Method, apparatus and electronic device for processing preceding vehicle data based on YTS system

Abstract

The present application provides a method, apparatus and electronic device for processing data of a vehicle ahead based on a YTS system, relating to the field of vehicle technologies, and solving the technical problem of low accuracy in the judgment result of the upcoming driving speed of the vehicle ahead. The method includes: if the vehicle ahead is a gasoline vehicle, using an infrared imaging device to identify the real-time temperature at the exhaust pipe of the vehicle ahead, and determining heat change data of the exhaust gas emission heat of the vehicle ahead changing with time according to the real-time temperature; analyzing the throttle operation degree of the vehicle ahead through an AI algorithm according to the heat change data; predicting upcoming driving speed change data of the vehicle ahead according to the throttle operation degree; and controlling its own vehicle-mounted terminal to send a first prompt message about the predicted situation of the upcoming speed change of the vehicle ahead based on the upcoming driving speed change data of the vehicle ahead.

Description

Technical Field

[0001] The present application relates to the field of vehicle technologies, and in particular, to a method, apparatus, and electronic device for processing data of a vehicle ahead based on a YTS system. Background Art

[0002] Currently, a vehicle-mounted terminal can identify whether a vehicle ahead is lit based on an image of the vehicle ahead collected by an image acquisition device, and then determine whether the vehicle ahead is about to decelerate according to whether the vehicle ahead is lit.

[0003] However, only judging whether the vehicle ahead is about to decelerate based on the lighting condition of the vehicle ahead can only judge the situation where the vehicle ahead decelerates by braking, and cannot determine the situation where the vehicle ahead decelerates by other means and the specific speed change situation of the vehicle ahead. Moreover, if the rear lights of the vehicle ahead are damaged and cannot be lit, it is easy to cause misjudgment of the speed of the vehicle ahead, all of which will result in a low accuracy of the judgment result of the upcoming driving speed of the vehicle ahead. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, apparatus, and electronic device for processing data of a vehicle ahead based on a YTS system to solve the technical problem of low accuracy of the judgment result of the upcoming driving speed of the vehicle ahead.

[0005] In a first aspect, the present application provides a method for processing data of a vehicle ahead based on a YTS system. An infrared imaging device is provided on the vehicle-mounted terminal itself, and the method includes:

[0006] Obtain an image of the vehicle ahead corresponding to the vehicle-mounted terminal itself through the infrared imaging device;

[0007] During the rendering process of the image of the vehicle ahead by the YTS system of the vehicle-mounted terminal itself, identify the license plate image in the image of the vehicle ahead to obtain a license plate recognition result, and judge whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result;

[0008] If the vehicle ahead is a gasoline vehicle, use the infrared imaging device to identify the real-time temperature at the exhaust pipe of the vehicle ahead, and determine the heat change data of the exhaust gas emission heat of the vehicle ahead changing with time according to the real-time temperature;

[0009] Analyze the throttle operation degree of the vehicle ahead through an AI algorithm according to the heat change data; wherein, the greater the degree of heat increase change in the heat change data, the greater the throttle operation degree;

[0010] Predict the upcoming driving speed change data of the vehicle ahead according to the throttle operation degree; when the throttle operation degree increases and the vehicle ahead is about to perform an accelerating driving action, the corresponding upcoming driving speed change data is an increase in speed; when the throttle operation degree decreases and the vehicle ahead is about to perform a decelerating driving action, the corresponding upcoming driving speed change data is a decrease in speed; when the throttle operation degree remains unchanged and the vehicle ahead is about to perform a constant-speed driving action or a decelerating driving action, the corresponding upcoming driving speed change data is an unchanged speed or a decrease in speed.

[0011] Control the own vehicle-mounted terminal to send out a first prompt message about the predicted upcoming speed change of the vehicle ahead based on the upcoming driving speed change data of the vehicle ahead.

[0012] In a possible implementation, a wind speed and direction meter is also provided on the own vehicle-mounted terminal; after determining the heat change data of the exhaust gas emission heat of the vehicle ahead changing with time according to the real-time temperature, it further includes:

[0013] If the degree of heat reduction change in the heat change data is greater than a preset cooling degree, it is determined that the current throttle operation degree belongs to a completely unoperated throttle, and the ground image of the location where the vehicle ahead is located is obtained through the infrared imaging device, and the current wind speed and direction data is obtained through the wind speed and direction meter.

[0014] Identify the ground texture data corresponding to the driving of the vehicle ahead based on the ground image, identify the vehicle model of the vehicle ahead based on the vehicle ahead image, and determine the vehicle weight of the vehicle ahead according to the vehicle model.

[0015] Analyze the comprehensive driving resistance value of the vehicle ahead according to the running resistance value of the vehicle weight relative to the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and direction data relative to the vehicle ahead.

[0016] In the case where the vehicle ahead remains in the state of completely unoperated throttle, predict the vehicle stop driving time of the vehicle ahead according to the current driving speed and the comprehensive driving resistance value of the vehicle ahead, and control the own vehicle-mounted terminal to send out a warning message about the vehicle stop driving time to avoid a rear-end collision between the own vehicle-mounted terminal and the vehicle ahead.

[0017] In a possible implementation, the vehicle ahead image includes the video data of the vehicle ahead; after identifying the real-time temperature at the exhaust pipe of the vehicle ahead by using the infrared imaging device, it further includes:

[0018] Identify the historical vehicle speed change data of the vehicle ahead based on the video data;

[0019] Determine the historical vehicle speed change data and the real-time temperature at the exhaust pipe corresponding to the previous moment as the first training sample, and train the initial binary regression analysis model based on the first training sample to obtain the first vehicle speed operation rule model of the vehicle ahead; wherein, the first vehicle speed operation rule model is used to characterize the corresponding relationship between the real-time temperature at the exhaust pipe of the vehicle ahead and the vehicle speed change data at the next moment;

[0020] According to the real-time temperature at the exhaust pipe at the current moment, predict the first next moment vehicle speed change data corresponding to the next moment of the current moment through the first vehicle speed operation rule model;

[0021] Control the own vehicle-mounted terminal to send out a second prompt message about the predicted situation of the vehicle speed change of the vehicle ahead at the next moment based on the first next moment vehicle speed change data.

[0022] In a possible implementation, after predicting the upcoming driving speed change data of the vehicle ahead according to the throttle operation degree, it further includes:

[0023] Analyze the vehicle speed adaptation range of the own vehicle-mounted terminal for the vehicle ahead through an AI algorithm according to the upcoming driving speed change data of the vehicle ahead;

[0024] Control the own vehicle to send out a third prompt message about the current vehicle speed suggestion based on the vehicle speed adaptation range.

[0025] In a possible implementation, the image of the vehicle ahead includes the video data of the vehicle ahead; after the step of judging whether the vehicle ahead belongs to a gasoline vehicle or an electric vehicle according to the license plate recognition result, it further includes:

[0026] In the case that the vehicle ahead is an electric vehicle, use the infrared imaging device to obtain the image at the tire of the vehicle ahead, and use the infrared imaging device to identify the current climate condition;

[0027] If it is identified that the current climate condition belongs to a rain or snow climate, identify the splashing degree of water or snow between the tire of the vehicle ahead and the ground based on the image at the tire;

[0028] If it is identified that the current climate condition belongs to a non-rain or snow climate, identify the dust generation degree between the tire of the vehicle ahead and the ground based on the image at the tire;

[0029] Identify the real-time vehicle speed change data of the vehicle ahead based on the video data, and determine the dust generation degree or the splashing degree and the real-time vehicle speed change data corresponding to the next moment as the second training sample, and train the initial multiple regression analysis model based on the second training sample to obtain the second vehicle speed operation law model of the vehicle ahead; wherein, the second vehicle speed operation law model is used to characterize the corresponding relationship between the dust generation degree or the splashing degree of the vehicle ahead and the real-time vehicle speed change data corresponding to the next moment;

[0030] Predict the second next moment vehicle speed change data corresponding to the current moment through the second vehicle speed operation law model according to the dust generation degree or the splashing degree at the current moment;

[0031] Control the own vehicle-mounted terminal to send out the fourth prompt information about the predicted situation of the vehicle speed change of the vehicle ahead at the next moment based on the second next moment vehicle speed change data.

[0032] In a possible implementation, a wind speed and direction meter is further arranged on the own vehicle-mounted terminal; the predicting the upcoming driving speed change data of the vehicle ahead according to the throttle operation degree includes:

[0033] When the throttle operation degree remains unchanged, obtain the ground image of the ground where the vehicle ahead is located through the infrared imaging device, and obtain the current wind speed and direction data through the wind speed and direction meter;

[0034] Identify the ground texture data corresponding to the driving of the vehicle ahead based on the ground image, identify the vehicle model of the vehicle ahead based on the vehicle image of the vehicle ahead, and determine the vehicle weight of the vehicle ahead according to the vehicle model;

[0035] If the sum of the vehicle weight relative to the running resistance value of the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and direction data relative to the vehicle ahead is greater than the preset resistance value, then predict that the vehicle ahead is about to perform a decelerating driving action, and determine the corresponding upcoming driving speed change data as a speed decrease.

[0036] In a possible implementation, after identifying the ground texture data corresponding to the driving of the vehicle ahead based on the ground image, identifying the vehicle model of the vehicle ahead based on the vehicle image of the vehicle ahead, and determining the vehicle weight of the vehicle ahead according to the vehicle model, it further includes:

[0037] If the vehicle weight is less than or equal to the preset resistance value with respect to the sum of the running resistance value of the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and direction data with respect to the vehicle ahead, it is predicted that the vehicle ahead is about to perform a constant-speed driving action, and the corresponding upcoming driving speed change data is determined to be that the speed remains unchanged.

[0038] In a second aspect, the present application provides a vehicle-ahead data processing device based on the YTS system. An infrared imaging device is provided on its own vehicle-mounted terminal. The device includes:

[0039] An acquisition module for acquiring an image of the vehicle ahead corresponding to its own vehicle-mounted terminal through the infrared imaging device;

[0040] A judgment module for identifying a license plate image in the vehicle-ahead image during the rendering process of the vehicle-ahead image by the YTS system of its own vehicle-mounted terminal, obtaining a license plate recognition result, and judging whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result;

[0041] An identification module for, if the vehicle ahead is a gasoline vehicle, using the infrared imaging device to identify the real-time temperature at the exhaust pipe of the vehicle ahead, and determining heat change data of the exhaust gas emission heat of the vehicle ahead changing with time according to the real-time temperature;

[0042] An analysis module for analyzing the throttle operation degree of the vehicle ahead through an AI algorithm according to the heat change data; wherein, the greater the degree of heat increase change in the heat change data, the greater the throttle operation degree;

[0043] A prediction module for predicting upcoming driving speed change data of the vehicle ahead according to the throttle operation degree; when the throttle operation degree increases, corresponding to the vehicle ahead about to perform an acceleration driving action, the corresponding upcoming driving speed change data is that the speed increases; when the throttle operation degree decreases, corresponding to the vehicle ahead about to perform a deceleration driving action, the corresponding upcoming driving speed change data is that the speed decreases; when the throttle operation degree remains unchanged, corresponding to the vehicle ahead about to perform a constant-speed driving action or a deceleration driving action, the corresponding upcoming driving speed change data is that the speed remains unchanged or the speed decreases;

[0044] A sending module for controlling its own vehicle-mounted terminal to send a first prompt message about the predicted upcoming speed change of the vehicle ahead based on the upcoming driving speed change data of the vehicle ahead.

[0045] In a third aspect, the present application further provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the method described in the first aspect above is implemented.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the method described in the first aspect above.

[0047] The present application brings the following beneficial effects:

[0048] A method, device and electronic device for processing preceding vehicle data based on the YTS system provided by the present application can obtain a preceding vehicle image of a vehicle in front corresponding to its own vehicle-mounted terminal through the infrared imaging device; during the rendering process of the preceding vehicle image by the YTS system of its own vehicle-mounted terminal, identify a license plate image in the preceding vehicle image to obtain a license plate recognition result, and determine whether the preceding vehicle is a gasoline vehicle or an electric vehicle according to the license plate recognition result; if the preceding vehicle is a gasoline vehicle, use the infrared imaging device to identify the real-time temperature at the exhaust pipe of the preceding vehicle, and determine heat change data of the exhaust gas emission heat of the preceding vehicle changing with time according to the real-time temperature; analyze the throttle operation degree of the preceding vehicle through an AI algorithm according to the heat change data; wherein, the greater the degree of heat increase change in the heat change data, the greater the throttle operation degree; predict upcoming driving speed change data of the preceding vehicle about to run according to the throttle operation degree; when the throttle operation degree increases corresponding to the preceding vehicle about to perform an accelerating driving action, the corresponding upcoming driving speed change data is an increase in speed; when the throttle operation degree decreases corresponding to the preceding vehicle about to perform a decelerating driving action, the corresponding upcoming driving speed change data is a decrease in speed; when the throttle operation degree remains unchanged corresponding to the preceding vehicle about to perform a constant-speed driving action or a decelerating driving action, the corresponding upcoming driving speed change data is an unchanged speed or a decrease in speed; control the own vehicle-mounted terminal to send a first prompt message about the predicted upcoming speed change of the preceding vehicle based on the upcoming driving speed change data of the preceding vehicle. In this solution, by monitoring and analyzing the exhaust pipe temperature change in real time and combining with the AI algorithm, the speed change situation of the preceding vehicle can be predicted more accurately, improving the accuracy of speed prediction, solving the technical problem of low accuracy of the judgment result of the upcoming driving speed of the preceding vehicle, and moreover, compared with the traditional method based on distance and relative speed, this method can reflect the operation intention of the preceding vehicle faster, thus giving the driver more time to react.

[0049] To make the above - mentioned objects, features, and advantages of the present application more obvious and understandable, the following provides preferred embodiments in conjunction with the accompanying drawings and makes a detailed description as follows. Description of the Drawings

[0050] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a schematic flowchart of the method for processing the data of the vehicle ahead based on the YTS system provided by the embodiment of the present application;

[0052] Figure 2 It is another schematic flowchart of the method for processing the data of the vehicle ahead based on the YTS system provided by the embodiment of the present application;

[0053] Figure 3 It is a schematic structural diagram of a device for processing the data of the vehicle ahead based on the YTS system provided by the embodiment of the present application;

[0054] Figure 4 It shows a schematic structural diagram of an electronic device provided by the embodiment of the present application. Detailed Embodiments

[0055] To make the objects, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions of the present application in conjunction with the drawings. Obviously, the described embodiments are some, rather than all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0056] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present application are intended to cover non - exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other unlisted steps or units, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0057] At present, the accuracy of the judgment result of the upcoming driving speed of the vehicle ahead is relatively low. Based on this, the embodiments of the present application provide a method, device and electronic device for processing the data of the vehicle ahead based on the YTS system, which can solve the technical problem of relatively low accuracy of the judgment result of the upcoming driving speed of the vehicle ahead.

[0058] The embodiments of the present invention will be further introduced below with reference to the accompanying drawings.

[0059] Figure 1 It is a schematic flowchart of a method for processing the data of the vehicle ahead based on the YTS system provided by the embodiments of the present application. Among them, this method is applied to the control system of the vehicle-mounted terminal itself, and an infrared camera device is set on the vehicle-mounted terminal itself. As Figure 1 shown, this method includes:

[0060] Step S110, obtain the image of the vehicle ahead corresponding to the vehicle-mounted terminal itself through the infrared camera device.

[0061] It should be noted that the YTS (unity TV Service) system in the embodiments of the present application represents the unity visual rendering service system. Among them, unity is a real-time 3D interactive content creation and operation platform. All creators, including game development, art, architecture, automotive design, and film and television, can turn their creativity into reality with the help of unity. The platform provides a complete set of software solutions, which can be used to create, operate and monetize any real-time interactive 2D and 3D content, and the supported platforms include mobile phones, tablets, PCs, game consoles, augmented reality and virtual reality devices.

[0062] As a possible implementation manner, the system first adjusts the camera settings according to preset parameters or user input, such as resolution, frame rate, etc., to optimize the image quality. Then the infrared camera device starts to capture the image information of the vehicle ahead, and the camera continuously transmits the captured video stream to the vehicle-mounted terminal for processing. Then, after the vehicle-mounted terminal receives the data from the infrared camera device, it will first preprocess the image, including noise reduction, contrast adjustment, etc., to improve the accuracy of subsequent analysis. Apply computer vision algorithms to identify and track the position, speed and other information of the vehicle ahead. The processed information can be used for assisted driving decisions, such as functions like automatic emergency braking and lane keeping assistance.

[0063] Step S120, during the rendering process of the image of the vehicle ahead through the YTS system of the vehicle-mounted terminal itself, identify the license plate image in the image of the vehicle ahead, obtain the license plate recognition result, and judge whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result.

[0064] Exemplarily, first, a real-time image of the vehicle ahead is captured by an infrared camera installed on the vehicle. Due to the adoption of infrared technology, the clarity of the image can be ensured even at night or under adverse weather conditions. The received image undergoes a series of preprocessing operations, such as grayscale conversion, noise removal, etc., to improve the accuracy of subsequent processing. Computer vision algorithms (such as deep learning-based methods) are used to detect the possible license plate area from the image. The detected license plate area is further segmented into individual characters, and optical character recognition (OCR) technology is applied to identify the text and numbers on the license plate. Once the license plate information is obtained, the system accesses a database containing license plate numbers and their corresponding vehicle types. This database can be locally stored or a cloud database accessed through a network service. According to the query results, the system can determine whether the vehicle corresponding to the license plate is a gasoline vehicle or an electric vehicle. This step may need to consider the different license plate coding rules in different countries or regions. Based on the identified vehicle type, the system can provide corresponding prompt information to the driver, such as reminding that the approaching vehicle is an electric vehicle and that attention needs to be paid to its acceleration characteristics different from those of traditional gasoline vehicles.

[0065] Step S130, if the vehicle ahead is a gasoline vehicle, use the infrared imaging device to identify the real-time temperature at the exhaust pipe of the vehicle ahead, and determine the heat change data of the exhaust gas emission heat of the vehicle ahead changing with time according to the real-time temperature.

[0066] In an alternative embodiment, once it is confirmed that the vehicle ahead is a gasoline vehicle, the system needs to further analyze the vehicle image to locate the position of the exhaust pipe. Since the positions and shapes of the exhaust pipes of different vehicle models may vary, a deep learning-based object detection algorithm can be used to accurately identify the exhaust pipe. During driving, as the relative position between the two vehicles changes, the position of the exhaust pipe will also change accordingly. Therefore, the system needs to have the ability of dynamic tracking to ensure that it can continuously and accurately locate the exhaust pipe. Use the on-vehicle infrared imaging device to aim at the located exhaust pipe area for real-time monitoring. The infrared camera can capture the infrared radiation intensity emitted by the object and then convert it into temperature information. Convert the data captured by the infrared camera into a visual heat map to show the temperature distribution of the exhaust pipe area. According to the real-time temperature data of the exhaust pipe area, combined with the vehicle operating state (such as speed, acceleration, etc.), calculate the heat change data of the exhaust gas emission. Physical principles and related formulas can be applied here, such as calculating the heat output according to the mass flow rate and specific heat capacity.

[0067] Step S140, analyze the throttle operation degree of the vehicle ahead through an AI algorithm according to the heat change data.

[0068] Among them, the greater the degree of increase in heat in the heat change data, the greater the throttle operation degree. Continuously monitor the temperature change at the exhaust pipe of the gasoline vehicle in front using an infrared imaging device and convert this data into a structured format (such as time series data). At the same time, collect relevant external environmental parameters, such as the outside air temperature, humidity, etc., for calibration in subsequent analysis. Extract key features from the original temperature data, such as the average temperature, peak temperature, temperature volatility, etc. These features may be directly related to the vehicle's combustion efficiency and throttle operation. Considering the change in exhaust gas emission temperature when the vehicle accelerates or decelerates, it is necessary to analyze the change pattern of temperature over time to identify acceleration-related features. To train a model to predict the throttle operation degree, a set of known data sets are required, which contain temperature change data at different throttle openings. This data set can be obtained through experiments or simulations. Select a suitable machine learning or deep learning model based on the complexity of the problem and the required accuracy. For example, a regression model can be used to predict continuous values (i.e., throttle opening), or a time series model can be used to capture the trend of temperature change over time.

[0069] For the model training process, use the labeled data set to train the selected model. During this process, ensure that the model can learn the mapping relationship between temperature change and throttle operation. To ensure the generalization ability of the model, use cross-validation techniques to evaluate the model performance and adjust the model parameters according to the results. Use the real-time temperature data collected and preprocessed in the above steps as the input to the trained model. The model outputs an estimated value of the throttle operation degree of the vehicle in front. This value reflects the current operation state of the other driver, such as whether they are accelerating, maintaining a constant speed, or decelerating.

[0070] Step S150, predict the upcoming driving speed change data of the vehicle in front according to the throttle operation degree.

[0071] When the throttle operation degree increases, it corresponds to the vehicle in front about to perform an accelerating driving action, and the corresponding upcoming driving speed change data is an increase in speed; when the throttle operation degree decreases, it corresponds to the vehicle in front about to perform a decelerating driving action, and the corresponding upcoming driving speed change data is a decrease in speed; when the throttle operation degree remains unchanged, it corresponds to the vehicle in front about to perform a constant-speed driving action or a decelerating driving action, and the corresponding upcoming driving speed change data is a constant speed or a decrease in speed.

[0072] Obtain the real-time throttle operation degree data of the vehicle ahead through sensors or V2X communication technology. Other auxiliary data (optional): such as the distance between the vehicle ahead and the following vehicle, road condition information, etc., to improve the prediction accuracy. Clean and format the collected data to ensure the data is accurate and error-free and easy for subsequent analysis. If necessary, discretize the continuous data to simplify the analysis process. Analyze the throttle operation degree at the current moment and compare it with the data in the previous time period (such as the previous second, the previous five seconds, etc.). Determine whether the throttle operation degree increases, decreases, or remains unchanged according to the comparison result. Throttle increases: If it is detected that the throttle operation degree increases, predict that the vehicle ahead is about to accelerate, that is, the speed increases. Throttle decreases: If it is detected that the throttle operation degree decreases, predict that the vehicle ahead is about to decelerate, that is, the speed decreases. Throttle remains unchanged: If the throttle operation degree remains unchanged, but considering possible external factors (such as the traffic light turning red), the system should also consider whether deceleration is required. In some cases, the vehicle may continue to travel at a constant speed; in other cases, especially when approaching an intersection or encountering an obstacle, the vehicle may decelerate. Output the predicted speed change data to relevant systems or modules, such as the decision-making layer of the autonomous driving system, for adjusting the speed and driving strategy of the vehicle itself.

[0073] As an optional implementation manner, a wind speed and direction sensor is also provided on the on-vehicle terminal itself; predicting the upcoming driving speed change data of the vehicle ahead according to the throttle operation degree includes: when the throttle operation degree remains unchanged, obtaining the ground image of the ground where the vehicle ahead is located through an infrared imaging device, and obtaining the current wind speed and direction data through the wind speed and direction sensor; identifying the ground texture data corresponding to the driving of the vehicle ahead based on the ground image, identifying the vehicle model of the vehicle ahead based on the vehicle image of the vehicle ahead, and determining the vehicle weight of the vehicle ahead according to the vehicle model; if the vehicle weight is greater than the preset resistance value with respect to the sum of the running resistance value of the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and direction data with respect to the vehicle ahead, then predict that the vehicle ahead is about to perform a deceleration driving action, and determine the corresponding upcoming driving speed change data as a speed decrease.

[0074] The system uses the wind speed and direction sensor and the infrared imaging device to collect environmental data (such as wind speed, wind direction) and the ground image of the vehicle ahead, and combines the vehicle weight identified by the vehicle model recognition to calculate the total driving resistance faced by the vehicle ahead (including running resistance, resistance caused by ground texture, and wind resistance). This multi-source information fusion method can provide a more comprehensive and accurate driving resistance assessment.

[0075] When the calculated total driving resistance exceeds a preset value, the system can intelligently predict that the vehicle ahead is about to perform a deceleration operation. This provides the possibility of early warning for the following vehicle, helping the driver to adjust their driving strategy in time to avoid potential risks.

[0076] By predicting the speed change trend of the vehicle ahead (especially the case of speed reduction) and promptly conveying this information to the driver, the system significantly improves the driving safety. It allows the driver more time to react, such as decelerating appropriately or changing lanes, thus reducing the probability of traffic accidents.

[0077] Based on this, after identifying the ground texture data corresponding to the vehicle ahead based on the ground image, identifying the vehicle model of the vehicle ahead based on the vehicle image of the vehicle ahead, and determining the vehicle weight of the vehicle ahead according to the vehicle model, it further includes: if the vehicle weight is less than or equal to the preset resistance value relative to the sum of the running resistance value of the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and direction data relative to the vehicle ahead, then predict that the vehicle ahead is about to perform a uniform driving action and determine the corresponding upcoming speed change data as the speed remaining unchanged.

[0078] When the system calculates that the total resistance faced by the vehicle ahead (including running resistance, resistance caused by ground texture, and wind resistance) is less than or equal to the preset resistance value, it can predict that the vehicle will continue to drive at the current speed. This ability enables the system not only to identify deceleration behaviors but also to accurately judge the state of uniform driving.

[0079] By providing predictive information on the speed change of the vehicle ahead, especially for the case of maintaining a constant speed, the system enhances the driving assistance function. This allows the following driver to better plan their driving behavior, such as maintaining the current speed or making appropriate adjustments, to ensure smooth and safe driving.

[0080] Step S160, control the in-vehicle terminal of its own to send out the first prompt information about the predicted upcoming speed change of the vehicle ahead based on the upcoming speed change data of the vehicle ahead.

[0081] Receive the analysis results of the throttle operation degree of the vehicle ahead and the corresponding data on the upcoming driving speed change in real time through V2X (Vehicle-to-Everything) communication technology or other sensor technologies. Analyze the received speed change data to extract key information such as acceleration, deceleration, or constant speed. Combine parameters such as the current position, speed, and acceleration of the vehicle itself, as well as factors such as road type (highway, urban road) and weather conditions to comprehensively evaluate the current driving environment. Determine whether to issue a prompt to the driver based on the speed change data of the vehicle ahead and the current driving environment. For example: If the vehicle ahead decelerates and the distance is relatively close, a prompt should be issued immediately. If the vehicle ahead is accelerating but still within a safe distance, it can be decided whether to issue a prompt according to the actual situation.

[0082] Generate corresponding prompt information according to the speed change. For example: For acceleration, a prompt information similar to "The vehicle ahead is accelerating. Please keep a safe distance" can be generated. For deceleration, generate a prompt like "Attention! The vehicle ahead is decelerating. Please prepare to adjust the vehicle speed accordingly". For constant speed, if necessary, some information to remind to maintain the current speed can also be generated. Issue a prompt through the in-vehicle terminal: Use the sound, visual, or tactile feedback mechanism of the in-vehicle terminal to issue a prompt to the driver. For example: Sound prompt, use voice broadcast to convey information; Visual prompt, display icons or text information on the instrument panel or the central control display screen; Tactile prompt, warn the driver through the vibration of the steering wheel or the seat.

[0083] In the embodiments of the present application, by real-time monitoring and analyzing the change of the exhaust pipe temperature and combining with the AI algorithm, the speed change situation of the vehicle ahead can be predicted more accurately, improving the accuracy of speed prediction. Moreover, compared with the traditional method based on distance and relative speed, this method can reflect the operation intention of the vehicle ahead faster, thus giving the driver more time to react.

[0084] In some embodiments, a wind speed and direction meter is also set on the in-vehicle terminal itself; after the above step S130, the method may further include the following steps:

[0085] If the degree of heat reduction change in the heat change data is greater than the preset cooling degree, it is determined that the current throttle operation degree belongs to completely non-operating throttle, and a ground image of the area where the vehicle ahead is located is obtained through an infrared imaging device, and the current wind speed and direction data are obtained through the wind speed and direction meter.

[0086] Identify the ground texture data corresponding to the driving of the vehicle ahead based on the ground image, identify the vehicle model of the vehicle ahead based on the vehicle ahead image, and determine the vehicle weight of the vehicle ahead according to the vehicle model.

[0087] Analyze the comprehensive driving resistance value of the vehicle ahead based on the running resistance value of the vehicle weight relative to the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and direction data relative to the vehicle ahead;

[0088] When the vehicle ahead remains in a completely non-operating throttle state, predict the vehicle stopping time of the vehicle ahead based on the current driving speed and the comprehensive driving resistance value of the vehicle ahead, and control the on-vehicle terminal to send a warning message of the vehicle stopping time to avoid a rear-end collision between the on-vehicle terminal and the vehicle ahead.

[0089] In the embodiment of the present application, when it is detected that the heat change data shows that the current throttle operation degree is completely non-operating (i.e., the degree of heat reduction change is greater than the preset cooling degree), the system can quickly determine this state and obtain the ground image of the vehicle ahead through an infrared imaging device and collect environmental data using an anemometer.

[0090] Based on the obtained ground image, vehicle model (identified from the image of the vehicle ahead), and the current wind speed and direction data, the system can accurately calculate the comprehensive driving resistance value faced by the vehicle ahead. This includes the resistance caused by the ground texture, the running resistance caused by the vehicle weight, and the additional resistance caused by the wind.

[0091] By analyzing the current driving speed and the comprehensive driving resistance value of the vehicle ahead, the system can predict the time when the vehicle stops. This is especially important for the vehicle behind, as it can help the driver take measures in advance, such as decelerating or adjusting the vehicle distance, thus effectively preventing rear-end collisions.

[0092] The on-vehicle terminal sends a warning message of the vehicle stopping time based on the above analysis results, providing timely and important decision-making support for the driver, not only improving the safety of individual driving, but also indirectly promoting the safety and efficiency of the overall road traffic.

[0093] In some embodiments, the image of the vehicle ahead includes video data of the vehicle ahead; as Figure 2 shown, after identifying the real-time temperature at the exhaust pipe of the vehicle ahead using the infrared imaging device in the above step S130, the method may further include the following steps:

[0094] Step S210, identifying the historical vehicle speed change data of the vehicle ahead based on the video data;

[0095] Step S220: Determine the historical vehicle speed change data and the real-time temperature at the exhaust pipe corresponding to the previous moment as the first training sample, and train the initial binary regression analysis model based on the first training sample to obtain the first vehicle speed operation law model of the vehicle ahead; wherein, the first vehicle speed operation law model is used to represent the correspondence between the real-time temperature at the exhaust pipe of the vehicle ahead and the vehicle speed change data at the next moment.

[0096] Step S230: According to the real-time temperature at the exhaust pipe at the current moment, predict the first next moment vehicle speed change data corresponding to the next moment of the current moment through the first vehicle speed operation law model.

[0097] Step S240: Control the in-vehicle terminal of its own to send out the second prompt information about the predicted situation of the speed change of the vehicle ahead at the next moment based on the first next moment vehicle speed change data.

[0098] In the embodiment of the present application, by using the historical vehicle speed change data and the real-time temperature at the exhaust pipe as the training sample, the trained binary regression analysis model (i.e., the first vehicle speed operation law model) can more accurately capture the key factors affecting the vehicle speed change and the relationship between them. In particular, by using the indirect but closely related parameter of the exhaust pipe temperature during vehicle operation, the model is more accurate in predicting the vehicle speed change.

[0099] Based on the first next moment vehicle speed change data predicted by the above model, the in-vehicle terminal can send out the second prompt information in advance to remind the driver or the automatic control system of the upcoming vehicle speed change of the vehicle ahead. This helps the driver to react in advance, such as adjusting the vehicle speed, maintaining a safe distance, etc., so as to effectively reduce the occurrence of rear-end collisions and improve driving safety.

[0100] In addition to safety, this prediction mechanism can also help optimize driving behavior and make the driving process smoother. For example, in the case of heavy traffic, timely understanding of the speed change trend of the vehicle ahead can help the driver better plan his driving strategy, avoid frequent braking and accelerating, thereby saving fuel and reducing vehicle wear.

[0101] In some embodiments, after the above step S150, the method may further include the following steps: Analyze the vehicle speed adaptation range of the in-vehicle terminal of its own for the vehicle ahead through the AI algorithm according to the upcoming vehicle speed change data of the vehicle ahead; Control the in-vehicle terminal of its own to send out the third prompt information about the current vehicle speed suggestion based on the vehicle speed adaptation range.

[0102] In the embodiments of the present application, based on the speed change data of the vehicle ahead and the current traffic conditions, an adaptation range is calculated using an AI algorithm to provide personalized current vehicle speed suggestions for the driver. These suggestions take into account the behavior of the vehicle ahead (such as accelerating, decelerating, or maintaining a constant speed), making the suggestions more in line with the actual road conditions.

[0103] Adjusting the vehicle's own speed in a timely manner according to the speed change of the vehicle ahead can effectively avoid the occurrence of rear-end collisions, especially in the case of emergency braking or sudden deceleration. Through the third prompt message sent by the in-vehicle terminal, the driver can be timely reminded to take appropriate actions, such as decelerating in advance or maintaining a safe distance.

[0104] When the vehicle can dynamically adjust its driving speed according to the speed change of the vehicle ahead, the entire traffic flow will become smoother. This helps to reduce traffic congestion because smoother speed adjustment can reduce the possibility of a "wave-like" braking chain reaction, thus optimizing the overall road traffic efficiency.

[0105] In addition to improving safety, this technology can also make the driving process more comfortable and stable. By receiving reasonable vehicle speed suggestions, the driver can more easily cope with complex traffic environments, reduce unnecessary hard braking and rapid acceleration operations, and thus enhance the driving experience.

[0106] In summary, the main technical effect of this method is that it uses an advanced AI algorithm to provide accurate vehicle speed suggestions, which not only improves driving safety and smoothness, but also helps to optimize traffic flow and improve the driving experience.

[0107] In some embodiments, the image of the vehicle ahead includes video data of the vehicle ahead; after the above step S120, the method may further include the following steps: when the vehicle ahead is an electric vehicle, use an infrared imaging device to obtain an image of the tire of the vehicle ahead, and use the infrared imaging device to identify the current climate condition; if it is identified that the current climate condition belongs to a rain or snow climate, identify the degree of splashing of water or snow between the tire of the vehicle ahead and the ground based on the image of the tire; if it is identified that the current climate condition belongs to a non-rain or snow climate, identify the degree of dust generation between the tire of the vehicle ahead and the ground based on the image of the tire; identify the real-time vehicle speed change data of the vehicle ahead based on the video data, and determine the degree of dust generation or splashing degree and the real-time vehicle speed change data corresponding to the next moment as the second training sample, and train the initial multiple regression analysis model based on the second training sample to obtain the second vehicle speed operation law model of the vehicle ahead; wherein, the second vehicle speed operation law model is used to characterize the corresponding relationship between the degree of dust generation or splashing degree of the vehicle ahead and the real-time vehicle speed change data corresponding to the next moment; according to the degree of dust generation or splashing degree at the current moment, predict the second next moment vehicle speed change data corresponding to the next moment of the current moment through the second vehicle speed operation law model; control its own vehicle-mounted terminal to send out the fourth prompt information about the predicted situation of the speed change of the vehicle ahead at the next moment based on the second next moment vehicle speed change data.

[0108] In the embodiments of the present application, an infrared imaging device is used to capture an image of the tire of the vehicle ahead, and the degree of dust generation or the degree of splashing of water / snow is respectively evaluated according to the identified climate conditions (rain or snow or non-rain or snow), and combined with the real-time vehicle speed change in the video data, a second vehicle speed operation law model is trained. This model can accurately reflect the relationship between the dust generation or splashing degree of the vehicle ahead and the speed change of the vehicle at the next moment.

[0109] By predicting the future speed change of the vehicle ahead, the driver can react in advance, such as taking measures such as appropriate deceleration and maintaining a safe distance, thereby greatly improving the driving safety and predictability. Especially in bad weather conditions (such as rain or snow weather), timely understanding of the possible speed change of the vehicle ahead is crucial for preventing rear-end collisions.

[0110] In addition to improving safety, this technology can also make the driving process smoother and more stable. The driver can more reasonably plan his driving strategy according to the predicted information of the speed change of the vehicle ahead, reduce unnecessary sudden braking and rapid acceleration, and make the driving process more comfortable.

[0111] When all vehicles can make corresponding adjustments according to the speed change trend of the vehicle ahead, the entire traffic flow will become more orderly and efficient. This helps to reduce traffic jams caused by sudden deceleration or acceleration, and thus improves the road traffic efficiency.

[0112] In summary, by using advanced image recognition technology and data analysis methods, the speed change trend of the vehicle ahead under different climate conditions can be accurately predicted, and warning information can be provided through the in-vehicle terminal, thus significantly improving driving safety, driving experience, and overall traffic flow.

[0113] Figure 3 There is provided a structural schematic diagram of a data processing device for the vehicle ahead based on the YTS system. This device can be applied to the control system of its own in-vehicle terminal, and an infrared imaging device is provided on its own in-vehicle terminal. As Figure 3 shown, the data processing device 300 for the vehicle ahead based on the YTS system includes:

[0114] An acquisition module 301, configured to acquire an image of the vehicle ahead corresponding to its own in-vehicle terminal through the infrared imaging device;

[0115] A judgment module 302, configured to identify a license plate image in the image of the vehicle ahead during the rendering process of the image of the vehicle ahead through the YTS system of its own in-vehicle terminal, obtain a license plate recognition result, and judge whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result;

[0116] An identification module 303, configured to, if the vehicle ahead is a gasoline vehicle, use the infrared imaging device to identify the real-time temperature at the exhaust pipe of the vehicle ahead, and determine heat change data of the exhaust gas emission heat of the vehicle ahead changing with time according to the real-time temperature;

[0117] An analysis module 304, configured to analyze the throttle operation degree of the vehicle ahead through an AI algorithm according to the heat change data; wherein, the greater the degree of heat increase change in the heat change data, the greater the throttle operation degree;

[0118] A prediction module 305, configured to predict the upcoming driving speed change data of the vehicle ahead according to the throttle operation degree; when the throttle operation degree increases corresponding to the vehicle ahead about to perform an accelerating driving action, the corresponding upcoming driving speed change data is an increase in speed; when the throttle operation degree decreases corresponding to the vehicle ahead about to perform a decelerating driving action, the corresponding upcoming driving speed change data is a decrease in speed; when the throttle operation degree remains unchanged corresponding to the vehicle ahead about to perform a constant-speed driving action or a decelerating driving action, the corresponding upcoming driving speed change data is an unchanged speed or a decrease in speed;

[0119] An emission module 306, configured to control its own in-vehicle terminal to emit a first prompt message about the predicted upcoming speed change of the vehicle ahead based on the upcoming driving speed change data of the vehicle ahead.

[0120] The front vehicle data processing device based on the YTS system provided by the embodiments of the present application has the same technical features as the front vehicle data processing method based on the YTS system provided by the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.

[0121] An electronic device provided by an embodiment of the present application, such as Figure 4 shown, the electronic device 400 includes a processor 402 and a memory 401. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps of the method provided by the above embodiments are implemented.

[0122] See Figure 4 , the electronic device further includes: a bus 403 and a communication interface 404. The processor 402, the communication interface 404, and the memory 401 are connected through the bus 403. The processor 402 is used to execute an executable module stored in the memory 401, such as a computer program.

[0123] Among them, the memory 401 may include a high-speed random access memory (Random Access Memory, abbreviated as RAM), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 404 (which can be wired or wireless), a communication connection between the system network element and at least one other network element is realized, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0124] The bus 403 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 4 only a bidirectional arrow is used in

[0125] to represent, but it does not mean that there is only one bus or one type of bus.

[0126] The processor 402 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 402 or the instructions in the form of software. The above-mentioned processor 402 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 401, and the processor 402 reads the information in the memory 401 and combines its hardware to complete the steps of the above method.

[0127] Corresponding to the above-mentioned method for processing the data of the vehicle in front based on the YTS system, an embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and run by the processor, the computer-executable instructions cause the processor to run the steps of the above-mentioned method for processing the data of the vehicle in front based on the YTS system.

[0128] The device for processing the data of the vehicle in front based on the YTS system provided by the embodiments of the present application may be specific hardware on the device or software or firmware installed on the device, etc. For the device provided by the embodiments of the present application, the implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can all refer to the corresponding processes in the above method embodiments, and will not be repeated here.

[0129] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0130] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, the functional units in the embodiments provided in the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0133] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method for processing the data of the vehicle in front based on the YTS system described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks, or optical discs.

[0134] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0135] Finally, it should be noted that: the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solutions of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by this application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for processing the data of the vehicle ahead based on the YTS system, characterized in that An infrared imaging device is provided on the vehicle-mounted terminal itself, and the method includes: Obtaining a front vehicle image of the vehicle in front corresponding to the vehicle-mounted terminal itself through the infrared imaging device; During the rendering process of the front vehicle image by the YTS system of the vehicle-mounted terminal itself, identifying a license plate image in the front vehicle image to obtain a license plate recognition result, and judging whether the front vehicle belongs to a gasoline vehicle or an electric vehicle according to the license plate recognition result; If the front vehicle belongs to a gasoline vehicle, using the infrared imaging device to identify the real-time temperature at the exhaust pipe of the front vehicle, and determining heat change data of the exhaust gas emission heat of the front vehicle changing with time according to the real-time temperature; Analyzing the throttle operation degree of the front vehicle through an AI algorithm according to the heat change data; wherein, the greater the degree of heat increase change in the heat change data, the greater the throttle operation degree; Predicting upcoming driving speed change data of the front vehicle according to the throttle operation degree; when the throttle operation degree increases, it corresponds to the front vehicle about to perform an accelerating driving action, and the corresponding upcoming driving speed change data is an increase in speed; when the throttle operation degree decreases, it corresponds to the front vehicle about to perform a decelerating driving action, and the corresponding upcoming driving speed change data is a decrease in speed; when the throttle operation degree remains unchanged, it corresponds to the front vehicle about to perform a constant-speed driving action or a decelerating driving action, and the corresponding upcoming driving speed change data is an unchanged speed or a decrease in speed; Controlling the vehicle-mounted terminal itself to send out a first prompt message about the predicted upcoming speed change of the vehicle in front based on the upcoming driving speed change data of the front vehicle.

2. The method according to claim 1, wherein A wind speed and wind direction meter is also provided on the vehicle-mounted terminal itself; after determining the heat change data of the exhaust gas emission heat of the front vehicle changing with time according to the real-time temperature, it further includes: If the degree of heat decrease change in the heat change data is greater than a preset cooling degree, determining that the current throttle operation degree belongs to a completely unoperated throttle, obtaining a ground image of the ground where the front vehicle is located through the infrared imaging device, and obtaining current wind speed and wind direction data through the wind speed and wind direction meter; Identifying ground texture data of the ground corresponding to the driving of the front vehicle based on the ground image, identifying the vehicle model of the front vehicle based on the front vehicle image, and determining the vehicle weight of the front vehicle according to the vehicle model; Analyzing the comprehensive driving resistance value of the front vehicle according to the running resistance value of the front vehicle relative to the vehicle weight, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the front vehicle relative to the current wind speed and wind direction data; When the front vehicle remains in the state of completely unoperated throttle, predicting the vehicle stop driving time of the front vehicle according to the current driving speed and the comprehensive driving resistance value of the front vehicle, and controlling the vehicle-mounted terminal itself to send out a warning message about the vehicle stop driving time to avoid a rear-end collision between the vehicle-mounted terminal itself and the front vehicle.

3. The method according to claim 1, wherein The image of the vehicle ahead includes video data of the vehicle ahead; After identifying the real-time temperature at the exhaust pipe of the vehicle ahead using the infrared imaging device, it further includes: Identifying the historical vehicle speed change data of the vehicle ahead based on the video data; Determining the historical vehicle speed change data and the real-time temperature at the exhaust pipe corresponding to the previous moment as the first training sample, and training the initial binary regression analysis model based on the first training sample to obtain the first vehicle speed operation law model of the vehicle ahead; wherein, the first vehicle speed operation law model is used to characterize the corresponding relationship between the real-time temperature at the exhaust pipe of the vehicle ahead and the vehicle speed change data at the next moment; Predicting the first next moment vehicle speed change data corresponding to the current moment through the first vehicle speed operation law model according to the real-time temperature at the exhaust pipe at the current moment; Controlling the own vehicle-mounted terminal to send out the second prompt information about the predicted situation of the vehicle speed change of the vehicle ahead at the next moment based on the first next moment vehicle speed change data.

4. The method according to claim 1, wherein After predicting the upcoming vehicle speed change data of the vehicle ahead according to the throttle operation degree, it further includes: Analyzing the vehicle speed adaptation range of the own vehicle-mounted terminal for the vehicle ahead through the AI algorithm according to the upcoming vehicle speed change data of the vehicle ahead; Controlling the own vehicle to send out the third prompt information about the current vehicle speed suggestion based on the vehicle speed adaptation range.

5. The method according to claim 1, characterized in that, The image of the vehicle ahead includes video data of the vehicle ahead; After the step of judging whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result, it further includes: In the case that the vehicle ahead is an electric vehicle, using the infrared imaging device to obtain the image at the tire of the vehicle ahead, and using the infrared imaging device to identify the current climate condition; If it is recognized that the current climate condition belongs to a rain or snow climate, then identifying the splashing degree of water flowers or snowflakes between the tire of the vehicle ahead and the ground based on the image at the tire; If it is recognized that the current climate condition belongs to a non-rain or snow climate, then identifying the dust generation degree between the tire of the vehicle ahead and the ground based on the image at the tire; Identifying the real-time vehicle speed change data of the vehicle ahead based on the video data, and determining the dust generation degree or the splashing degree and the real-time vehicle speed change data corresponding to the next moment as the second training sample, and training the initial multiple regression analysis model based on the second training sample to obtain the second vehicle speed operation law model of the vehicle ahead; wherein, the second vehicle speed operation law model is used to characterize the corresponding relationship between the dust generation degree or the splashing degree of the vehicle ahead and the real-time vehicle speed change data corresponding to the next moment; Predicting the second next moment vehicle speed change data corresponding to the current moment through the second vehicle speed operation law model according to the dust generation degree or the splashing degree at the current moment; Controlling the own vehicle-mounted terminal to send out the fourth prompt information about the predicted situation of the vehicle speed change of the vehicle ahead at the next moment based on the second next moment vehicle speed change data.

6. The method according to claim 1, wherein An anemometer and wind vane are also provided on the vehicle-mounted terminal itself; the predicted upcoming driving speed change data of the vehicle ahead based on the degree of throttle operation includes: When the degree of throttle operation remains unchanged, obtain the ground image of the ground where the vehicle ahead is located through the infrared imaging device, and obtain the current wind speed and wind direction data through the anemometer and wind vane; Identify the ground texture data corresponding to the driving of the vehicle ahead based on the ground image, identify the vehicle model of the vehicle ahead based on the vehicle image of the vehicle ahead, and determine the vehicle weight of the vehicle ahead according to the vehicle model; If the sum of the vehicle weight relative to the running resistance value of the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and wind direction data relative to the vehicle ahead is greater than the preset resistance value, predict that the vehicle ahead is about to perform a decelerating driving action, and determine the corresponding upcoming driving speed change data as a speed decrease.

7. The method according to claim 6, characterized in that, After identifying the ground texture data corresponding to the driving of the vehicle ahead based on the ground image, identifying the vehicle model of the vehicle ahead based on the vehicle image of the vehicle ahead, and determining the vehicle weight of the vehicle ahead according to the vehicle model, it further includes: If the sum of the vehicle weight relative to the running resistance value of the vehicle ahead, the ground resistance value corresponding to the ground texture data, and the wind resistance value of the current wind speed and wind direction data relative to the vehicle ahead is less than or equal to the preset resistance value, predict that the vehicle ahead is about to perform a constant-speed driving action, and determine the corresponding upcoming driving speed change data as a constant speed.

8. A front vehicle data processing device based on the YTS system, characterized in that, An infrared imaging device is provided on the vehicle-mounted terminal itself, and the device includes: An acquisition module for obtaining the vehicle image of the vehicle ahead corresponding to the vehicle-mounted terminal itself through the infrared imaging device; A judgment module for identifying the license plate image in the vehicle image of the vehicle ahead during the rendering process of the vehicle image of the vehicle ahead by the YTS system of the vehicle-mounted terminal itself, obtaining the license plate recognition result, and judging whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result; An identification module for, if the vehicle ahead is a gasoline vehicle, using the infrared imaging device to identify the real-time temperature at the exhaust pipe of the vehicle ahead, and determining the heat change data of the exhaust gas emission heat of the vehicle ahead changing with time according to the real-time temperature; An analysis module for analyzing the degree of throttle operation of the vehicle ahead through an AI algorithm according to the heat change data; wherein, the greater the degree of heat increase change in the heat change data, the greater the degree of throttle operation. A prediction module, configured to predict the upcoming driving speed change data of the vehicle ahead according to the degree of throttle operation; when the degree of throttle operation increases, corresponding to the vehicle ahead about to perform an accelerating driving action, the corresponding upcoming driving speed change data is an increase in speed; when the degree of throttle operation decreases, corresponding to the vehicle ahead about to perform a decelerating driving action, the corresponding upcoming driving speed change data is a decrease in speed; when the degree of throttle operation remains unchanged, corresponding to the vehicle ahead about to perform a constant-speed driving action or a decelerating driving action, the corresponding upcoming driving speed change data is an unchanged speed or a decrease in speed. An emission module, configured to control the own vehicle-mounted terminal to emit a first prompt message about the predicted upcoming speed change of the vehicle ahead based on the upcoming driving speed change data of the vehicle ahead.

9. An electronic device, comprising a memory and a processor, wherein a computer program that can run on the processor is stored in the memory, and is characterized in that When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7 above.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and run by the processor, the computer-executable instructions cause the processor to run the method according to any one of claims 1 to 7.

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