if so, triggering a second generation unit;

[0120] The second generating unit is used to generate repelling measure options according to the basic information of the animal.

[0121] As an optional embodiment, the second generating module is specifically used to: display the road risk and the handling suggestion in a head-up display system and / or a display screen of the vehicle.

[0122] As an optional embodiment, the device further includes:

[0123] A receiving module, configured to receive a selection instruction triggered by a user; if the selection instruction is a path selection instruction, the determining module is triggered; if the selection instruction is an avoidance measure selection instruction, the sending module is triggered;

[0124] A determination module, used to determine a second driving path corresponding to the path selection instruction, and switch the current driving path to the second driving path;

[0125] The sending module is used to send the avoidance measure selection instruction to the drone, so that the drone executes the avoidance measure corresponding to the avoidance measure selection instruction to drive the animal away from the current driving path.

[0126] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0127] See also Figure 5 , Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device specifically includes:

[0128] A processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. The processor 21 executes the steps of the road safety monitoring method described in any of the above method embodiments through the computer program.

[0129] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0130] The memory 22 may include one or more computer-readable storage media, which may be non-transitory. The memory 22 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 22 is at least used to store the following computer program 221, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps in the road safety monitoring method disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 22 may also include an operating system 222 and data 223, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 222 may include Windows, Unix, Linux, etc.

[0131] In some embodiments, the electronic device may further include a display screen 23 , an input / output interface 24 , a communication interface 25 , a sensor 26 , a power source 27 , and a communication bus 28 .

[0132] certainly, Figure 5 The structure of the electronic device shown does not constitute a limitation on the electronic device in the embodiments of the present application. In actual applications, the electronic device may include Figure 5 More or fewer components than shown, or combinations of certain components.

[0133] In another exemplary embodiment, a computer storage medium is also provided, and when the program instructions are executed by a processor, the steps of the road safety monitoring method described in any of the above method embodiments are implemented. The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0134] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0135] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0136] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A road safety monitoring method, characterized in that: include: Get the road detection information sent by the drone; The road detection information is information obtained by the vehicle within the detection range of the current driving path; detecting a target object from the road detection information; Determine whether there is a road risk according to the road detection information; the road risk is the risk generated by the target object on the current driving path; If yes, generating treatment suggestions corresponding to the road risk; A warning message is generated according to the processing suggestion.

2. The road safety monitoring method according to claim 1, characterized in that: The determining whether there is a road risk according to the road detection information includes: Using the road detection information to determine whether the target object is in motion; If yes, predicting the motion trajectory of the target object using the road detection information; and determining whether there is a first road risk according to the motion trajectory; If not, determine whether there is a second road risk based on the road detection information.

3. The road safety monitoring method according to claim 1, characterized in that: The determining whether there is a road risk according to the road detection information includes: Using the road detection information to detect whether the target object is an animal; If so, the basic information of the animal is determined, and whether there is a third road risk is determined based on the basic information and the road detection information.

4. The road safety monitoring method according to claim 1, characterized in that: The generating a processing suggestion corresponding to the road risk includes: Generate a road switching suggestion; wherein the road switching suggestion includes a first driving path that avoids the road risk.

5. The road safety monitoring method according to claim 4, characterized in that: After the target driving path to be switched is generated, the method further includes: Using the road detection information to detect whether the target object is an animal; If so, repellent measures options are generated according to the basic information of the animal.

6. The road safety monitoring method according to any one of claims 1 to 5, characterized in that: The generating of warning information according to the processing suggestion includes: The road risk and the handling suggestion are displayed in a head-up display system and / or a display screen of the vehicle.

7. The road safety monitoring method according to claim 6, characterized in that: After displaying the road risk and the treatment suggestion, the method further includes: Receive a selection instruction triggered by a user; If the selection instruction is a path selection instruction, determining a second driving path corresponding to the path selection instruction, and switching the current driving path to the second driving path; If the selection instruction is an avoidance measure selection instruction, the avoidance measure selection instruction is sent to the drone, so that the drone executes the avoidance measure corresponding to the avoidance measure selection instruction to drive the animal away from the current driving path.

8. A road safety monitoring device, characterized in that: include: The acquisition module is used to obtain the road detection information sent by the drone; The road detection information is information obtained by the vehicle within the detection range of the current driving path; A first detection module, used to detect a target object from the road detection information; A second detection module is used to determine whether there is a road risk according to the road detection information; the road risk is the risk generated by the target object on the current driving path; If yes, trigger the first generation module; A first generating module, used to generate a processing suggestion corresponding to the road risk; The second generating module is used to generate warning information according to the processing suggestion.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the road safety monitoring method described in any one of claims 1 to 7 of the present application through the computer program.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the steps of the road safety monitoring method described in any one of claims 1 to 7 of the present application.

Images