Front vehicle data processing method and device based on YTS system and electronic equipment

Abstract

The invention provides a front vehicle data processing method and device based on a YTS system, and electronic equipment, relates to the field of vehicle-mounted technologies, and solves the technical problem that the accuracy of a judgment result of the to-be-driven speed of a front vehicle is low. The method comprises the steps that if a front vehicle belongs to a gasoline car, infrared camera equipment is used for recognizing the real-time temperature of an exhaust pipe on the front vehicle, and heat change data of exhaust emission heat of the front vehicle along with time change is determined according to the real-time temperature; according to the heat change data, the accelerator operation degree of the front vehicle is analyzed through an AI algorithm; predicting imminent driving speed change data of imminent driving of the front vehicle according to the accelerator operation degree; and controlling the own vehicle-mounted terminal to send out first prompt information of the imminent speed change prediction condition of the front vehicle based on the imminent driving speed change data of the front vehicle.

Description

Technical Field

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

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

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

[0005] In a first aspect, this application provides a method for processing data of a preceding vehicle based on a YTS system. An infrared imaging device is provided on a vehicle-mounted terminal of its own. The method includes: Obtaining a front-vehicle image of a preceding vehicle corresponding to the vehicle-mounted terminal of its own through the infrared imaging device; During the rendering process of the front-vehicle image by the YTS system of the vehicle-mounted terminal of its own, identifying a license plate image in the front-vehicle image to obtain a license plate recognition result, and judging 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, using the infrared imaging device to identify the real-time temperature at the exhaust pipe of the preceding vehicle, and determining heat change data of the exhaust gas emission heat of the preceding vehicle changing with time according to the real-time temperature; Analyzing 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 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. 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.

[0006] 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: If the degree of heat reduction change in the heat change data is greater than the preset cooling degree, determine that the current degree of throttle operation belongs to a completely unoperated throttle, 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. 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. 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. When 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.

[0007] 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: Identify the historical vehicle speed change data of the vehicle ahead based on the video data. 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 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; According to the real-time temperature at the exhaust pipe at the current moment, predict the first vehicle speed change data corresponding to the next moment of the current moment through the first vehicle speed operation law model; Based on the first vehicle speed change data at the next moment, control 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.

[0008] In a possible implementation, after predicting the upcoming vehicle speed change data of the vehicle ahead according to the throttle operation degree, it further includes: According to the upcoming vehicle speed change data of the vehicle ahead, analyze the vehicle speed adaptation range of the own vehicle-mounted terminal for the vehicle ahead through the AI algorithm; Based on the vehicle speed adaptation range, control the own vehicle to send out the third prompt information about the current vehicle speed suggestion.

[0009] 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 is a gasoline vehicle or an electric vehicle according to the license plate recognition result, it further includes: When 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; If it is recognized that the current climate condition belongs to a rain-snow climate, then based on the image at the tire, recognize the splashing degree of water or snow between the tire of the vehicle ahead and the ground; If it is recognized that the current climate condition belongs to a non-rain-snow climate, then based on the image at the tire, recognize the dust generation degree between the tire of the vehicle ahead and the ground; Based on the video data, recognize the real-time vehicle speed change data of the vehicle ahead, 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; 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 according to the dust generation degree or the splashing degree at the current moment; Control the own vehicle - mounted terminal to send a fourth prompt message about the predicted situation of the speed change of the vehicle in front at the next moment based on the second next - moment vehicle speed change data.

[0010] In a possible implementation, a wind speed and wind direction meter is further provided on the own vehicle - mounted terminal; the predicting the upcoming driving speed change data of the vehicle in front according to the throttle operation degree includes: When the throttle operation degree remains unchanged, obtain the ground image of the ground where the vehicle in front is located through the infrared imaging device, and obtain the current wind speed and wind direction data through the wind speed and wind direction meter; Identify the ground texture data corresponding to the driving of the vehicle in front based on the ground image, identify the vehicle model of the vehicle in front based on the vehicle image of the vehicle in front, and determine the vehicle weight of the vehicle in front according to the vehicle model; If the sum of the vehicle weight relative to the running resistance value of the vehicle in front, 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 in front is greater than the preset resistance value, predict that the vehicle in front is about to perform a decelerating driving action, and determine the corresponding upcoming driving speed change data as a speed reduction.

[0011] In a possible implementation, after identifying the ground texture data corresponding to the driving of the vehicle in front based on the ground image, identifying the vehicle model of the vehicle in front based on the vehicle image of the vehicle in front, and determining the vehicle weight of the vehicle in front according to the vehicle model, it further includes: If the sum of the vehicle weight relative to the running resistance value of the vehicle in front, 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 in front is less than or equal to the preset resistance value, predict that the vehicle in front is about to perform a uniform - speed driving action, and determine the corresponding upcoming driving speed change data as a constant speed.

[0012] In a second aspect, the present application provides a vehicle - in - front data processing device based on the YTS system. An infrared imaging device is provided on the own vehicle - mounted terminal, and the device includes: An acquisition module, configured to obtain a vehicle - in - front image of the own vehicle - mounted terminal corresponding to the vehicle in front through the infrared imaging device; A judgment module, 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 vehicle-mounted terminal, obtain a license plate recognition result, and determine whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result; An identification module, 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 indicating the change of the exhaust gas emission heat of the vehicle ahead over time according to the real-time temperature; An analysis module, 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; A prediction module, configured to predict upcoming driving speed change data of the vehicle ahead according to the throttle operation degree; when the throttle operation degree increases, it corresponds to an upcoming acceleration driving action of the vehicle ahead, and the corresponding upcoming driving speed change data is an increase in speed; when the throttle operation degree decreases, it corresponds to an upcoming deceleration driving action of the vehicle ahead, and the corresponding upcoming driving speed change data is a decrease in speed; when the throttle operation degree remains unchanged, it corresponds to an upcoming constant-speed driving action or deceleration driving action of the vehicle ahead, and 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.

[0013] 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.

[0014] 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.

[0015] The present application brings the following beneficial effects: A method, device and electronic device for processing data of a vehicle in front based on a YTS system provided by this application can obtain an image of the vehicle in front corresponding to the own vehicle terminal through the infrared imaging device; during the rendering process of the image of the vehicle in front by the YTS system of the own vehicle terminal, identify the license plate image in the image of the vehicle in front to obtain a license plate recognition result, and judge whether the vehicle in front belongs to a gasoline vehicle or an electric vehicle according to the license plate recognition result; if the vehicle in front belongs to a gasoline vehicle, use the infrared imaging device to identify the real-time temperature at the exhaust pipe of the vehicle in front, and determine the heat change data of the exhaust gas emission heat of the vehicle in front changing with time according to the real-time temperature; analyze the throttle operation degree of the vehicle in front 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 the upcoming driving speed change data of the vehicle in front according to the throttle operation degree; when the throttle operation degree increases corresponding to the vehicle in front 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 in front 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 in front 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 terminal 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 vehicle in front. In this solution, by monitoring and analyzing the change of the exhaust pipe temperature in real time and combining with the AI algorithm, the speed change of the vehicle in front 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 vehicle in front. Moreover, compared with the traditional method based on distance and relative speed, this method can reflect the operation intention of the vehicle in front faster, thus giving the driver more time to react.

[0016] To make the above objects, features and advantages of this application more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 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; Figure 2 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; Figure 3 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; Figure 4 Schematic structural diagram of an electronic device provided by the embodiment of the present application is shown. Detailed implementation manners

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0020] 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 steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0021] Currently, 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, a device, and an electronic device for processing the data of the vehicle ahead based on the YTS system, and through this method, the technical problem of relatively low accuracy of the judgment result of the upcoming driving speed of the vehicle ahead can be solved.

[0022] The embodiments of the present invention will be further introduced below with reference to the accompanying drawings.

[0023] Figure 1 Schematic flowchart of a method for processing the data of the vehicle ahead based on the YTS system provided by the embodiment of the present application. Among them, this method is applied to the control system of the vehicle-mounted terminal itself, and an infrared imaging device is provided on the vehicle-mounted terminal itself. As Figure 1 shown, this method includes: Step S110, obtaining an image of the vehicle ahead corresponding to the vehicle-mounted terminal itself through the infrared imaging device.

[0024] It should be noted that the YTS (unity TV Service) system in the embodiments of the present application represents a 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 that can be used to create, operate, and monetize any real-time interactive 2D and 3D content. The supported platforms include mobile phones, tablets, PCs, game consoles, augmented reality, and virtual reality devices.

[0025] As a possible implementation, 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. 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. Computer vision algorithms are applied 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.

[0026] Step S120, during the rendering process of the image of the vehicle ahead through the YTS system of its own vehicle-mounted terminal, 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.

[0027] Exemplarily, first, capture the real-time image of the vehicle ahead through 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 bad 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 possible license plate regions from the image. The detected license plate regions are further segmented into individual characters, and optical character recognition (OCR) technology is applied to identify the letters and numbers on the license plate. Once the license plate information is obtained, the system will access 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 result, 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 for the driver, such as reminding that the approaching vehicle is an electric vehicle and the driver needs to pay attention to its acceleration characteristics different from traditional gasoline vehicles.

[0028] Step S130, if the vehicle ahead is a gasoline vehicle, use an 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 emissions of the vehicle ahead changing with time according to the real-time temperature.

[0029] 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 to dynamically track to ensure that the exhaust pipe can be continuously and accurately located. Use an in-vehicle infrared imaging device to monitor the located exhaust pipe area in real time. The infrared camera can capture the infrared radiation intensity emitted by an 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 and in combination with the vehicle operating conditions (such as speed, acceleration, etc.), calculate the heat change data of the exhaust gas emissions. Physical principles and related formulas can be used here, such as calculating the heat output according to the mass flow rate and specific heat capacity.

[0030] Step S140, analyze the throttle operation degree of the vehicle ahead according to the heat change data through an AI algorithm.

[0031] Among them, the greater the degree of heat increase change in the heat change data, the greater the throttle operation degree. Continuously monitor the temperature change at the exhaust pipe of the gasoline vehicle ahead 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 combustion efficiency and throttle operation of the vehicle. Considering the change in the exhaust gas emission temperature when the vehicle accelerates or decelerates, it is necessary to analyze the change pattern of the 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 set of data 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.

[0032] For the model training process, a selected model is trained using the labeled dataset. During this process, it is necessary to ensure that the model can learn the mapping relationship between temperature changes and throttle operations. To ensure the generalization ability of the model, cross-validation technology is adopted to evaluate the model performance, and the model parameters are adjusted according to the results. The real-time temperature data collected and preprocessed in the above steps is used as the input to train the 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.

[0033] Step S150, predict the upcoming driving speed change data of the vehicle in front according to the throttle operation degree.

[0034] 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.

[0035] Obtain the real-time throttle operation degree data of the vehicle in front through sensors or V2X communication technology. Other auxiliary data (optional): such as the distance between the vehicle in front and the following vehicle, road condition information, etc., to improve the prediction accuracy. Clean and format the collected data to ensure that 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 increase: If it is detected that the throttle operation degree increases, predict that the vehicle in front is about to accelerate, that is, the speed increases. Throttle decrease: If it is detected that the throttle operation degree decreases, predict that the vehicle in front is about to decelerate, that is, the speed decreases. Throttle 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 drive 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.

[0036] As an alternative implementation, a wind speed and direction meter is also provided on the vehicle's own on-board terminal; the predicted upcoming driving speed change data of the vehicle ahead is obtained according to the degree of throttle operation, including: when the degree of throttle operation 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 meter; 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 a 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 predicting that the vehicle ahead is about to perform a decelerating driving action, and determining the corresponding upcoming driving speed change data as a speed decrease.

[0037] The system uses a wind speed and direction meter and an infrared imaging device to collect environmental data (such as wind speed and direction) and the ground image of the vehicle ahead, and combines the vehicle weight obtained by 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 assessment of driving resistance.

[0038] When the calculated total driving resistance exceeds the preset value, the system can intelligently predict that the vehicle ahead is about to perform a decelerating operation. This provides the possibility of early warning for the vehicle behind, helping the driver to adjust their driving strategy in time to avoid potential risks.

[0039] By predicting the speed change trend of the vehicle ahead (especially the case of speed decrease) and communicating this information to the driver in a timely manner, the system significantly improves the safety of driving. It allows the driver more time to react, such as decelerating appropriately or changing lanes, thus reducing the probability of traffic accidents.

[0040] Based on this, 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 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 predicting that the vehicle ahead is about to perform a constant-speed driving action, and determining the corresponding upcoming driving speed change data as a speed constant.

[0041] 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 maintain its current speed. This ability enables the system to not only identify deceleration behavior but also accurately judge the state of uniform motion.

[0042] By providing predictive information about 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 driver behind to better plan their driving behavior, such as maintaining the current speed or making appropriate adjustments, to ensure smooth and safe driving.

[0043] Step S160, based on the upcoming speed change data of the vehicle ahead, control the in-vehicle terminal to send out the first prompt information about the predicted upcoming speed change of the vehicle ahead.

[0044] Through V2X (Vehicle-to-Everything) communication technology or other sensor technologies, receive the analysis result of the throttle operation degree of the vehicle ahead and the corresponding upcoming speed change data in real time. Analyze the received speed change data, extract key information such as acceleration, deceleration, or uniform motion. 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. Based on the speed change data of the vehicle ahead and the current driving environment, determine whether to send a prompt to the driver. For example: If the vehicle ahead decelerates and the distance is relatively close, a prompt should be sent immediately. If the vehicle ahead is accelerating but still within a safe distance, it can be decided whether to send a prompt according to the actual situation.

[0045] Generate corresponding prompt information according to the speed change. For example: For acceleration, a prompt information like "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 uniform motion, if necessary, some information to remind to maintain the current speed can also be generated. Send the prompt through the in-vehicle terminal: Use the sound, visual, or tactile feedback mechanism of the in-vehicle terminal to send 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.

[0046] 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.

[0047] In some embodiments, a wind speed and direction meter is further provided on the vehicle-mounted terminal itself; after the above step S130, the method may further include the following steps: 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 a completely non-operating throttle, and a ground image of the ground 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; Based on the ground image, identify the ground texture data corresponding to the vehicle ahead, 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; 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, analyze the comprehensive running resistance value of the vehicle ahead; When the vehicle ahead remains in a completely non-operating throttle, according to the current driving speed and the comprehensive running resistance value of the vehicle ahead, predict the vehicle stop driving time of the vehicle ahead, and control the vehicle-mounted terminal itself to send out a warning message of the vehicle stop driving time, so as to avoid a rear-end collision between the vehicle-mounted terminal itself and the vehicle ahead.

[0048] 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 (that is, 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 the infrared imaging device and collect environmental data using the wind speed and direction meter.

[0049] Based on the obtained ground image, vehicle model (identified from the vehicle ahead image), and current wind speed and direction data, the system can accurately calculate the comprehensive running 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.

[0050] By analyzing the current driving speed and the comprehensive running resistance value of the vehicle ahead, the system can predict the time when the vehicle stops driving. This is especially important for the vehicle behind, because it can help the driver take measures in advance, such as decelerating or adjusting the vehicle distance, so as to effectively prevent the occurrence of rear-end collisions.

[0051] The vehicle-mounted terminal issues a warning message of the vehicle stop driving time according to the above analysis results, providing timely and important decision-making support for the driver, not only improving the safety of personal driving, but also indirectly promoting the safety and efficiency of the overall road traffic.

[0052] In some embodiments, the vehicle ahead image includes video data of the vehicle ahead; such as Figure 2As 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: Step S210, identifying the historical vehicle speed change data of the vehicle ahead based on the video data; Step S220, 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 rule model of the vehicle ahead; wherein, the first vehicle speed operation rule model is used to characterize 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; Step S230, predicting the first next moment vehicle speed change data corresponding to the current moment through the first vehicle speed operation rule model according to the real-time temperature at the exhaust pipe at the current moment; Step S240, controlling the in-vehicle terminal of its own to send out the second prompt information about the predicted situation of the vehicle speed change of the vehicle ahead based on the first next moment vehicle speed change data.

[0053] 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 samples, the trained binary regression analysis model (i.e., the first vehicle speed operation rule model) can more accurately capture the key factors affecting the vehicle speed change and the relationship between them. In particular, by using the parameter of the exhaust pipe temperature during vehicle operation, which is indirect but closely related, the model is more accurate in predicting the vehicle speed change.

[0054] 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., thereby effectively reducing the occurrence of rear-end collisions and improving driving safety.

[0055] 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 their driving strategy, avoid frequent braking and accelerating, thereby saving fuel and reducing vehicle wear.

[0056] In some embodiments, after the above step S150, the method may further include the following steps: analyzing the vehicle speed adaptation range of its own in-vehicle terminal for the vehicle ahead through the AI algorithm according to the upcoming vehicle speed change data of the vehicle ahead; controlling 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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, based on the image of the tire, identify the degree of splashing of water or snow between the tire of the vehicle ahead and the ground; if it is identified that the current climate condition belongs to a non-rain or snow climate, based on the image of the tire, identify the degree of dust generation between the tire of the vehicle ahead and the ground; based on the video data, identify the real-time vehicle speed change data of the vehicle ahead, 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 represent 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; based on the second next moment vehicle speed change data, control its own vehicle-mounted terminal to send out the fourth prompt information about the predicted situation of the next moment speed change of the vehicle ahead.

[0063] 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 its next moment vehicle speed change.

[0064] 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.

[0065] In addition to enhancing 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 vehicle speed change of the vehicle ahead, reduce unnecessary sudden braking and rapid acceleration, and make the driving process more comfortable.

[0066] 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 improve the road traffic efficiency.

[0067] 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, thereby significantly improving driving safety, driving experience and overall traffic flow.

[0068] Figure 3 A structural schematic diagram of a data processing device for the vehicle ahead based on the YTS system is provided. This device can be applied to the control system of the in-vehicle terminal itself, and an infrared imaging device is set on the in-vehicle terminal itself. As Figure 3 shown, the data processing device 300 for the vehicle ahead based on the YTS system includes: An acquisition module 301, configured to acquire an image of the vehicle ahead corresponding to the in-vehicle terminal itself through the infrared imaging device; 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 by the YTS system of the in-vehicle terminal itself, 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; 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; 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; 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, it corresponds to the vehicle ahead 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 ahead 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 ahead 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; An emission module 306, configured to control the in-vehicle terminal itself 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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 this system network element and at least one other network element can be realized. The Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0073] 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

[0074] to represent, but it does not mean that there is only one bus or one type of bus.

[0075] The processor 402 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 402 or 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 a hardware decoding processor, or 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.

[0076] Corresponding to the above-mentioned method for processing the data of the vehicle ahead based on the YTS system, an embodiment of the present application also provides a computer-readable storage medium, and 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 steps of the above-mentioned method for processing the data of the vehicle ahead based on the YTS system.

[0077] The device for processing the data of the vehicle ahead 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 generated 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 systems, devices, and units described above can all refer to the corresponding processes in the above method embodiments, and will not be repeated here.

[0078] 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0079] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the 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 may 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 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, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0080] The units described as separate components may or may not be physically separated. 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.

[0081] In addition, the functional units in the embodiments provided in the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0082] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they 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 aforementioned 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.

[0083] 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 descriptive distinction and cannot be understood as indicating or implying relative importance.

[0084] Finally, it should be noted that: the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solution 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 in this application can still modify the technical solution described in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of this application. All should be covered by 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 preceding vehicle data based on a YTS system, characterized in that: The vehicle-mounted terminal is provided with an infrared camera device, and the method comprises: Acquire a front vehicle image corresponding to the front vehicle of the vehicle-mounted terminal by the infrared camera device; In the process of rendering the image of the vehicle in front by the YTS system of the vehicle-mounted terminal itself, the license plate image in the image of the vehicle in front is identified to obtain a license plate recognition result, and it is determined whether the vehicle in front is a gasoline vehicle or an electric vehicle according to the license plate recognition result; If the vehicle ahead is a gasoline vehicle, the infrared camera device is used to identify the real-time temperature of the exhaust pipe of the vehicle ahead, and the heat change data of the exhaust heat of the vehicle ahead over time is determined 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; wherein the greater the heat increase change degree in the heat change data, the greater the throttle operation degree; Predicting the upcoming speed change data of the vehicle ahead according to the throttle operation degree; when the throttle operation degree increases, the vehicle ahead is about to perform an acceleration operation, and the corresponding upcoming speed change data is an increase in speed; when the throttle operation degree decreases, the vehicle ahead is about to perform a deceleration operation, and the corresponding upcoming speed change data is a decrease in speed; when the throttle operation degree remains unchanged, the vehicle ahead is about to perform a constant speed operation or a deceleration operation, and the corresponding upcoming speed change data is an unchanged speed or a reduced speed; Based on the upcoming speed change data of the front vehicle, the vehicle-mounted terminal is controlled to issue first prompt information of the upcoming speed change prediction of the front vehicle.

2. The method according to claim 1, characterized in that The vehicle-mounted terminal is also provided with an anemometer; after determining the heat change data of the exhaust heat of the vehicle ahead over time according to the real-time temperature, it also includes: If the degree of heat reduction in the heat change data is greater than the preset temperature reduction degree, it is determined that the current throttle operation degree is completely non-operation of the throttle, and a ground image of the ground where the front vehicle is located is obtained by the infrared camera device, and current wind speed and direction data are obtained by the anemometer; identifying the texture data of the ground on which the front vehicle is traveling based on the ground image, identifying the 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 running resistance value of the front vehicle according to the running resistance value of the vehicle weight relative to the front vehicle, 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 front vehicle; When the front vehicle remains in the state where the throttle is not operated at all, the vehicle stopping time of the front vehicle is predicted according to the current driving speed of the front vehicle and the comprehensive driving resistance value, and the own vehicle-mounted terminal is controlled to issue a warning message of the vehicle stopping time to avoid a rear-end collision between the own vehicle-mounted terminal and the front vehicle.

3. The method according to claim 1, characterized in that The front vehicle image includes video data of the front vehicle; After the infrared camera device is used to identify the real-time temperature of the exhaust pipe of the front vehicle, the method further includes: Identifying historical vehicle speed change data of the leading vehicle based on the video data; Determine the historical vehicle speed change data and the real-time temperature at the exhaust pipe corresponding to the previous moment as a first training sample, and train an initial binary regression analysis model based on the first training sample to obtain a first vehicle speed operation law model of the front vehicle; 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 front vehicle and the vehicle speed change data at the next moment; According to the real-time temperature at the exhaust pipe at the current moment, predicting the first vehicle speed change data corresponding to the next moment after the current moment by using the first vehicle speed operation law model; Based on the first vehicle speed change data at the next moment, the vehicle-mounted terminal is controlled to issue a second prompt information on the predicted speed change of the preceding vehicle at the next moment.

4. The method according to claim 1, characterized in that: After predicting the upcoming speed change data of the vehicle ahead according to the throttle operation degree, the method further includes: According to the upcoming speed change data of the front vehicle, analyzing the speed adaptation range of the vehicle terminal for the front vehicle through an AI algorithm; Based on the vehicle speed adaptation range, the vehicle itself is controlled to send a third prompt message of the current vehicle speed suggestion.

5. The method according to claim 1, characterized in that The front vehicle image includes video data of the front vehicle; After the step of determining whether the vehicle ahead is a gasoline vehicle or an electric vehicle according to the license plate recognition result, the method further includes: In the case where the vehicle ahead is an electric vehicle, using the infrared camera device to obtain an image of the tire of the vehicle ahead, and using the infrared camera device to identify the current climate conditions; If it is identified that the current climate condition is rainy and snowy, then identifying the extent of splashing of water or snow between the tires of the vehicle ahead and the ground based on the tire image; If it is identified that the current climate condition is a non-rainy and snowy climate, identifying the degree of dust generation between the tires of the front vehicle and the ground based on the tire image; Based on the video data, the real-time vehicle speed change data of the vehicle in front is identified, and the dust generation degree or the splashing degree and the real-time vehicle speed change data corresponding to the next moment are determined as second training samples, and an initial multivariate regression analysis model is trained based on the second training samples to obtain a second vehicle speed operation law model of the vehicle in front; 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 in front and the real-time vehicle speed change data corresponding to the next moment; According to the dust generation degree or the splashing degree at the current moment, predicting the second vehicle speed change data corresponding to the next moment after the current moment by using the second vehicle speed operation law model; Based on the second next moment vehicle speed change data, the own vehicle-mounted terminal is controlled to issue fourth prompt information of the predicted speed change of the leading vehicle at the next moment.

6. The method according to claim 1, characterized in that The vehicle-mounted terminal is also provided with an anemometer and an anemometer; the prediction of the speed change data of the vehicle ahead according to the throttle operation degree includes: When the throttle operation degree remains unchanged, a ground image of the ground where the front vehicle is located is obtained by the infrared camera device, and current wind speed and direction data is obtained by the anemometer; identifying the texture data of the ground on which the front vehicle is traveling based on the ground image, identifying the model of the front vehicle based on the vehicle image of the front vehicle, and determining the vehicle weight of the front vehicle according to the vehicle model; If the sum of the vehicle weight relative to the running resistance value of the vehicle in front, 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 in front is greater than the preset resistance value, it is predicted that the vehicle in front is about to perform a deceleration driving action, and the corresponding upcoming driving speed change data is determined to be a speed reduction.

7. The method according to claim 6, characterized in that After identifying the ground texture data corresponding to the driving of the front vehicle based on the ground image, identifying the vehicle model of the front vehicle based on the vehicle image of the front vehicle, and determining the vehicle weight of the front vehicle according to the vehicle model, the method further includes: If the sum of the vehicle weight relative to the running resistance value of the vehicle in front, 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 in front is less than or equal to the preset resistance value, it is predicted that the vehicle in front is about to perform a uniform speed driving action, and the corresponding upcoming driving speed change data is determined to be the speed remaining unchanged.

8. A preceding vehicle data processing device based on the YTS system, characterized in that: The vehicle-mounted terminal is provided with an infrared camera device, which includes: An acquisition module, used for acquiring a front vehicle image corresponding to the front vehicle of the vehicle-mounted terminal itself through the infrared camera device; A judgment module, used for identifying the license plate image in the image of the vehicle in front during the rendering process of the image of the vehicle in front by the YTS system of the vehicle-mounted terminal itself, obtaining a license plate recognition result, and judging whether the vehicle in front is a gasoline vehicle or an electric vehicle according to the license plate recognition result; an identification module, for identifying the real-time temperature of the exhaust pipe of the front vehicle by using the infrared camera device if the front vehicle is a gasoline vehicle, and determining the heat change data of the exhaust heat of the front vehicle over time according to the real-time temperature; An analysis module, 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 in the heat change data, the greater the throttle operation degree; A prediction module is used to predict the upcoming speed change data of the vehicle ahead according to the throttle operation degree; when the throttle operation degree increases, the vehicle ahead is about to perform an acceleration operation, and the corresponding upcoming speed change data is an increase in speed; when the throttle operation degree decreases, the vehicle ahead is about to perform a deceleration operation, and the corresponding upcoming speed change data is a decrease in speed; when the throttle operation degree remains unchanged, the vehicle ahead is about to perform a constant speed operation or a deceleration operation, and the corresponding upcoming speed change data is an unchanged speed or a reduced speed; The issuing module is used to control the vehicle-mounted terminal to issue the first prompt information of the predicted speed change of the preceding vehicle based on the upcoming speed change data of the preceding vehicle.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method according to any one of claims 1 to 7.

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