Method for adaptive warning of moving obstacles during driving and vehicle

Abstract

The application provides a moving obstacle adaptive warning method in driving process and a vehicle. The method can determine the current precipitation intensity and precipitation type according to precipitation scene data; the cut-off frequency and the threshold value of filtering are determined according to the precipitation intensity and the precipitation type, wherein the cut-off frequency is negatively related to the single-particle precipitation diameter corresponding to the precipitation intensity and the precipitation type, the threshold value is negatively related to the single-particle precipitation diameter corresponding to the precipitation type, the threshold value is positively related to the precipitation intensity, and the echo data is filtered according to the cut-off frequency and the threshold value; whether there is a moving obstacle in front is identified according to the filtered echo data, the reliability of the distance between the moving obstacle and the vehicle is high if there is a moving obstacle, and the vehicle body warning module is controlled to perform a light warning operation under the condition that the distance between the moving obstacle and the vehicle is less than a set distance threshold, so that the reliability of the warning is high and the probability of false alarm is greatly reduced.

Description

Technical Field

[0001] This application relates to the field of safe driving technology, and in particular to an adaptive warning method and vehicle for moving obstacles during driving. Background Technology

[0002] With the rapid development of autonomous driving technology and advanced driver assistance systems (ADAS), vehicle environmental perception capabilities have become a core element in ensuring driving safety.

[0003] Current mainstream environmental perception solutions typically employ detection transceiver modules (such as millimeter-wave radar and ultrasonic transceiver modules) to accurately detect, track, and identify moving obstacles around vehicles, and provide warnings for these obstacles (such as vehicles and other obstacles) upon identification. However, detection transceiver modules face severe meteorological clutter interference during precipitation. Raindrops, snowflakes, and fog droplets, as numerous distributed scatterers, generate large areas of echo data. These echoes appear as continuous or semi-continuous clutter regions in space, causing inaccurate echo data collected by the detection transceiver module. This results in inaccurate identification and distance detection of moving obstacles, leading to false alarms for moving obstacles. Summary of the Invention

[0004] This application provides an adaptive alarm method and vehicle for moving obstacles during driving, which solves the problem in the prior art where the echo data collected by the vehicle's detection wave transceiver module is inaccurate, resulting in inaccurate identification and distance detection of moving obstacles, and thus false alarms for moving obstacles.

[0005] In a first aspect, this application provides an adaptive warning method for moving obstacles during driving, applied to the vehicle's onboard controller. The vehicle also includes a precipitation sensing module and a detection wave transceiver module. The method provided in this application includes: During vehicle operation, the system receives precipitation scene data sensed by the precipitation sensing module and echo data reflected after the detection wave transceiver module transmits the detection wave forward. Based on precipitation scene data, determine the current precipitation intensity and precipitation type; Based on the precipitation intensity and precipitation type, the cutoff frequency and threshold of the filter are determined. The cutoff frequency is negatively correlated with the precipitation intensity and positively correlated with the diameter of a single precipitation particle corresponding to the precipitation type. The threshold is negatively correlated with the diameter of a single precipitation particle corresponding to the precipitation type and positively correlated with the precipitation intensity. The echo data is filtered based on the cutoff frequency and threshold. Based on the filtered echo data, identify whether there is a moving obstacle ahead; if there is a moving obstacle, identify the distance between the moving obstacle and the vehicle. If the distance between a moving obstacle and the vehicle is less than a set distance threshold, the vehicle body warning module will execute a light warning operation.

[0006] In some implementations, the cutoff frequency and threshold value of the filter are determined based on the precipitation intensity and precipitation type, including: The first cutoff frequency of the filter is determined based on the precipitation intensity, and the second cutoff frequency of the filter is determined based on the precipitation type. The first threshold for filtering is determined based on precipitation intensity, and the second threshold for filtering is determined based on precipitation type. The first and second cutoff frequencies are weighted and averaged to obtain the filter cutoff frequency. The first threshold and the second threshold are weighted and averaged to obtain the filtering threshold.

[0007] In some implementations, the filter cutoff frequency is obtained by weighted averaging of the first cutoff frequency and the second cutoff frequency, including: According to the formula The cutoff frequency of the filter is obtained, where, This is the cutoff frequency for the filter. The first cutoff frequency, The second cutoff frequency, As the preset first weight, As the preset second weight, and ; The filtered threshold is obtained by weighting the first threshold and the second threshold, including: According to the formula The filtering threshold is obtained, where A is the filtering threshold. The first threshold value. The second threshold value. As the preset third weight, As the preset fourth weight, and .

[0008] In some implementations, the precipitation scene data consists of environmental images captured by a vehicle-mounted camera. Based on the precipitation scene data, the current precipitation intensity and type are determined, including: Environmental images are input into a pre-trained precipitation recognition model to determine the current precipitation intensity and precipitation type. The precipitation recognition model is trained by inputting multiple first training samples into the network to be trained. Each first training sample includes historical environmental images and labeled historical precipitation intensity and historical precipitation type.

[0009] In some implementations, the detection wave includes ultrasonic echo data and millimeter-wave echo data. Based on the filtered echo data, the presence of a moving obstacle ahead is identified. If a moving obstacle exists, the distance between the moving obstacle and the vehicle is determined, including: Based on the ultrasonic echo data, determine whether the echo intensity of the ultrasonic echo data is greater than the set signal strength threshold. If the signal strength exceeds the set threshold, the presence of a moving obstacle in front is identified based on the ultrasonic echo data. If a moving obstacle is detected ahead, the distance between the moving obstacle and the vehicle is determined based on the ultrasonic echo data. After determining that the echo intensity of the ultrasonic echo data has switched to be less than the set signal strength threshold, the distance between the moving obstacle and the vehicle is identified based on the millimeter wave echo data.

[0010] In some implementations, the vehicle also includes temperature and humidity sensors to identify the distance between the moving obstacle and the vehicle based on ultrasonic echo data, including: Receives ambient temperature data from a temperature sensor and ambient humidity data from a humidity sensor; According to the formula ,in, The actual propagation rate of the ultrasonic echo data. The preset base propagation rate, and For ambient temperature, For ambient humidity, The preset fifth weight, and , The preset sixth weight, and According to the formula The distance between the moving obstacle and the vehicle is determined, where S is the distance between the moving obstacle and the vehicle, and V is the actual propagation speed of the ultrasonic echo data. The time interval between emitting ultrasonic waves and receiving ultrasonic echo data.

[0011] In some implementations, controlling the vehicle warning module to perform a light warning operation includes: Obtain the vehicle's driving mode and find the driving hazard level associated with the driving mode; Determine the lighting warning strategy based on the level of driving hazard; According to the lighting warning strategy, the vehicle body warning module is controlled to perform lighting warning operations. The higher the driving hazard level, the more conspicuous the lighting warning operation.

[0012] In some implementations, controlling the vehicle warning module to perform a light warning operation includes: Based on the current precipitation intensity and type, find the driving hazard level associated with the current precipitation intensity and type; Determine the lighting warning strategy based on the level of driving hazard; According to the lighting warning strategy, the vehicle body warning module is controlled to perform lighting warning operations. The higher the driving hazard level, the more conspicuous the lighting warning operation.

[0013] Secondly, embodiments of this application also provide a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle performs the method provided in the first aspect of this application.

[0014] Thirdly, this application also provides a storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the first aspect of this application.

[0015] Fourthly, this application also provides a computer program product, including a computer program that, when run, causes a vehicle to perform the method provided in the first aspect of this application.

[0016] This application provides an adaptive warning method and vehicle for moving obstacles during driving. Based on precipitation scene data, the current precipitation intensity and type are determined. Based on the precipitation intensity and type, a filtering cutoff frequency and threshold are determined. The cutoff frequency is negatively correlated with both the precipitation intensity and the diameter of individual precipitation particles corresponding to the precipitation type. The threshold is negatively correlated with the diameter of individual precipitation particles corresponding to the precipitation type, and positively correlated with the precipitation intensity. This ensures high accuracy of the determined filtering cutoff frequency and threshold. The echo data is then filtered based on these cutoff frequencies and thresholds. Based on the filtered echo data, the presence of moving obstacles ahead is identified. If a moving obstacle is present, the reliability of determining the distance between the moving obstacle and the vehicle is high. If the distance between the moving obstacle and the vehicle is less than a set distance threshold, the vehicle's warning module is controlled to execute a light warning operation. This ensures high warning reliability and significantly reduces the probability of false alarms. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A circuit module connection block diagram of a vehicle provided in an embodiment of this application; Figure 2 A flowchart of an adaptive alarm method for moving obstacles during driving provided in an embodiment of this application. Detailed Implementation

[0019] Embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0020] The accompanying drawings illustrate various structural schematics according to embodiments of the present disclosure. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0021] In the context of this disclosure, when a layer / element is referred to as being "above" another layer / element, the layer / element may be directly above the other layer / element, or there may be an intermediate layer / element between them. Additionally, if a layer / element is "above" another layer / element in one orientation, then when the orientation is reversed, the layer / element may be "below" the other layer / element.

[0022] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0023] This application provides an adaptive alarm method for moving obstacles during driving, applied to the vehicle's onboard controller. Figure 1 As shown, the vehicle also includes a precipitation sensing module, a detection wave transceiver module, and a vehicle body warning module. The onboard controller is communicatively connected to the precipitation sensing module, the detection wave transceiver module, and the vehicle body warning module, respectively. Figure 2 As shown, the method provided in this application embodiment includes: S201: During vehicle operation, it receives precipitation scene data sensed by the precipitation sensing module and echo data reflected after the detection wave is emitted in front of the vehicle by the detection wave transceiver module.

[0024] For example, precipitation scene data may be, but is not limited to, environmental images captured by vehicle-mounted cameras; echo data may include ultrasonic echo data captured by ultrasonic transceiver modules and millimeter-wave echo data captured by millimeter-wave radar.

[0025] S202: Determine the current precipitation intensity and precipitation type based on precipitation scene data.

[0026] For example, environmental images can be input into a pre-trained precipitation recognition model to determine the current precipitation intensity and precipitation type. The precipitation recognition model is trained by inputting multiple first training samples into a network to be trained. Each first training sample includes historical environmental images and labeled historical precipitation intensity and historical precipitation type. For example, the network to be trained can be, but is not limited to, a convolutional neural network.

[0027] For example, precipitation types can be divided into rain, snow, and fog, and precipitation intensity can be divided into heavy, medium, and light precipitation. The diameter of a single snow particle is larger than that of a single rain particle, and the diameter of a single rain particle is larger than that of a single fog particle.

[0028] S203: Determine the cutoff frequency and threshold value of the filter based on the precipitation intensity and precipitation type.

[0029] Among them, the cutoff frequency is negatively correlated with precipitation intensity, and the cutoff frequency is positively correlated with the single-particle precipitation diameter corresponding to the precipitation type. The threshold is negatively correlated with the single-particle precipitation diameter corresponding to the precipitation type, and the threshold is positively correlated with precipitation intensity.

[0030] It should be noted that the cutoff frequency filters data based on spatial scale or motion velocity. In echo data processing, lowering the cutoff frequency means increasing the voxel grid size, retaining only large-scale targets such as cars, while filtering out tiny raindrops as small-scale details. The threshold defines "how strong a signal is considered a target." Echoes with intensity above the threshold are identified as targets, while those below the threshold are considered noise and discarded.

[0031] It should be noted that the stronger the precipitation, the larger the spatial scale and the wider the velocity spectrum occupied by clutter. Only by correspondingly lowering the cutoff frequency can the filter's "passband" avoid the frequency band occupied by clutter and achieve effective suppression. Therefore, the cutoff frequency is negatively correlated with precipitation intensity. The smaller the diameter of the single precipitation particle corresponding to the precipitation type, the more obvious the discrete small-scale echoes appear in the echo data. Therefore, it is necessary to lower the cutoff frequency (e.g., increase the voxel grid) to filter out the clutter from precipitation with small single precipitation particle diameters. Thus, the cutoff frequency is positively correlated with the single precipitation particle diameter corresponding to the precipitation type.

[0032] It should be noted that because fog droplets are extremely small and uniformly distributed, they cannot be effectively distinguished by scale or velocity. Therefore, a higher threshold is needed to suppress the uniformly raised background noise through amplitude dimension. Consequently, the threshold is negatively correlated with the diameter of the individual precipitation particles corresponding to the precipitation type. When precipitation intensity is low and precipitation is sparse, a moderate increase in the threshold can filter out the sparse precipitation. When precipitation intensity is high and precipitation is dense, a significantly higher threshold is needed to filter out the dense precipitation; therefore, the threshold is positively correlated with precipitation intensity.

[0033] Specifically, S203 can be implemented as follows: Step A1: Determine the first cutoff frequency of the filter based on the precipitation intensity, and determine the second cutoff frequency of the filter based on the precipitation type.

[0034] For example, the first cutoff frequency of the filter corresponding to the precipitation intensity and the second cutoff frequency of the filter corresponding to the precipitation type can be found from a preset first mapping table.

[0035] Step A2: Determine the first threshold for filtering based on the precipitation intensity, and determine the second threshold for filtering based on the precipitation type.

[0036] For example, a first threshold value for filtering corresponding to precipitation intensity can be found from a preset first mapping table, and a second threshold value for filtering corresponding to precipitation type can be found.

[0037] Step A3: Take a weighted average of the first cutoff frequency and the second cutoff frequency to obtain the filter cutoff frequency.

[0038] For example, it can be based on the formula The cutoff frequency of the filter is obtained, where, This is the cutoff frequency for the filter. The first cutoff frequency, The second cutoff frequency, As the preset first weight, As the preset second weight, and .For example, .

[0039] Step 4: Take a weighted average of the first threshold and the second threshold to obtain the filtering threshold.

[0040] Specifically, according to the formula The filtering threshold is obtained, where A is the filtering threshold. The first threshold value. The second threshold value. As the preset third weight, As the preset fourth weight, and ,like .

[0041] S204: Filter the echo data based on the cutoff frequency and threshold.

[0042] S205: Based on the filtered echo data, identify whether there is a moving obstacle ahead. If there is a moving obstacle, identify the distance between the moving obstacle and the vehicle.

[0043] S205 can be specifically implemented as follows: Based on the ultrasonic echo data, determine whether the echo intensity of the ultrasonic echo data is greater than a set signal strength threshold; if it is greater than the set signal strength threshold, identify whether there is a moving obstacle in front based on the ultrasonic echo data; if a moving obstacle is identified in front, identify the distance between the moving obstacle and the vehicle based on the ultrasonic echo data (such as receiving the ambient temperature from the temperature sensor and the ambient humidity from the humidity sensor); after determining that the echo intensity of the ultrasonic echo data switches to less than the set signal strength threshold, identify the distance between the moving obstacle and the vehicle based on the millimeter wave echo data.

[0044] For example, the method of identifying the distance between a moving obstacle and a vehicle based on ultrasonic echo data can specifically be based on a formula. To determine the actual propagation rate of the ultrasonic echo data. The actual propagation rate of the ultrasonic echo data. The preset base propagation rate, and like For ambient temperature, For ambient humidity, The preset fifth weight, and , The preset sixth weight, and , =0.0124. Therefore, according to the formula... The distance between the moving obstacle and the vehicle is determined, where S is the distance between the moving obstacle and the vehicle, and V is the actual propagation speed of the ultrasonic echo data. This refers to the time interval between emitting the ultrasonic wave and receiving the ultrasonic echo data. This filters out the effects of temperature and humidity on the speed of sound, resulting in high accuracy in determining the distance between moving obstacles and vehicles.

[0045] S206: When the distance between a moving obstacle and the vehicle is less than a set distance threshold, control the vehicle body warning module to perform a light warning operation.

[0046] It should be noted that the specific implementation of S206 can include, but is not limited to, the following two methods: The first type: Step B1: Obtain the vehicle's driving mode and find the driving hazard level associated with the driving mode.

[0047] Step B2: Determine the lighting warning strategy based on the driving hazard level.

[0048] For example, the correspondence between a vehicle's driving mode, driving hazard level, and corresponding lighting warning strategy can be shown in Table 1 below.

[0049] Table 1

[0050] Step B3: According to the lighting warning strategy, control the vehicle warning module to perform lighting warning operations. The higher the driving hazard level, the more conspicuous the lighting warning operation.

[0051] The second type: Step C1: Based on the current precipitation intensity and precipitation type, find the driving hazard level associated with the current precipitation intensity and precipitation type.

[0052] Step C2: Determine the lighting warning strategy based on the driving hazard level.

[0053] The correspondence between precipitation intensity and type, driving hazard level, and corresponding lighting warning strategies for vehicles can be shown in Table 2 below.

[0054] Table 2

[0055] Step C3: According to the lighting warning strategy, control the vehicle warning module to perform lighting warning operations. The higher the driving hazard level, the more conspicuous the lighting warning operation.

[0056] It should be noted that the vehicle warning module can be, but is not limited to, a vehicle light strip set around the vehicle body.

[0057] In summary, this application provides an adaptive warning method for moving obstacles during driving. Based on precipitation scene data, the current precipitation intensity and type are determined. Based on the precipitation intensity and type, a filtering cutoff frequency and threshold are determined. The cutoff frequency is negatively correlated with both precipitation intensity and the diameter of individual precipitation particles corresponding to each precipitation type. The threshold is negatively correlated with the diameter of individual precipitation particles corresponding to each precipitation type, and positively correlated with precipitation intensity. This ensures high accuracy of the determined filtering cutoff frequency and threshold. The echo data is then filtered based on these cutoff frequencies and thresholds. Based on the filtered echo data, the presence of a moving obstacle ahead is identified. If a moving obstacle is present, the reliability of determining the distance between the moving obstacle and the vehicle is high. If the distance between the moving obstacle and the vehicle is less than a set distance threshold, the vehicle warning module is controlled to execute a light warning operation. This results in high warning reliability and significantly reduces the probability of false alarms.

[0058] In addition, this application also provides a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it causes the vehicle to perform the method provided in the above embodiments of this application.

[0059] In addition, this application embodiment also provides a storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the above embodiments of this application.

[0060] Fourthly, this application also provides a computer program product, including a computer program that, when run, causes a vehicle to perform the methods provided in the above embodiments of this application.

[0061] The above description does not provide detailed technical specifications regarding the structure of each layer. However, those skilled in the art should understand that layers and regions of desired shapes can be formed using various technical means. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be advantageously combined.

[0062] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0063] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An adaptive warning method for moving obstacles during vehicle operation, characterized in that, An onboard controller for a vehicle, the vehicle further including a precipitation sensing module and a detection wave transceiver module, the method comprising: During the vehicle's operation, the system receives precipitation scene data sensed by the precipitation sensing module and echo data reflected after the detection wave transceiver module emits a detection wave forward towards the vehicle. Based on the precipitation scene data, determine the current precipitation intensity and precipitation type; Based on the precipitation intensity and precipitation type, the cutoff frequency and threshold of the filter are determined, wherein the cutoff frequency is negatively correlated with the precipitation intensity and positively correlated with the single-particle precipitation diameter corresponding to the precipitation type, the threshold is negatively correlated with the single-particle precipitation diameter corresponding to the precipitation type and positively correlated with the precipitation intensity; The echo data is filtered according to the cutoff frequency and the threshold value. Based on the filtered echo data, identify whether there is a moving obstacle ahead; if the moving obstacle exists, identify the distance between the moving obstacle and the vehicle. If the distance between the moving obstacle and the vehicle is less than a set distance threshold, the vehicle body warning module is controlled to perform a light warning operation.

2. The method according to claim 1, characterized in that, The step of determining the cutoff frequency and threshold value for filtering based on the precipitation intensity and precipitation type includes: The first cutoff frequency of the filter is determined based on the precipitation intensity, and the second cutoff frequency of the filter is determined based on the precipitation type. A first threshold for filtering is determined based on the precipitation intensity, and a second threshold for filtering is determined based on the precipitation type. The first cutoff frequency and the second cutoff frequency are weighted and averaged to obtain the filter cutoff frequency; The first threshold and the second threshold are weighted and averaged to obtain the filtering threshold.

3. The method according to claim 2, characterized in that, The step of obtaining the filtered cutoff frequency by weighted averaging of the first cutoff frequency and the second cutoff frequency includes: According to the formula The cutoff frequency of the filter is obtained, where, The cutoff frequency of the filter is [value]. The first cutoff frequency, This is the second cutoff frequency. As the preset first weight, As the preset second weight, and ; The step of weighting the first threshold and the second threshold to obtain the filtering threshold includes: According to the formula The filtering threshold is obtained, where A is the filtering threshold. The first threshold value. The second threshold value. As the preset third weight, The fourth weight is preset, and .

4. The method according to claim 1, characterized in that, The precipitation scene data is environmental images captured by a vehicle-mounted camera. Determining the current precipitation intensity and type based on the precipitation scene data includes: The environmental image is input into a pre-trained precipitation recognition model to determine the current precipitation intensity and precipitation type. The precipitation recognition model is trained by inputting multiple first training samples into the network to be trained. Each first training sample includes a historical environmental image and labeled historical precipitation intensity and historical precipitation type.

5. The method according to claim 1, characterized in that, The detection wave includes ultrasonic echo data and millimeter-wave echo data. Based on the filtered echo data, the system identifies whether a moving obstacle exists ahead. If the moving obstacle exists, it identifies the distance between the moving obstacle and the vehicle, including: Based on the ultrasonic echo data, determine whether the echo intensity of the ultrasonic echo data is greater than the set signal strength threshold. If the signal strength exceeds the set threshold, the presence of a moving obstacle ahead is identified based on the ultrasonic echo data. If a moving obstacle is detected ahead, the distance between the moving obstacle and the vehicle is determined based on the ultrasonic echo data. After determining that the echo intensity of the ultrasonic echo data has switched to be less than a set signal strength threshold, the distance between the moving obstacle and the vehicle is identified based on the millimeter-wave echo data.

6. The method according to claim 5, characterized in that, The vehicle also includes a temperature sensor and a humidity sensor. The step of identifying the distance between the moving obstacle and the vehicle based on the ultrasonic echo data includes: Receives ambient temperature collected by the temperature sensor and ambient humidity collected by the humidity sensor; According to the formula To determine the actual propagation rate of the ultrasonic echo data, where, The actual propagation rate of the ultrasonic echo data. The preset base propagation rate, and The ambient temperature is... The ambient humidity is... The preset fifth weight, and , The preset sixth weight, and ; According to the formula The distance between the moving obstacle and the vehicle is determined, where S is the distance between the moving obstacle and the vehicle, and V is the actual propagation rate of the ultrasonic echo data. The time interval between emitting the ultrasonic wave and receiving the ultrasonic echo data.

7. The method according to any one of claims 1-6, characterized in that, The vehicle body warning module performs light warning operations, including: Obtain the vehicle's driving mode and find the driving hazard level associated with the driving mode; Determine the lighting warning strategy based on the stated driving hazard level; According to the aforementioned light warning strategy, the vehicle body warning module is controlled to perform light warning operations, wherein the higher the driving hazard level, the more conspicuous the light warning operation.

8. The method according to any one of claims 1-6, characterized in that, The vehicle body warning module performs light warning operations, including: Based on the current precipitation intensity and precipitation type, find the driving hazard level associated with the current precipitation intensity and precipitation type; Determine the lighting warning strategy based on the stated driving hazard level; According to the aforementioned light warning strategy, the vehicle body warning module is controlled to perform light warning operations, wherein the higher the driving hazard level, the more conspicuous the light warning operation.

9. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the vehicle to perform the method as described in any one of claims 1 to 8.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the computer to perform the method as described in any one of claims 1 to 8.

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