Vehicle early warning method and device, vehicle and computer readable storage medium

Abstract

The embodiment of the invention provides a vehicle early warning method and device, a vehicle and a computer readable storage medium. The method comprises the steps that environment data of the vehicle in the driving process are collected; vehicle features, road features and weather features of the environment are extracted from the environment data, the vehicle features are used for representing vehicle density on the road, the road features are used for representing road quality of the road, and the weather features are used for representing weather conditions of weather in the environment; based on the vehicle characteristics, the road characteristics and the weather characteristics, determining a driving performance index of the vehicle, the driving performance index being used for representing a driving difficulty degree of the vehicle under the environmental condition; based on the driving performance index, early warning information is generated, and the early warning information is used for prompting the current driving difficulty degree of the vehicle; and controlling the vehicle to output early warning information. The technical problem of low vehicle driving safety caused by excessive dependence on a driving assistance system is solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of vehicles, in particular, to a vehicle early warning method and device, a vehicle and a computer readable storage medium. BACKGROUND

[0002] With the rapid development of vehicle intelligent technology, the driving assistance system has become an important part of modern vehicles. In the automatic driving mode, the vehicle can autonomously complete a series of driving operations such as route planning, speed control, obstacle avoidance, etc. This not only simplifies the driving difficulty, but also greatly reduces the driver's participation and the interest of driving. In the long-term driving process, the driver of the vehicle may become too dependent on the driving assistance system due to lack of actual operation, attention decreases, and even in an emergency, the reaction is slow, affecting the driving safety of the vehicle.

[0003] At present, there is no good solution to the above problems. SUMMARY

[0004] Embodiments of the present application provide a vehicle early warning method, device, vehicle and computer readable storage medium to at least solve the technical problem of low vehicle driving safety caused by excessive dependence on driving assistance systems.

[0005] According to an aspect of an embodiment of the present application, a vehicle early warning method is provided. The method comprises: collecting environmental data of the vehicle in the driving process, wherein the environmental data is used to represent the environmental condition of the environment in which the vehicle is currently located; extracting the vehicle feature on the road currently driven by the vehicle, the road feature of the road, and the weather feature of the environment from the environmental data, wherein the vehicle feature is used to represent the vehicle density on the road, the road feature is used to represent the road quality of the road, and the weather feature is used to represent the weather condition of the weather in the environment; determining the driving performance index of the vehicle based on the vehicle feature, the road feature and the weather feature, wherein the driving performance index is used to represent the driving difficulty of the vehicle under the environmental condition; generating early warning information based on the driving performance index, wherein the early warning information is used to prompt the driving difficulty of the vehicle; and controlling the vehicle to output the early warning information.

[0006] Further, the extraction of the vehicle feature on the road currently driven by the vehicle, the road feature of the road, and the weather feature of the environment from the environmental data comprises: preprocessing the environmental data to obtain preprocessed environmental data, wherein the preprocessing at least includes denoising processing and data enhancement processing; using a deep learning algorithm to identify the number of vehicles on the road currently driven by the vehicle, the road surface flatness of the road, and the weather type and the environmental temperature in the environment from the preprocessed environmental data; determining the vehicle feature based on the number of vehicles, determining the road feature based on the road surface flatness, and determining the weather feature based on the weather type and the environmental temperature.

[0007] Further, based on the vehicle feature, the road feature and the weather feature, a driving performance index of the vehicle is determined, including: based on the vehicle feature, a congestion degree score of a road currently traveled by the vehicle is determined, based on the road feature, a road condition quality score of the road currently traveled by the vehicle is determined, and based on the weather feature, a weather score of an environment currently located by the vehicle is determined; based on the congestion degree score, the road condition quality score and the weather score, the driving performance index of the vehicle is determined.

[0008] Further, based on the vehicle feature, the road feature and the weather feature, a driving performance index of the vehicle is determined, including: based on the vehicle feature, a congestion degree score of a road currently traveled by the vehicle is determined, based on the road feature, a road condition quality score of the road currently traveled by the vehicle is determined, and based on the weather feature, a weather score of an environment currently located by the vehicle is determined; based on the congestion degree score, the road condition quality score and the weather score, the driving performance index of the vehicle is determined.

[0009] Further, based on the vehicle feature, the road feature and the weather feature, a driving performance index of the vehicle is determined, including: based on the vehicle feature, a congestion degree score of a road currently traveled by the vehicle is determined, based on the road feature, a road condition quality score of the road currently traveled by the vehicle is determined, and based on the weather feature, a weather score of an environment currently located by the vehicle is determined; based on the congestion degree score, the road condition quality score and the weather score, the driving performance index of the vehicle is determined.

[0010] Further, based on the driving performance index, the warning information is generated, including: in response to an index value of the driving performance index being greater than a target index threshold value, a target warning rule corresponding to the target index threshold value is determined as a target warning rule corresponding to the driving performance index, wherein the target warning rule is used to represent a generation rule of the warning information, and different index values correspond to different warning rules; based on the target warning rule, the warning information is generated.

[0011] Further, the vehicle outputs the warning information, including: according to a preset display rule, the warning information is displayed on a display screen of the vehicle, wherein the display rule is used to represent a display duration and a display manner of the warning information.

[0012] According to a further aspect of the embodiments of the present application, a pre-warning device of a vehicle is provided. The device comprises: a collecting unit configured to collect environment data of the vehicle during driving, wherein the environment data is used to represent an environment condition of an environment where the vehicle is currently located; an extracting unit configured to extract, from the environment data, a vehicle feature of vehicles on a road where the vehicle is currently located, a road feature of the road, and a weather feature of the environment, wherein the vehicle feature is used to represent a vehicle density on the road, the road feature is used to represent a road quality of the road, and the weather feature is used to represent a weather condition of weather in the environment; a determining unit configured to determine a driving performance index of the vehicle based on the vehicle feature, the road feature, and the weather feature, wherein the driving performance index is used to represent a driving difficulty level of the vehicle under the environment condition; a generating unit configured to generate a pre-warning information based on the driving performance index, wherein the pre-warning information is used to prompt the driving difficulty level of the vehicle; and an output unit configured to control the vehicle to output the pre-warning information.

[0013] According to a further aspect of the embodiments of the present application, a vehicle is provided. The vehicle comprises: a memory configured to store an executable program; and a processor configured to execute the program, wherein the program is executed to perform the method in the embodiments of the present application.

[0014] According to a further aspect of the embodiments of the present application, a computer readable storage medium is provided. The computer readable storage medium comprises an executable program stored therein, wherein the executable program is executed to control a device where the computer readable storage medium is located to perform the method in the embodiments of the present application.

[0015] According to a further aspect of the embodiments of the present application, a computer program product is provided. The computer program product comprises a computer program, and the computer program is executed by a processor to implement the method in the embodiments of the present application.

[0016] According to a further aspect of the embodiments of the present application, a computer program product is provided. The computer program product comprises a non-volatile computer readable storage medium, and the non-volatile computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the method in the embodiments of the present application.

[0017] According to a further aspect of the embodiments of the present application, a computer program is provided. The computer program is executed by a processor to implement the method in the embodiments of the present application.

[0018] In the embodiment of the present application, the environment data of the vehicle during driving is collected, wherein the environment data is used to represent the environment condition of the current environment of the vehicle; from the environment data, the vehicle features on the road where the vehicle is currently driving, the road features of the road, and the weather features of the environment are extracted, wherein the vehicle features are used to represent the vehicle density on the road, the road features are used to represent the road quality of the road, and the weather features are used to represent the weather condition of the weather in the environment; based on the vehicle features, the road features and the weather features, the driving performance index of the vehicle is determined, wherein the driving performance index is used to represent the driving difficulty of the vehicle under the environment condition; based on the driving performance index, the warning information is generated, wherein the warning information is used to prompt the driving difficulty of the vehicle currently driving; and the vehicle outputs the warning information. That is, in the embodiment of the present application, during the driving of the vehicle, the vehicle features on the road where the vehicle is currently driving, the road features of the road, and the weather features of the environment can be determined according to the environment data around the vehicle, and then the driving performance index is calculated according to the vehicle features, the road features and the weather features, a comprehensive driving difficulty evaluation result is provided for the driver, and the warning information is generated. Through real-time feedback of the driving difficulty and generation of the corresponding warning information, the enthusiasm and concentration of the driver can be improved in the process of relying on the automatic driving of the vehicle, and then the driving safety of the vehicle is ensured, and potential traffic accidents are avoided, so as to solve the technical problem of low vehicle driving safety caused by excessive dependence on the driving assistance system in the related art. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which are included to provide a further understanding of the present application and constitute a part of this application, illustrate certain illustrative embodiments of the present application and together with the description serve to explain the present application. In the drawings:

[0020] Figure 1 is a flowchart of a warning method of a vehicle according to an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of a warning system of a vehicle according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of a control interface of a warning system of a vehicle according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of a warning device of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application, so that those skilled in the art can better understand the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to the embodiments of the present application, an embodiment of a vehicle warning method is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0027] In the present embodiment, a vehicle warning method is provided, Figure 1 is a flowchart of a vehicle warning method according to an embodiment of the present application, as Figure 1 shown, the flow includes the following steps:

[0028] Step S101, collecting environmental data of the vehicle during driving.

[0029] In the technical solution provided in the above step S101 of the present application, the environmental data is used to represent the environmental condition of the environment in which the vehicle is currently located.

[0030] In this embodiment, during the driving of the vehicle, various on-board sensors and devices can be used to collect environmental data, including but not limited to: high-definition cameras, radars and lidars (LiDAR), ultrasonic sensors, ultrasonic sensors, global positioning systems (GPS) and map data, etc. Among them, high-definition cameras are used to capture road images in front of, behind and on the sides of the vehicle; radars and lidars (LiDAR) can measure the distance between the vehicle and the surrounding objects, helping the vehicle's assisted driving system to understand the traffic density and the position of the front obstacles in real time; ultrasonic sensors are usually used for close-range obstacle detection, especially when driving at low speed or parking, which can provide detailed perception of the surrounding environment; ultrasonic sensors are used to collect real-time weather information, including temperature, humidity, wind speed, visibility, etc., to assess the impact of weather on driving difficulty; GPS and map data provide accurate location and driving route information for the vehicle, which can predict the driving difficulty of the front section in combination with the current traffic conditions.

[0031] In this step, during the driving of the vehicle, all-around information of the environment around the vehicle can be obtained to help the vehicle's assisted driving system to identify potential driving risks and provide a data basis for the subsequent driving difficulty score of the vehicle.

[0032] Step S102, from the environmental data, extracting vehicle features on the road currently driven by the vehicle, road features of the road, and weather features of the environment.

[0033] In the technical solution provided by the above step S102 of the present application, the above-mentioned vehicle features are used to represent the vehicle density on the road currently driven by the vehicle, the above-mentioned road features are used to represent the road quality of the road currently driven by the vehicle, and the above-mentioned weather features are used to represent the weather conditions of the weather in the environment currently where the vehicle is located.

[0034] In this embodiment, since the obtained environmental data usually includes noise, based on this, after obtaining the environmental data, the environmental data can be pre-processed first, for example, by filtering, smoothing and other methods to pre-process the environmental data, to reduce or eliminate noise, improve the purity and signal-to-noise ratio of the environmental data. Moreover, since the environmental data from different sensors may be inconsistent or biased, the environmental data will also be calibrated as necessary in the preprocessing stage to ensure that the data from different sensors are compared under the same reference. This includes time and space synchronization, unit conversion and standardization of device characteristics. After obtaining the pre-processed environmental data, the above-mentioned vehicle features, road features of the road, and weather features of the environment can be extracted from the pre-processed environmental data.

[0035] Optionally, the vehicle feature is mainly used to quantify the vehicle density on the road. As introduced in the foregoing, the environmental data includes real-time image data captured by sensors such as cameras and radars, based on which advanced image processing techniques such as edge detection, feature point matching and pattern recognition can be used to identify other vehicles on the road. Further, through deep learning algorithms such as convolutional neural networks, information such as the position, size and direction of the vehicle can be automatically extracted from the image, realizing accurate detection and tracking of the vehicle. By counting the number of vehicles in a certain area or time window, the vehicle density can be calculated, and then the vehicle feature can be obtained.

[0036] Optionally, the road feature reflects the quality and structure of the road the vehicle is currently driving on, which has a great influence on the driving safety and difficulty of the vehicle. The road feature includes but is not limited to the flatness of the road surface, the clarity of the road sign, the width of the road and the straightness of the road, etc. Using high-precision map data combined with sensor-collected information, the road condition of the road the vehicle is currently driving on can be evaluated. For example, through the images captured by the camera, it can be identified whether there are cracks, potholes or other obstacles on the road; radars and LiDARs can measure the smoothness of the road surface. In addition, the visibility and clarity of the road signs can also be analyzed, which are key indicators for judging the quality of the road.

[0037] Optionally, the weather feature covers a series of meteorological parameters for describing the weather conditions in the driving environment, including temperature, humidity, visibility and rainfall, etc. Through meteorological sensors installed on the vehicle, such as temperature sensors, humidity sensors and raindrop sensors, these data can be obtained in real time. Changes in weather conditions are directly related to the quality of driving conditions, for example, rain and fog will reduce visibility and increase driving difficulty; high or low temperature also has an undeniable impact on tire grip and vehicle performance.

[0038] Optionally, after the extraction of the vehicle feature, the road feature of the road and the weather feature of the environment is completed, the following step S103 can be performed to comprehensively analyze these data and determine the driving performance index of the vehicle.

[0039] Step S103, determining the driving performance index of the vehicle based on the vehicle feature, the road feature and the weather feature.

[0040] In the technical solution provided by the above step S103 of the present application, the driving performance index is used to represent the driving difficulty of the vehicle under the current environmental conditions.

[0041] In this embodiment, the congestion degree score of the road currently traveled by the vehicle can be evaluated according to the vehicle characteristics, wherein the congestion degree score is used to evaluate the congestion degree of the road currently traveled by the vehicle. The road condition quality score of the road currently traveled by the vehicle can be evaluated according to the road characteristics, wherein the road condition quality score is used to represent the road quality of the road currently traveled by the vehicle. The weather score of the environment currently traveled by the vehicle can be evaluated according to the weather characteristics, wherein the weather score is used to represent the weather condition of the environment currently traveled by the vehicle. After obtaining the congestion degree score, the road condition quality score, and the weather score, the congestion degree score, the road condition quality score, and the weather score can be comprehensively analyzed to obtain the driving performance indicator of the vehicle.

[0042] For example, the congestion degree score, the road condition quality score, and the weather score can be weighted according to the corresponding weight coefficients to obtain the driving difficulty score of the vehicle, and then the driving performance indicator of the vehicle can be determined according to the driving difficulty score, wherein the higher the driving difficulty score, the greater the driving difficulty corresponding to the driving performance indicator of the vehicle.

[0043] In step S104, the warning information is generated based on the driving performance indicator.

[0044] In the technical solution provided in the above step S104 of the present application, the warning information is used to prompt the difficulty of the current driving of the vehicle.

[0045] In this embodiment, whether to generate the warning information can be determined according to the indicator value of the driving performance indicator, wherein the indicator value of the driving performance indicator can be the driving difficulty score, or a value redefined according to the driving difficulty of the vehicle.

[0046] For example, the indicator value of the driving performance indicator can be compared with a preset warning threshold to determine whether the current driving difficulty reaches the warning standard, wherein the warning threshold can be adjusted according to different driving environments, weather conditions, and road conditions to adapt to various complex driving scenarios.

[0047] Optionally, the generation rule of the current warning information can be determined according to the comparison result of the indicator value of the driving performance indicator and the preset warning threshold. For example, if the indicator value of the driving performance indicator is greater than a certain warning threshold, the warning information can be generated according to the generation rule of the warning information corresponding to the warning threshold, wherein each warning threshold corresponds to a generation rule of the warning information.

[0048] In step S105, the vehicle is controlled to output the warning information.

[0049] In the technical solution provided in the above step S105 of the present application, after the warning information is generated, the vehicle can be controlled to output the warning information.

[0050] In this embodiment, the pre-warning information can be output in a pre-defined output mode, wherein the output mode of the pre-warning information allows the driver to customize. For example, a sound warning can be received when a certain difficulty level is reached, or a text prompt can be displayed on the display screen of the vehicle. Through the switches, reminder intervals and alarm thresholds set on the display screen, the driver can adjust the triggering conditions and frequency of the pre-warning information according to his own preferences and driving habits to ensure the effectiveness and non-intrusiveness of the pre-warning information.

[0051] Optionally, the pre-warning information can be output through multiple channels, in addition to visual and auditory warnings, it can also be conveyed to the driver through tactile feedback (such as steering wheel vibration) to ensure that the driver's attention can be aroused in different driving situations.

[0052] In steps S101-S105 described above, during the driving of the vehicle, the vehicle feature on the road currently driven by the vehicle, the road feature of the road, and the weather feature of the environment can be determined according to the environmental data of the vehicle surroundings, and then the driving performance index can be calculated according to the vehicle feature, the road feature and the weather feature, so as to provide a comprehensive driving difficulty evaluation result for the driver and generate pre-warning information. By feeding back the driving difficulty in real time and generating corresponding pre-warning information, the enthusiasm and concentration of the driver can be improved in the process of relying on automatic driving of the vehicle, and the driving safety of the vehicle can be ensured, and potential traffic accidents can be avoided, so as to solve the technical problem of low vehicle driving safety in the related art.

[0053] The above-mentioned pre-warning method of the vehicle of the present application will be further introduced below.

[0054] As an optional implementation, in step S102, the vehicle feature on the road currently driven by the vehicle, the road feature of the road, and the weather feature of the environment are extracted from the environmental data, including: pre-processing the environmental data to obtain pre-processed environmental data, wherein the pre-processing at least includes denoising processing and data enhancement processing; using a deep learning algorithm to identify the number of vehicles on the road currently driven by the vehicle, the road surface flatness of the road, and the weather type and the environmental temperature in the environment from the pre-processed environmental data; determining the vehicle feature based on the number of vehicles, determining the road feature based on the road surface flatness, and determining the weather feature based on the weather type and the environmental temperature.

[0055] In this embodiment, the purpose of preprocessing the environmental data is to improve the data quality and provide more reliable data for subsequent data processing. The preprocessing can at least include denoising data and data enhancement processing. Since the environmental data usually contains various noises, which can be caused by the limitations of the sensor itself, the interference of the external environment, or the distortion in the data transmission process. Therefore, by denoising the environmental data, the purity and signal-to-noise ratio of the environmental data can be improved. The purpose of data enhancement is to enhance the features in the environmental data. For example, by performing contrast enhancement and sharpening on the image data in the environmental data, the vehicle, pedestrian, and obstacle can be more obvious, which is convenient for subsequent detection and recognition.

[0056] Optionally, in addition to denoising and data enhancement processing, the environmental data can also be subjected to format unification processing. Since the data can exist in different formats, the preprocessing can convert these data into a unified or compatible format, which is convenient for subsequent algorithm processing.

[0057] Optionally, after preprocessing the environmental data, the preprocessed environmental data can be input into a deep learning algorithm for feature recognition.

[0058] For example, the deep learning algorithm can identify the number of vehicles and their distribution on the road currently traveled by the vehicle according to the preprocessed environmental data, to provide a data basis for subsequent evaluation of traffic congestion. The road surface flatness of the road currently traveled by the vehicle can also be identified according to the preprocessed environmental data, to provide a data basis for subsequent evaluation of road quality. The weather condition (e.g., sunny, rainy, snowy) and the environmental temperature of the environment currently traveled by the vehicle can also be determined according to the preprocessed environmental data, to provide a data basis for subsequent evaluation of the weather condition of the environment traveled by the vehicle.

[0059] Optionally, after identifying the number of vehicles and their distribution, the road surface flatness, and the weather condition and the environmental temperature of the environment currently traveled by the vehicle, the vehicle characteristics can be determined according to the number of vehicles, the road characteristics can be determined based on the road surface flatness, and the weather characteristics can be determined based on the weather type and the environmental temperature, wherein the vehicle characteristics are used to reflect the traffic congestion degree of the road currently traveled by the vehicle, the road characteristics are used to represent the road quality of the road currently traveled by the vehicle, and the weather characteristics are used to represent the weather condition of the environment currently traveled by the vehicle.

[0060] As an optional implementation, in step S103, the driving performance index of the vehicle is determined based on the vehicle feature, the road feature and the weather feature, including: determining a congestion degree score of the road currently traveled by the vehicle based on the vehicle feature, determining a road condition quality score of the road currently traveled by the vehicle based on the road feature, and determining a weather score of the environment currently traveled by the vehicle based on the weather feature; and determining the driving performance index of the vehicle based on the congestion degree score, the road condition quality score and the weather score.

[0061] In this embodiment, after obtaining the vehicle feature, the road feature and the weather feature, corresponding scores can be determined according to the vehicle feature, the road feature and the weather feature respectively.

[0062] For example, for the vehicle feature, the vehicle feature can be analyzed to determine the traffic density and vehicle spacing on the road currently traveled by the vehicle, and then the congestion degree score of the road currently traveled by the vehicle is determined according to these parameters. For the road feature, the road feature can be analyzed to determine the road flatness, road water or snow conditions of the road currently traveled by the vehicle, and then the road condition quality score of the road currently traveled by the vehicle is determined. For the weather feature, the weather feature can be analyzed to determine whether the environment currently traveled by the vehicle is rainy, snowy, foggy weather, and the temperature, humidity, visibility and the like of the environment currently traveled by the vehicle, and then the weather score of the environment currently traveled by the vehicle is determined according to these information.

[0063] Optionally, after the congestion degree score, the road condition quality score and the weather score are determined, the congestion degree score, the road condition quality score and the weather score can be comprehensively analyzed to obtain the driving performance index of the vehicle, and then the driving difficulty of the road currently traveled by the vehicle is determined according to the driving performance index.

[0064] As an optional implementation, the congestion degree score of the road currently traveled by the vehicle is determined based on the vehicle feature, the road condition quality score of the road currently traveled by the vehicle is determined based on the road feature, and the weather score of the environment currently traveled by the vehicle is determined based on the weather feature, including: determining the traffic congestion degree on the road currently traveled by the vehicle based on the vehicle feature; determining the congestion degree score based on the traffic congestion degree and a preset congestion degree score rule; determining the road flatness on the road currently traveled by the vehicle based on the road feature; determining the road condition quality score based on the road flatness and a preset road condition quality score rule; determining the weather condition of the environment currently traveled by the vehicle based on the weather feature; and determining the weather score based on the weather condition and a preset weather score rule.

[0065] In this embodiment, when determining the congestion degree score of the road currently traveled by the vehicle according to the vehicle feature, the vehicle density on the road currently traveled by the vehicle can be determined according to the vehicle feature, so as to determine the traffic congestion degree on the road currently traveled by the vehicle, and then the congestion degree score of the road currently traveled by the vehicle is determined according to the traffic congestion degree and the preset congestion degree score rule, wherein the preset congestion degree score rule includes a mapping relationship between the congestion degree and the congestion degree score.

[0066] Optionally, when determining the road condition quality score of the road currently traveled by the vehicle according to the road feature, the road surface flatness of the road currently traveled by the vehicle can be determined according to the road feature, and then the road condition quality score of the road currently traveled by the vehicle is determined according to the determined road surface flatness and the preset road condition quality score rule, wherein the preset road condition quality score rule includes a mapping relationship between the road surface flatness and the road condition quality score.

[0067] Optionally, when determining the weather score of the environment currently traveled by the vehicle according to the weather feature, the weather condition of the environment currently traveled by the vehicle can be determined according to the weather feature, and then the weather score is determined according to the weather condition and the preset weather score rule, wherein the preset weather score rule includes a mapping relationship between the weather condition and the weather score.

[0068] Optionally, after the congestion degree score, the road condition quality score and the weather score are determined, the congestion degree score, the road condition quality score and the weather score can be comprehensively analyzed to obtain the driving performance index of the vehicle.

[0069] As an optional implementation, the driving performance index of the vehicle is determined based on the congestion degree score, the road condition quality score and the weather score, including: determining a first weight coefficient corresponding to the congestion degree score, a second weight coefficient corresponding to the road condition quality score and a third weight coefficient corresponding to the weather score; performing weighted calculation on the congestion degree score and the first weight coefficient, the road condition quality score and the second weight coefficient, and the weather score and the third weight coefficient to obtain a driving difficulty score of the vehicle; and determining the driving performance index of the vehicle based on the driving difficulty score.

[0070] In this embodiment, based on the congestion degree score, the road condition quality score, and the weather score, the driving performance indicator of the vehicle is determined. The first weight coefficient corresponding to the congestion degree score, the second weight coefficient corresponding to the road condition quality score, and the third weight coefficient corresponding to the weather score can be determined respectively. The first weight coefficient is used to represent the influence degree of traffic congestion on the driving performance indicator of the vehicle. For example, in urban roads or busy highways, high-density traffic flow increases the complexity and fatigue of vehicle driving. Therefore, a higher first weight coefficient means that more consideration will be given to traffic congestion in the calculation of the driving performance indicator, especially during peak traffic hours and in specific geographic areas. The second weight coefficient is used to represent the influence degree of road condition quality on the driving performance indicator of the vehicle. For example, damaged road surface, construction area, wet or icy road surface, etc. will increase the difficulty of vehicle driving and challenge the controllability and stability of the vehicle. By giving a proper second weight coefficient to the road condition quality score, the additional driving burden caused by poor road conditions can be more accurately evaluated, especially for heavy vehicles and high-performance vehicles. The third weight coefficient is used to represent the influence degree of weather conditions on the driving performance indicator of the vehicle. For example, severe weather conditions such as heavy rain, heavy fog, strong wind, snowstorm, etc. will significantly reduce the visibility, affect the wetness of the road and the controllability of the vehicle, and thus have a significant impact on driving difficulty. A higher third weight coefficient indicates that weather factors play an important role in evaluating driving difficulty, especially in areas with seasonal changes and frequent extreme weather. The weight of the weather score should be strengthened to ensure the timeliness and accuracy of the warning information. The first weight coefficient, the second weight coefficient, and the third weight coefficient are usually based on a large amount of driving data and expert experience, and are set by analyzing the variation of driving difficulty under different environmental factors. At the same time, the first weight coefficient, the second weight coefficient, and the third weight coefficient also support user-defined configuration.

[0071] Optionally, after determining the first weight coefficient corresponding to the congestion degree score, the second weight coefficient corresponding to the road condition quality score, and the third weight coefficient corresponding to the weather score, the congestion degree score, the road condition quality score, and the weather score can be weighted and calculated by the following formula, and then the driving difficulty score of the vehicle is obtained.

[0072] S=aA+bB+cC

[0073] Wherein, S represents the driving difficulty score of the vehicle, a can represent the first weight coefficient, for example, 30%, A can represent the congestion degree score, b can represent the second weight coefficient, for example, 30%, B can represent the road condition quality score, c can represent the third weight coefficient, for example, 40%, and C can represent the weather score.

[0074] Optionally, after determining the driving difficulty score of the vehicle according to the above formula, the driving performance indicator of the vehicle can be determined according to the driving difficulty score.

[0075] For example, if the driving difficulty score of the vehicle is less than a first difficulty score threshold (e.g., 45), it is considered that the current driving environment of the vehicle is relatively stable, and in this case, the driving performance indicator of the vehicle is determined to indicate that the current driving difficulty level of the vehicle is a first level, i.e., the current driving difficulty of the vehicle is low. If the driving difficulty score of the vehicle is greater than or equal to the first difficulty score threshold and less than a second difficulty score threshold (e.g., 75), it is considered that the current driving environment of the vehicle belongs to a medium difficulty, and in this case, the driving performance indicator of the vehicle can be determined to indicate that the current driving difficulty level of the vehicle is a second level, i.e., the current driving difficulty of the vehicle exists to a certain extent. If the driving difficulty score of the vehicle is greater than or equal to the second difficulty score threshold (e.g., 75), it is considered that the current driving environment of the vehicle belongs to a high difficulty, and in this case, the driving performance indicator of the vehicle can be determined to indicate that the current driving difficulty level of the vehicle is a third level, i.e., the current driving difficulty of the vehicle is large. That is, the higher the driving difficulty level, the greater the driving difficulty.

[0076] As an optional implementation, in step S104, the pre-warning information is generated based on the driving performance indicator, including: in response to the indicator value of the driving performance indicator being greater than a target indicator threshold, determining the pre-warning rule corresponding to the target indicator threshold as the target pre-warning rule corresponding to the driving performance indicator, wherein the target pre-warning rule is used to represent the generation rule of the pre-warning information, and different indicator values correspond to different pre-warning rules; and generating the pre-warning information based on the target pre-warning rule.

[0077] In this embodiment, after the driving performance indicator is determined, the pre-warning information can be generated according to the driving performance indicator. For example, the indicator value of the driving performance indicator is compared with a plurality of preset indicator thresholds to obtain a comparison result, and then the target pre-warning rule corresponding to the driving performance indicator of the vehicle is determined according to the comparison result, and then the pre-warning information is generated according to the target pre-warning rule. The indicator value of the driving performance indicator can be the corresponding driving difficulty score or the corresponding driving difficulty level, which is not limited here. The preset indicator threshold is a baseline for judging whether the driving difficulty reaches a degree that needs to warn the driver. Different driving environments and conditions can correspond to different preset indicator thresholds, and different preset indicator thresholds correspond to different pre-warning rules, i.e., the way of reminding the driver is different under different preset indicator thresholds. It should be noted that different preset indicator thresholds correspond to pre-warning rules supporting user self-defined configuration.

[0078] Optionally, if the comparison result indicates that the index value of the driving performance index is greater than the target index threshold, the target index threshold corresponds to the pre-warning rule, and the target pre-warning rule corresponding to the driving performance index is determined, wherein the target pre-warning rule is used to represent the generation rule of the pre-warning information, such as the specific content of the pre-warning information.

[0079] As an optional implementation, the vehicle outputs the pre-warning information, including: displaying the pre-warning information on the display screen of the vehicle according to the preset display rule, wherein the display rule is used to represent the display duration and the display mode of the pre-warning information.

[0080] In this embodiment, after the pre-warning information is determined, the vehicle outputs the pre-warning information, and before the pre-warning information is output, the preset display rule is determined, wherein the display rule can include the display duration and the display mode of the pre-warning information.

[0081] Optionally, in order to ensure that the driver has enough reaction time, the system will preset a reasonable display duration, which is usually based on the average reading speed and processing time of the driver. The display duration should be long enough for the driver to read the information completely, and should also ensure that it does not occupy the driver's attention too much, affecting the control of the vehicle. The vehicle supports various display modes, including text, icons, color coding, etc., to adapt to different driving environments and driver preferences. For example, for emergency pre-warning, a prominent red icon and flashing effect can be used; while for general pre-warning, a static yellow or orange icon can be used with simple text instructions. Various display modes can ensure that the pre-warning information can be quickly recognized by the driver in any situation.

[0082] Optionally, after the display rule is determined, the pre-warning information can be displayed on the display screen of the vehicle according to the display rule, for example, the key pre-warning information can be projected on the heads-up display (HUD), so that the driver can also pay attention to the pre-warning while observing the road conditions in front. This is only an example. In addition to displaying the pre-warning information on the display screen, the pre-warning information can also be played externally, and the steering wheel can be vibrated to attract the driver's attention.

[0083] Optionally, the above display rule supports user self-defined configuration, including selecting specific icon style, text size, color preference, etc. This personalized setting not only improves the user experience, but also ensures that the presentation of the pre-warning information can attract the driver's attention to the greatest extent, improving the reception rate of the pre-warning information. This is only an example and does not limit the specific way of the display rule.

[0084] In this step, by flexibly applying the preset display rules, it is ensured that the early warning information is conveyed to the driver in the most effective way. This mechanism not only embodies the intelligent and humanized design of the vehicle's auxiliary driving system, but also helps to continuously improve driving safety in complex and variable driving environments.

[0085] The above technical solutions of the embodiments of the present application will be further introduced by examples in combination with the preferred embodiments of the present application.

[0086] Figure 2 is a schematic diagram of a vehicle early warning system according to an embodiment of the present application, as shown in Figure 2 The vehicle early warning system 200 includes a data acquisition module 201, a data processing module 202, a data analysis module 203, and a display reminder module 204.

[0087] The data acquisition module 201 acquires real-time information such as road images, traffic density, and weather conditions during vehicle driving through high-definition cameras, radars, temperature sensors, and other intelligent sensors installed on the vehicle. These sensors can capture changes in the vehicle's surrounding environment in all directions, providing a basis for subsequent data analysis.

[0088] The data processing module 202 preprocesses the raw data collected in this stage, including image enhancement, denoising, and scaling, to ensure data quality. The preprocessed data will be input into a deep learning model for automatic identification and extraction of vehicle features, road condition features, and weather features. In addition, temperature sensor data will also be integrated to provide real-time temperature information.

[0089] The data analysis module 203 calculates the driving congestion degree score A based on the detected vehicle features, gives the road condition quality score B based on the detected road features (such as flatness and slipperiness), and calculates the weather score C based on the detected weather features. By integrating the scores of these three aspects, a comprehensive score S reflecting the driving difficulty can be obtained.

[0090] The display reminder module 204 sends the comprehensive score S and the specific scores to the display reminder module. In this display reminder module, the driver can set the on-off state of the system, the reminder interval, and the alarm threshold through the vehicle's large screen. Based on the set threshold, it is determined when and how to remind the driver. If the driving difficulty score exceeds the set early warning threshold, the corresponding warning information will be displayed on the large screen, which may include sound warnings, text prompts, or graphical displays, to ensure that the driver can quickly respond to potential driving risks.

[0091] In the vehicle's early warning system, the coordinated action of the data acquisition module 201, data processing module 202, data analysis module 203, and display and reminder module 204 enhances the driver's real-time understanding and control of the driving environment, increases the sense of participation and enjoyment during the driving process, and improves the vehicle's driving safety.

[0092] Figure 3 This is a schematic diagram of the control interface of a vehicle warning system according to an embodiment of this application. Figure 3 As shown, the control interface of the early warning system includes a driving difficulty rating switch component, a road condition quality rating switch component, a congestion level rating switch component, a weather rating switch component, an alarm switch component, an alarm threshold duration setting component, and an alert time setting component.

[0093] The driving difficulty rating switch allows drivers to enable or disable the driving difficulty rating function. When enabled, the driving difficulty is rated based on real-time road conditions, traffic density, and weather conditions, and the results are fed back to the driver. For example, drivers can choose to enable this function according to their personal needs, especially when facing unfamiliar driving environments or long-distance driving. Enabling the driving difficulty rating will provide additional driving information, helping to improve driving safety and comfort.

[0094] The Road Condition Rating switch is used to control the activation and deactivation of the road condition rating. The road condition rating analyzes factors such as road smoothness, construction conditions, and obstacles to provide drivers with information about road conditions. For example, in environments where road conditions frequently change, such as city centers or areas undergoing large-scale road construction, activating the road condition rating switch will help drivers anticipate potential driving challenges.

[0095] The congestion rating switch is used to activate or deactivate the system's monitoring and rating of traffic density and congestion. This feature is particularly important for drivers who drive during peak hours or frequently travel on busy roads. For example, during weekday morning and evening rush hours or certain holidays, turning on this switch allows the system to provide real-time congestion ratings, helping drivers plan more efficient routes and optimize driving time.

[0096] The weather rating switch component determines whether the system provides a rating based on current weather conditions. The weather rating covers various factors such as temperature, humidity, rainfall, and wind speed, helping drivers understand the impact of the external environment on driving.

[0097] The alarm switch assembly allows the driver to enable or disable the system's alarm functions according to personal preferences and the needs of the current driving environment. When the switch is in the "on" position, the system will automatically trigger an alarm when the driving difficulty reaches a preset alarm threshold; otherwise, the system will not issue any warning signal.

[0098] The alarm threshold duration setting component is used to set the alarm threshold corresponding to different scores. For example, the alarm threshold of the driving difficulty score can be set to 70, the alarm threshold of the road condition quality score can be set to 70, and so on. Here, no specific limitation is made.

[0099] The reminder time setting component allows the driver to customize the frequency and advance time of the system's warnings or notifications. This function ensures that the driver can be prepared in advance when necessary to cope with upcoming driving challenges. For example, for long-distance driving or situations that require long-term driving under specific conditions, the driver can set a shorter reminder time interval to continuously obtain the latest driving environment information. In areas with relatively stable traffic, a longer reminder time interval can be selected to reduce unnecessary information interference.

[0100] Through the control interface of the vehicle's early warning system described above, the driver can flexibly adjust system parameters according to the specific needs of the current driving environment, ensuring that the most practical driving assistance information is obtained, thereby improving the safety and comfort of driving, and reflecting the user-friendly nature and high-speed customization capabilities of the vehicle's early warning system.

[0101] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant national and regional laws, regulations and standards, and provide corresponding operation portal for user to choose authorization or refusal.

[0102] According to an embodiment of the present application, an embodiment of a vehicle early warning device is provided. It should be noted that the device can be used to execute the vehicle early warning method described above.

[0103] Figure 4 is a schematic diagram of a vehicle early warning device according to an embodiment of the present application. As shown in Figure 4 the vehicle early warning device 400 can include a collection unit 401, an extraction unit 402, a determination unit 403, a generation unit 404, and an output unit 405.

[0104] The collection unit 401 is configured to collect environmental data of the vehicle during driving, wherein the environmental data is used to represent the environmental condition of the environment where the vehicle is currently located.

[0105] The extraction unit 402 is configured to extract, from the environment data, vehicle features on a road currently traveled by the vehicle, road features of the road, and weather features of the environment, wherein the vehicle features are used to represent a vehicle density on the road, the road features are used to represent a road quality of the road, and the weather features are used to represent a weather condition of the weather in the environment.

[0106] The determination unit 403 is configured to determine a driving performance index of the vehicle based on the vehicle features, the road features, and the weather features, wherein the driving performance index is used to represent a driving difficulty of the vehicle in the environment.

[0107] The generation unit 404 is configured to generate a warning information based on the driving performance index, wherein the warning information is used to prompt a driving difficulty of the vehicle currently traveled.

[0108] The output unit 405 is configured to control the vehicle to output the warning information.

[0109] Optionally, the extraction unit 402 is further configured to: pre-process the environment data to obtain pre-processed environment data, wherein the pre-processing at least includes a denoising processing and a data enhancement processing; and identify, by using a deep learning algorithm, a number of vehicles on the road currently traveled by the vehicle, a road surface flatness of the road, and a weather type and an environment temperature in the environment from the pre-processed environment data; determine the vehicle features based on the number of vehicles, determine the road features based on the road surface flatness, and determine the weather features based on the weather type and the environment temperature.

[0110] Optionally, the determination unit 403 is further configured to: determine a congestion degree score of the road currently traveled by the vehicle based on the vehicle features, determine a road condition quality score of the road currently traveled by the vehicle based on the road features, and determine a weather score of the environment currently traveled by the vehicle based on the weather features; and determine the driving performance index of the vehicle based on the congestion degree score, the road condition quality score, and the weather score.

[0111] Optionally, the determination unit 403 is further configured to: determine a traffic congestion degree on the road currently traveled by the vehicle based on the vehicle features; determine the congestion degree score based on the traffic congestion degree and a preset congestion degree score rule; determine a road surface flatness on the road currently traveled by the vehicle based on the road features; determine the road condition quality score based on the road surface flatness and a preset road condition quality score rule; and determine a weather condition of the environment currently traveled by the vehicle based on the weather features; and determine the weather score based on the weather condition and a preset weather score rule.

[0112] Optionally, the determining unit 403 is further configured to determine a first weight coefficient corresponding to the congestion degree score, a second weight coefficient corresponding to the road condition quality score, and a third weight coefficient corresponding to the weather score; determine the driving difficulty score of the vehicle by combining the congestion degree score with the first weight coefficient, the road condition quality score with the second weight coefficient, and the weather score with the third weight coefficient; and determine the driving performance index of the vehicle based on the driving difficulty score.

[0113] Optionally, the generating unit 404 is further configured to, in response to the index value of the driving performance index being greater than a target index threshold, determine a target warning rule corresponding to the target index threshold as the target warning rule corresponding to the driving performance index, where the target warning rule is used to represent a generation rule of the warning information, and different index values correspond to different warning rules; and generate the warning information based on the target warning rule.

[0114] Optionally, the output unit 405 is further configured to display the warning information on the display screen of the vehicle according to a preset display rule, where the display rule is used to represent a display duration and a display manner of the warning information.

[0115] In the warning device of the vehicle described above, during the driving of the vehicle, the vehicle characteristics, the road characteristics, and the weather characteristics of the environment on the road where the vehicle is currently driving can be determined according to the environmental data of the surroundings of the vehicle, and then the driving performance index can be calculated according to the vehicle characteristics, the road characteristics, and the weather characteristics, so as to provide a comprehensive driving difficulty evaluation result for the driver and generate the warning information. By feeding back the driving difficulty in real time and generating the corresponding warning information, the enthusiasm and concentration of the driver can be improved during the automatic driving of the vehicle, so as to ensure the driving safety of the vehicle and avoid potential traffic accidents, thereby solving the technical problem of low driving safety of the vehicle in the related art.

[0116] Embodiments of the present application also provide a vehicle, comprising a memory storing an executable program, and a processor configured to run the program, wherein the program is configured to execute the method in the embodiments of the present application when running.

[0117] Embodiments of the present application also provide a computer-readable storage medium, which comprises a stored executable program, wherein the executable program is configured to control the device where the computer-readable storage medium is located to execute the method in the embodiments of the present application when running.

[0118] Embodiments of the present application also provide a computer program product, comprising a computer program, which is configured to implement the method in the embodiments of the present application when executed by a processor.

[0119] The embodiment of the present application further provides a computer program product, comprising a nonvolatile computer readable storage medium, the nonvolatile computer readable storage medium is used for storing a computer program, the computer program is executed by a processor to realize the method in each embodiment of the present application.

[0120] The embodiment of the present application further provides a computer program, the computer program is executed by a processor to realize the method in each embodiment of the present application.

[0121] In the above-described embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0122] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be implemented by other manners. Among them, the above-described device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0123] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0124] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0125] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk and various program code storage media.

[0126] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method of early warning of a vehicle, characterized by, The method comprises: collecting environmental data of a vehicle during driving, wherein the environmental data is used to represent an environmental condition of an environment in which the vehicle is currently located; extracting, from the environmental data, vehicle features on a road on which the vehicle is currently driving, road features of the road, and weather features of the environment, wherein the vehicle features are used to represent a vehicle density on the road, the road features are used to represent a road quality of the road, and the weather features are used to represent a weather condition of weather in the environment; determining a driving performance index of the vehicle based on the vehicle features, the road features, and the weather features, wherein the driving performance index is used to represent a driving difficulty level of the vehicle under the environmental condition; generating warning information based on the driving performance index, wherein the warning information is used to prompt a driving difficulty level of the vehicle currently driving; controlling the vehicle to output the warning information.

2. The method of claim 1, wherein, The extracting, from the environmental data, vehicle features on a road on which the vehicle is currently driving, road features of the road, and weather features of the environment comprises: preprocessing the environmental data to obtain preprocessed environmental data, wherein the preprocessing at least includes denoising processing and data enhancement processing; recognizing, by using a deep learning algorithm, a number of vehicles on the road on which the vehicle is currently driving, a road surface flatness of the road, and a weather type and an environmental temperature in the environment from the preprocessed environmental data; determining the vehicle features based on the number of vehicles, determining the road features based on the road surface flatness, and determining the weather features based on the weather type and the environmental temperature.

3. The method of claim 1, wherein, The determining a driving performance index of the vehicle based on the vehicle features, the road features, and the weather features comprises: determining a congestion degree score of the road on which the vehicle is currently driving based on the vehicle features, determining a road condition quality score of the road on which the vehicle is currently driving based on the road features, and determining a weather score of the environment in which the vehicle is currently located based on the weather features; determining the driving performance index of the vehicle based on the congestion degree score, the road condition quality score, and the weather score.

4. The method of claim 3, wherein, The determining a congestion degree score of the road on which the vehicle is currently driving based on the vehicle features, determining a road condition quality score of the road on which the vehicle is currently driving based on the road features, and determining a weather score of the environment in which the vehicle is currently located based on the weather features comprises: determining a traffic congestion degree on the road on which the vehicle is currently driving based on the vehicle features; and determining the congestion degree score based on the traffic congestion degree and a preset congestion degree score rule; determining a road surface flatness on the road on which the vehicle is currently driving based on the road features; and determining the road condition quality score based on the road surface flatness and a preset road condition quality score rule; determining the weather condition of the environment in which the vehicle is currently located based on the weather features; and determining the weather score based on the weather condition and a preset weather score rule.

5. The method of claim 3, wherein, determine a driving performance index of the vehicle based on the congestion degree score, the road condition quality score, and the weather score, including: determine a first weight coefficient corresponding to the congestion degree score, a second weight coefficient corresponding to the road condition quality score, and a third weight coefficient corresponding to the weather score; perform weighted calculation on the congestion degree score and the first weight coefficient, the road condition quality score and the second weight coefficient, and the weather score and the third weight coefficient to obtain a driving difficulty score of the vehicle; determine the driving performance index of the vehicle based on the driving difficulty score.

6. The method of claim 1, wherein, generate warning information based on the driving performance index, including: in response to an index value of the driving performance index being greater than a target index threshold value, determine a warning rule corresponding to the target index threshold value as a target warning rule corresponding to the driving performance index, wherein the target warning rule is used to represent a generation rule of the warning information, and different index values correspond to different warning rules; generate the warning information based on the target warning rule.

7. The method according to any one of claims 1 to 6, characterized in that, control the vehicle to output the warning information, including: display the warning information on a display screen of the vehicle according to a preset display rule, wherein the display rule is used to represent a display duration and a display manner of the warning information.

8. A warning device for a vehicle, characterized in that including: a collection unit configured to collect environmental data of a vehicle during driving, wherein the environmental data is used to represent an environmental condition of an environment in which the vehicle is currently located; an extraction unit configured to extract vehicle features on a road on which the vehicle is currently driving, road features of the road, and weather features of the environment from the environmental data, wherein the vehicle features are used to represent vehicle density on the road, the road features are used to represent road quality of the road, and the weather features are used to represent weather conditions of the environment; a determination unit configured to determine a driving performance index of the vehicle based on the vehicle features, the road features, and the weather features, wherein the driving performance index is used to represent driving difficulty of the vehicle under the environmental condition; a generation unit configured to generate warning information based on the driving performance index, wherein the warning information is used to prompt driving difficulty of the vehicle; an output unit configured to control the vehicle to output the warning information.

9. A vehicle characterized by comprising: including: a memory storing an executable program; a processor configured to run the program, wherein the program performs the method of any one of claims 1 to 7 when running.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein the executable program controls a device in which the storage medium is located to perform the method of any one of claims 1 to 7 when running.

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