Vehicle control method and system for rainy and snowy weather and vehicle terminal

Abstract

The invention relates to the technical field of vehicle control, and discloses a vehicle control method and system for rainy and snowy weather and a vehicle terminal. According to the method, the weather related parameters corresponding to the target vehicle are obtained, and if the weather related parameters meet the preset first-level judgment condition, snowfield identification is performed on the road surface on which the target vehicle runs to obtain the wet and slippery road surface area in the road surface, so that when the target vehicle runs in the wet and slippery road surface area, snowfield identification is performed on the target vehicle. If yes, the target vehicle is controlled to start an automatic braking function, if the weather related parameters meet the rain and snow weather conditions, it is judged that accumulated snow possibly appears on the road surface, then snowfield recognition is conducted on the road surface where the target vehicle runs, and a wet and slippery road surface area is obtained; and the target vehicle is controlled to be braked according to the collision risk level, automatic emergency braking of the vehicle in the wet and slippery road area is achieved, obstacle avoidance support is provided for the vehicle in the wet and slippery road area, and therefore the vehicle running safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a vehicle control method, system and vehicle terminal for rainy and snowy weather. Background Art

[0002] In winter, especially when encountering rainy and snowy weather, the common presence of snow and ice on the road often greatly affects the braking performance of the vehicle, resulting in a significant increase in braking distance, a significant decrease in the vehicle's handling stability, and an increased risk of traffic accidents. Therefore, safety driving systems such as anti-lock braking systems (ABS) and electronic stability control systems (ESC) play a vital role in rainy and snowy weather. Through built-in sensors and advanced control algorithms, they can monitor the dynamic state of the vehicle in real time, and quickly adjust the brake pressure and engine output when detecting wheel slippage or the vehicle is about to lose control, thereby effectively avoiding vehicle slippage and loss of control, and maintaining the stability of the vehicle and the control of the driving direction.

[0003] However, safety driving systems such as anti-lock braking systems and electronic stability control systems mainly rely on the vehicle's own sensor data and control strategies to identify current road conditions. They cannot perceive the environmental information in which the vehicle is traveling in advance, and thus cannot provide support for the vehicle to avoid obstacles, resulting in lower safety of the vehicle in rainy and snowy weather. Summary of the invention

[0004] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0005] In view of the shortcomings of the prior art described above, the present application provides a vehicle control method, system and vehicle terminal for rainy and snowy weather, so as to provide obstacle avoidance support for the vehicle in rainy and snowy weather, thereby improving vehicle safety.

[0006] The present application provides a vehicle control method for rainy and snowy weather, comprising: obtaining weather-related parameters corresponding to the target vehicle, wherein the weather-related parameters include driving environment parameters and / or tire adhesion parameters; if the weather-related parameters meet a preset first-level determination condition, then performing target recognition on the road surface on which the target vehicle is traveling to obtain a road surface recognition result, wherein the first-level determination condition is determined by performing vehicle testing in different test environments, and the test environment includes a rainy and snowy weather environment; if the road surface recognition result meets a preset second-level determination condition, then predicting the collision risk of the target vehicle to obtain a collision risk level, so as to control the target vehicle to brake according to the collision risk level, wherein the second-level determination condition includes that the target vehicle is in a slippery road area.

[0007] In one embodiment of the present application, if the weather-related parameters meet the preset first-level judgment conditions, the method also includes: obtaining vehicle-road collaborative data and network map data; generating road condition information of the target vehicle based on the vehicle-road collaborative data and the network map data; and broadcasting the road condition information to users in the target vehicle.

[0008] In one embodiment of the present application, whether the road surface recognition result satisfies the secondary determination condition is determined in the following manner: data is collected by multiple vehicle sensors arranged on the target vehicle to obtain sensor parameters collected by each of the vehicle sensors; path planning is performed according to each of the sensor parameters to obtain the vehicle driving path of the target vehicle; if the road surface recognition result includes identifying a slippery road area, the target vehicle and the slippery road area are mapped to the vehicle driving path, and the target vehicle driving is simulated according to the vehicle driving path to determine whether the target vehicle is driving in the slippery road area according to the simulation result.

[0009] In one embodiment of the present application, path planning is performed according to each of the sensor parameters to obtain a vehicle driving path of the target vehicle, including: classifying each of the sensor parameters according to different parameter types, and fusing sensor parameters of the same parameter type to obtain fused data, wherein the fused data includes global data and real-time data, the global data includes map data and / or road condition data, and the implementation data includes image sensor data and / or radar sensor data; if the global data includes map data, a starting position and a target position are set in the map data, so as to perform path planning between the starting position and the target position according to the global data to obtain a vehicle driving path; environmental perception is performed on the environment in which the target vehicle is located according to the real-time data to obtain environmental perception data, so as to adjust the vehicle driving path according to the environmental perception data to avoid the target vehicle from colliding with the target obstacle, wherein the environmental perception data includes the obstacle position and / or obstacle motion trajectory corresponding to the target obstacle.

[0010] In one embodiment of the present application, the collision risk of the target vehicle is predicted to obtain a collision risk level, and the target vehicle is controlled to brake according to the collision risk level, including: obtaining a deceleration matching table, the deceleration matching table storing a plurality of original parameters, a reference deceleration corresponding to each of the original parameters, and an advanced deceleration corresponding to each of the original parameters, wherein the reference deceleration corresponding to the original parameter is less than the advanced deceleration corresponding to the original parameter; in response to identifying a target obstacle corresponding to the target vehicle, obtaining a collision risk parameter, wherein the collision risk parameter includes a relative distance between the target vehicle and the target obstacle, a relative speed between the target vehicle and the target obstacle, and a collision time between the target vehicle and the target obstacle. time, at least one of the tire friction coefficient of the target vehicle and the tire slip rate of the target vehicle; determining a collision risk level according to the collision risk parameter; if the collision risk level is greater than a preset level threshold and the target vehicle is in a normal area, determining a target deceleration from the reference deceleration according to a matching result between the collision risk parameter and the original parameter, and controlling the target vehicle to brake according to the target deceleration, wherein the normal area is different from the slippery road area; if the collision risk level is greater than a preset level threshold and the target vehicle is in a slippery road area, determining a target deceleration from the advanced deceleration according to a matching result between the collision risk parameter and the original parameter, and controlling the target vehicle to brake according to the target deceleration.

[0011] In one embodiment of the present application, a target deceleration is determined from the advanced decelerations according to a matching result between the collision risk parameter and the original parameter, including at least one of the following: determining an intermediate deceleration corresponding to each collision risk parameter from each advanced deceleration according to a matching result between the collision risk parameter and the original parameter, and taking the maximum intermediate deceleration as the target deceleration according to a comparison result between each intermediate deceleration; obtaining a parameter priority corresponding to each collision risk parameter, and taking a collision risk parameter of the first rank as a current parameter according to the parameter priority; obtaining a baseline parameter interval corresponding to the current parameter in response to the current parameter; if the current parameter is within the baseline parameter interval, determining a target parameter from each advanced deceleration according to a matching result between the current parameter and the original parameter; if the current parameter is outside the baseline parameter interval, taking a collision risk parameter of the next rank after the current parameter as a new current parameter according to the parameter priority.

[0012] In one embodiment of the present application, if the road surface recognition result meets a preset secondary judgment condition, the method further includes at least one of the following: generating road surface prompt information, wherein the road surface prompt information is used to prompt a user in the target vehicle that the target vehicle is in a slippery road area; if the relative distance between the target vehicle and the target obstacle is less than a preset warning distance, generating risk warning information, wherein the risk warning information is used to prompt a user in the target vehicle that the target vehicle is at risk of collision.

[0013] In one embodiment of the present application, the method also includes: iteratively training the environmental algorithm model according to preset training sample data to optimize the model parameters of the environmental algorithm model, wherein the environmental algorithm model includes a target recognition model and / or an environmental perception model, the target recognition model is used to identify the target of the road surface on which the target vehicle is traveling, and the environmental perception model is used to identify the target obstacles corresponding to the target vehicle; optimizing the parameters of the environmental algorithm model according to user feedback data corresponding to the environmental algorithm model; iteratively updating the environmental algorithm model according to the model accuracy corresponding to the environmental algorithm model until the model accuracy reaches a preset accuracy; obtaining environmental sample data corresponding to different environmental requirements, and iteratively training the environmental algorithm model according to each of the environmental sample data to optimize the model parameters of the environmental algorithm model, wherein the environmental requirements include one or more of lighting conditions, environmental conditions, and temperature conditions.

[0014] The present application provides a vehicle control system for rainy and snowy weather, including: an acquisition module, used to acquire weather-related parameters corresponding to the target vehicle, wherein the weather-related parameters include driving environment parameters and / or tire adhesion parameters; a primary determination module, used to perform target recognition on the road surface on which the target vehicle is traveling, and obtain a road surface recognition result if the weather-related parameters meet a preset primary determination condition, wherein the primary determination condition is determined by performing vehicle tests in different test environments, and the test environment includes a rainy and snowy weather environment; a secondary determination module, used to predict the collision risk of the target vehicle, and obtain a collision risk level, if the road surface recognition result meets a preset secondary determination condition, so as to control the target vehicle to brake according to the collision risk level, wherein the secondary determination condition includes that the target vehicle is in a slippery road area.

[0015] The present application provides a vehicle terminal, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the vehicle terminal executes the above method.

[0016] Beneficial effects of this application:

[0017] By acquiring the weather-related parameters corresponding to the target vehicle, and if the weather-related parameters meet the preset first-level judgment conditions, the road surface on which the target vehicle is traveling is identified for snow, and the slippery road surface area in the road surface is obtained, so that when the target vehicle is traveling in the slippery road surface area, the target vehicle is controlled to start the automatic braking function. In this way, if the weather-related parameters meet the rainy and snowy weather conditions, it is judged that snow may accumulate on the road surface, so the snow is identified for the road surface on which the target vehicle is traveling, and the slippery road surface area is obtained. When the vehicle is in the slippery road surface area, the target vehicle is controlled to brake according to the collision risk level, realizing the automatic emergency braking of the vehicle in the slippery road surface area, providing obstacle avoidance support for the vehicle in the slippery road surface area, thereby improving the safety of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a vehicle control method for rainy and snowy weather in an embodiment of the present application;

[0019] Figure 2 is a schematic structural diagram of a target vehicle provided with a vehicle sensor in an embodiment of the present application;

[0020] Figure 3 is a flow chart of another vehicle control method for rainy and snowy weather in an embodiment of the present application;

[0021] Figure 4is a flow chart of another vehicle control method for rainy and snowy weather in an embodiment of the present application;

[0022] Figure 5 It is a structural diagram of a system architecture for implementing a vehicle control method for rainy and snowy weather in an embodiment of the present application;

[0023] Figure 6 It is a structural schematic diagram of a vehicle control system for rainy and snowy weather in an embodiment of the present application;

[0024] Figure 7 It is a structural diagram of a vehicle terminal in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and sub-samples in the embodiments can be combined with each other without conflict.

[0026] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0027] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0028] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0029] Unless otherwise stated, the term "plurality" means two or more.

[0030] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0031] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0032] Combination Figure 1 As shown, the embodiment of the present disclosure provides a vehicle control method for rainy and snowy weather, including:

[0033] Step S101, obtaining weather-related parameters corresponding to the target vehicle;

[0034] Wherein, the weather-related parameters include driving environment parameters and / or tire adhesion parameters;

[0035] Step S102, if the weather-related parameters meet the preset first-level determination conditions, target recognition is performed on the road surface on which the target vehicle is traveling to obtain a road surface recognition result;

[0036] Among them, the first-level judgment conditions are determined by conducting vehicle tests in different test environments, including rainy and snowy weather environments;

[0037] Step S103, if the road surface recognition result meets the preset secondary determination condition, the collision risk of the target vehicle is predicted to obtain a collision risk level, so as to control the target vehicle to brake according to the collision risk level;

[0038] Among them, the secondary judgment condition includes that the target vehicle is in a slippery road area.

[0039] The vehicle control method for rainy and snowy weather provided by the embodiment of the present disclosure is adopted, by obtaining the weather-related parameters corresponding to the target vehicle, and if the weather-related parameters meet the preset first-level judgment conditions, the road surface on which the target vehicle is traveling is identified for snow, and the slippery road surface area in the road surface is obtained, so that when the target vehicle is traveling in the slippery road surface area, the target vehicle is controlled to start the automatic braking function. In this way, if the weather-related parameters meet the rainy and snowy weather conditions, it is judged that snow may accumulate on the road surface, so that the snow on the road surface on which the target vehicle is traveling is identified, and the slippery road surface area is obtained, and when the vehicle is in the slippery road surface area, the target vehicle is controlled to brake according to the collision risk level, realizing the automatic emergency braking of the vehicle in the slippery road surface area, providing obstacle avoidance support for the vehicle in the slippery road surface area, thereby improving the safety of vehicle driving.

[0040] Optionally, data collection is performed by using a plurality of vehicle sensors disposed on the target vehicle to obtain sensor parameters respectively collected by each vehicle sensor.

[0041] In some embodiments, the target vehicle is provided with a variety of vehicle sensors, including: an image sensor for capturing images around the target vehicle; a radar sensor for capturing point cloud data around the target vehicle; a temperature sensor for capturing the ambient temperature of the target vehicle; a map system for capturing map data from a server; a tire speed sensor for capturing the vehicle speed of the target vehicle; and a vehicle-road collaboration (V2X, Vehicle to Everything) system for capturing environmental information around the vehicle.

[0042] Combination Figure 2 As shown, the front, top, bottom and rear of the target vehicle are all provided with temperature sensors and image sensors, and the four wheels of the target vehicle are all provided with image sensors and tire speed sensors.

[0043] In some embodiments, the lens of the image sensor has an automatic heating function for defrosting and defogging.

[0044] In some embodiments, the driving environment parameters include at least one of ambient temperature, ambient light intensity, and weather parameters.

[0045] In some embodiments, the primary determination condition corresponding to the ambient temperature includes that the ambient temperature is less than or equal to a preset temperature threshold, wherein the preset temperature threshold includes 0-5°C (degrees Celsius).

[0046] In some embodiments, the first-level determination condition corresponding to the weather parameter includes the weather parameter being rainy, snowy or hail weather.

[0047] In some embodiments, the tire adhesion parameters include tire friction coefficient and / or tire slip ratio.

[0048] In some embodiments, the tire friction coefficient is calculated by using the current vehicle speed and the braking distance at the current vehicle speed.

[0049] In some embodiments, the tire friction coefficient is calculated by formula (1):

[0050]

[0051] In formula (1), μ is the tire friction coefficient, v is the current vehicle speed, S1 is the braking distance at the current vehicle speed, and g is the gravitational acceleration, which is generally 9.81 m / s 2 .

[0052] In some embodiments, the tire slip ratio is calculated by the vehicle body moving speed and the wheel moving speed.

[0053] In some embodiments, the tire slip ratio is calculated by formula (2):

[0054]

[0055] Optionally, if the weather-related parameters meet the preset first-level judgment conditions, the method also includes: acquiring vehicle-road collaborative data and network map data; generating road condition information of the target vehicle based on the vehicle-road collaborative data and network map data; and broadcasting the road condition information to users in the target vehicle.

[0056] In some embodiments, through map data and / or vehicle-road cooperative data, broadcast communication information, red light information, surrounding vehicle information, and road information on the vehicle's driving path can be obtained, so as to predict road and traffic conditions in advance and take preventive measures.

[0057] In some embodiments, vehicle-road collaborative data covers multiple communication modes such as vehicle to vehicle (V2V), vehicle to road (V2I), vehicle to pedestrian (V2P), vehicle to cloud (V2C), vehicle to network (V2N), and vehicle to traffic facility (V2T). The core is to use technologies such as wireless communication and the Internet to enable vehicles to exchange and coordinate information with the surrounding environment, thereby improving the safety, efficiency and comfort of road traffic.

[0058] In some embodiments, through vehicle-road collaborative data, vehicles can obtain information such as the location, speed, and driving intention of surrounding vehicles in real time, thereby avoiding collisions and congestion; through vehicle-road collaborative data, vehicles can receive traffic information such as traffic lights and road construction, and make driving adjustments in advance; through vehicle-road collaborative data, vehicles can remind pedestrians to pay attention to traffic safety and reduce the risk of traffic accidents.

[0059] Optionally, whether the road surface recognition result satisfies the secondary determination condition is determined in the following manner: path planning is performed according to the parameters of each sensor to obtain the vehicle driving path of the target vehicle; if the road surface recognition result includes recognition of a slippery road surface area, the target vehicle and the slippery road surface area are mapped to the vehicle driving path, and the target vehicle driving is simulated according to the vehicle driving path to determine whether the target vehicle is driving in the slippery road surface area according to the simulation result.

[0060] In some embodiments, the slippery road surface area includes at least one of a snow accumulation area, a water accumulation area, and a wet area.

[0061] In some embodiments, target recognition is performed on the road surface on which the target vehicle is traveling to obtain a road surface recognition result, including: acquiring an image of the road surface on which the target vehicle is traveling by an image sensor disposed on the target vehicle to obtain a road surface image, and performing target recognition on the road surface image by a preset image recognition model to obtain a slippery road surface area in the road surface.

[0062] In some embodiments, target recognition is performed on the road surface on which the target vehicle is traveling to obtain a road surface recognition result, including: acquiring an image of the road surface on which the target vehicle is traveling by an image sensor disposed on the target vehicle to obtain a road surface image, and obtaining a preset reference image corresponding to the road surface, so as to determine a slippery road surface area in the road surface based on a comparison result between the road surface image and the preset reference image.

[0063] In some embodiments, snow is identified on the road surface on which the target vehicle is traveling to obtain a slippery road surface area, including: receiving snow accumulation areas and water accumulation areas within a preset map range through a map system provided in the target vehicle, and determining the slippery road surface area based on the intersection between the traveling vehicle path and the snowy road section, wherein the preset map range is determined by taking the vehicle position of the target vehicle as the range center.

[0064] Optionally, path planning is performed according to each sensor parameter to obtain a vehicle driving path of the target vehicle, including: classifying each sensor parameter according to different parameter types, and fusing sensor parameters of the same parameter type to obtain fused data, wherein the fused data includes global data and real-time data, the global data includes map data and / or road condition data, and the implementation data includes image sensor data and / or radar sensor data; if the global data includes map data, a starting position and a target position are set in the map data, so as to perform path planning between the starting position and the target position according to the global data to obtain a vehicle driving path; performing environmental perception of the target vehicle's environment according to the real-time data to obtain environmental perception data, so as to adjust the vehicle driving path according to the environmental perception data to avoid the target vehicle from colliding with a target obstacle, wherein the environmental perception data includes an obstacle position and / or an obstacle motion trajectory corresponding to the target obstacle.

[0065] In some embodiments, data from different vehicle sensors are fused according to different parameter types to provide an environmental view of the vehicle's environment, wherein the vehicle sensors include image sensors, radar sensors, measurement units, global positioning systems, T-BOX, etc.

[0066] In some embodiments, environmental perception is performed based on the fused data, that is, target objects in the environmental view are identified and understood, where the target objects include road boundaries, traffic signs, other vehicles, pedestrians, obstacles, etc.; this process mainly uses image processing and machine learning technology, which plays an important role in environmental perception by identifying patterns in images and classifying objects.

[0067] In some embodiments, path planning includes two aspects: first, before or during vehicle travel, the best path from the starting point to the end point is planned based on the destination and road condition data. This is usually a relatively long-term and macro planning process, in which the road condition data covers traffic congestion information, etc.; second, during vehicle operation, the global path is fine-tuned or re-planned based on the real-time environmental perception data, and the algorithm is used to quickly respond and make decisions to ensure the safety and smooth operation of the vehicle.

[0068] In some embodiments, a preset simulation algorithm is used to simulate the driving of a target vehicle according to the driving path of the vehicle, wherein the simulation algorithm is composed of a main function, a sensor data collection function, a path planning function, an image processing function, a risk assessment function, a decision function, etc.; the main function is the entry point of the simulation algorithm, which calls the above functions in sequence, simulates the entire workflow of the AEB (Autonomous Emergency Braking) system, and prints out the final decision result; the sensor data collection function is used to simulate the process of collecting data from vehicle sensors. In actual applications, these data will come from the vehicle's hardware interface; the path planning function is used to receive radar data and image paths from the sensor data collection function and pre-process them; the image processing function simulates the image processing process, which usually involves complex algorithms to detect road types and obstacles in the image; the risk assessment function receives radar data and obstacle information and assesses the collision risk; the decision function receives the collision risk level and makes a decision based on the level, wherein if the risk level is "high", it is decided to start AEB, otherwise, no action is taken.

[0069] Optionally, the collision risk of the target vehicle is predicted to obtain a collision risk level, so as to control the target vehicle to brake according to the collision risk level, including: obtaining a deceleration matching table, the deceleration matching table storing a plurality of original parameters, a reference deceleration corresponding to each original parameter, and an advanced deceleration corresponding to each original parameter, wherein the reference deceleration corresponding to the original parameter is less than the advanced deceleration corresponding to the original parameter; in response to identifying a target obstacle corresponding to the target vehicle, obtaining a collision risk parameter, wherein the collision risk parameter includes a relative distance between the target vehicle and the target obstacle, a relative speed between the target vehicle and the target obstacle, a collision time between the target vehicle and the target obstacle, At least one of a tire friction coefficient of the target vehicle and a tire slip rate of the target vehicle; determining a collision risk level according to a collision risk parameter; if the collision risk level is greater than a preset level threshold and the target vehicle is in a normal area, determining a target deceleration from a baseline deceleration according to a matching result between the collision risk parameter and the original parameter, and controlling the target vehicle to brake according to the target deceleration, wherein the normal area is different from a slippery road area; if the collision risk level is greater than a preset level threshold and the target vehicle is in a slippery road area, determining a target deceleration from an advanced deceleration according to a matching result between the collision risk parameter and the original parameter, and controlling the target vehicle to brake according to the target deceleration.

[0070] Combination Figure 3 As shown, the embodiment of the present disclosure provides a vehicle control method for rainy and snowy weather, including:

[0071] Step S301, identifying that the target vehicle is in a slippery road area, jumping to step S302, step S303 and step S304;

[0072] Step S302, displaying an automatic braking function on mark in the vehicle;

[0073] Step S303, playing a preset prompt tone in the car;

[0074] Step S304, obtaining collision risk parameters corresponding to the target vehicle;

[0075] Wherein, the collision risk parameter includes at least a part of the relative distance, the collision time, the tire friction coefficient and the tire slip rate;

[0076] Step S305, determining a target deceleration from a preset deceleration matching table according to a matching result between the collision risk parameter and the original parameter;

[0077] The deceleration matching table stores a plurality of original parameters and the advanced decelerations corresponding to the original parameters;

[0078] Step S306, controlling the target vehicle to brake according to the target deceleration.

[0079] In some embodiments, the collision time is calculated by the relative speed and relative distance between the target vehicle and the target obstacle.

[0080] In some embodiments, the collision time is calculated by formula (3):

[0081]

[0082] In formula (3), TTC is the collision time, L is the relative distance between the target vehicle and the target obstacle, V rel is the relative speed between the target vehicle and the target obstacle.

[0083] In some embodiments, since the vehicle may slip and the braking distance may be too long in slippery road areas, more redundant distance must be reserved to prevent vehicle collision compared to automatic braking methods used on ordinary roads. Therefore, the deceleration used for braking in slippery road areas must be greater than the baseline deceleration.

[0084] In some embodiments, by adjusting the parameter value of the original parameter so that the same deceleration corresponds to different original parameters, the effect of reserving a redundant distance can also be achieved.

[0085] In some embodiments, the corresponding relationship between the target deceleration and the braking distance is expressed by formula (4):

[0086]

[0087] In formula (4), a is the target deceleration, v is the current vehicle speed, and S1 is the braking distance.

[0088] In some embodiments, if the relative distance between the target vehicle and the target obstacle is less than a preset braking distance, the target vehicle is controlled to brake, wherein the preset braking distance is a distance at which the collision time TTC (Time To Collision) of the target vehicle is equal to 6 seconds.

[0089] Optionally, the target deceleration is determined from the advanced decelerations according to the matching result between the collision risk parameters and the original parameters, including: determining the intermediate decelerations corresponding to each collision risk parameter from each advanced deceleration according to the matching result between the collision risk parameters and the original parameters, and taking the maximum intermediate deceleration as the target deceleration according to the comparison result between the intermediate decelerations.

[0090] In some embodiments, when the tire friction coefficient is less than 0.15, the advanced deceleration corresponding to the tire friction coefficient is -3.2 m / s 2(meters per second squared), when the tire friction coefficient is between 0.15-0.2, the advanced deceleration corresponding to the tire friction coefficient is -2.1m / s 2 ,When the tire friction coefficient is greater than 0.2, the advanced deceleration corresponding to the tire friction coefficient is -1m / s 2 .

[0091] In some embodiments, when the tire slip rate is less than 10%, the advanced deceleration corresponding to the tire slip rate is -0.8 m / s 2 When the tire slip rate is between 10% and 30%, the advanced deceleration corresponding to the tire slip rate is -3.5m / s 2 ; When the tire slip rate is greater than 30%, the advanced deceleration corresponding to the tire slip rate is -4.5m / s 2 .

[0092] Optionally, the target deceleration is determined from the advanced decelerations according to the matching result between the collision risk parameter and the original parameter, including: obtaining the parameter priority corresponding to each collision risk parameter, and taking the first-ranked collision risk parameter as the current parameter according to the parameter priority; in response to the current parameter, obtaining a reference parameter interval corresponding to the current parameter; if the current parameter is within the reference parameter interval, determining the target parameter from each advanced deceleration according to the matching result between the current parameter and the original parameter; if the current parameter is outside the reference parameter interval, taking the collision risk parameter with a rank after the current parameter as the new current parameter according to the parameter priority.

[0093] In some embodiments, when the target vehicle is located in a slippery road area and the target vehicle's speed is less than 60 km / h (kilometers per hour), when the collision time TTC is greater than 4.5 seconds (seconds), the advanced deceleration corresponding to the collision time TTC is 0 m / s 2 When the collision time TTC is between 3s-4.5s, the advanced deceleration corresponding to the collision time TTC is -2.5m / s 2 When the collision time TTC is less than 3s, the advanced deceleration corresponding to the collision time TTC is -3.5m / s 2 ; When the target vehicle is located in a slippery road area and the target vehicle's speed is greater than or equal to 60km / h, when the collision time TTC is greater than 6s, the advanced deceleration corresponding to the collision time TTC is 0m / s 2 When the collision time TTC is between 4s-6s, the advanced deceleration corresponding to the collision time TTC is -3m / s 2 When the collision time TTC is less than 4s, the advanced deceleration corresponding to the collision time TTC is -4m / s 2 .

[0094] Optionally, if the road surface recognition result meets a preset secondary determination condition, the method further includes: generating road surface prompt information, wherein the road surface prompt information is used to prompt a user in the target vehicle that the target vehicle is in a slippery road area.

[0095] In some embodiments, if the target vehicle is in a slippery road area, graphic frames such as "Snow AEB" and "Rain AEB" will appear on the front windshield and the central control display screen. At the same time, the system will play a preset prompt sound, such as a "dong dong" sound, to prompt the user that the target vehicle is in a slippery road area.

[0096] Optionally, if the road surface recognition result meets the preset secondary judgment condition, the method also includes: if the relative distance between the target vehicle and the target obstacle is less than the preset warning distance, generating risk warning information, wherein the risk warning information is used to prompt the user in the target vehicle that the target vehicle is at risk of collision.

[0097] Optionally, the target vehicle is provided with an environmental algorithm model, which includes a target recognition model and / or an environmental perception model, wherein the target recognition model is used to perform target recognition on the road surface on which the target vehicle is traveling, and the environmental perception model is used to identify the target obstacle corresponding to the target vehicle.

[0098] Optionally, the method further includes: iteratively training the environment algorithm model according to preset training sample data to optimize model parameters of the environment algorithm model.

[0099] In some embodiments, the environmental algorithm model in the target vehicle is iteratively trained using training sample data, including: collecting large, diverse, and high-quality data sets, including samples from various real-world scenarios to fully reflect the complexity of the real world; accurately labeling the collected data to ensure that the model can learn the correct features and categories; using this data to train and optimize the recognition model, and through an iterative training process, continuously adjusting the parameters and structure of the model to improve its recognition accuracy and generalization ability.

[0100] In some embodiments, advanced technologies such as deep learning are used to enable the environmental algorithm model to automatically extract useful features from raw data without the need for manual feature design, and to fully utilize the complementarity of various information and improve recognition accuracy by fusing features from multiple sources and types.

[0101] In some embodiments, new data samples are received through the environmental algorithm model, and its internal parameters and structure are updated in real time to adapt to new environments and scenarios; and, models that have been trained in related fields or scenarios are used to quickly adapt to new recognition tasks through methods such as fine-tuning, thereby reducing the demand for new data and training time.

[0102] Optionally, the method further includes: optimizing parameters of the environmental algorithm model according to user feedback data corresponding to the environmental algorithm model.

[0103] In some embodiments, user feedback information is collected to understand the performance of the model in actual applications, and targeted improvements are made based on the feedback.

[0104] Optionally, the method further includes: iteratively updating the environment algorithm model according to a model accuracy rate corresponding to the environment algorithm model until the model accuracy rate reaches a preset accuracy rate.

[0105] In some embodiments, the recognition performance of the model is regularly evaluated to ensure accuracy in order to monitor the stability and effectiveness of the model.

[0106] Optionally, the method also includes: obtaining environmental sample data corresponding to different environmental requirements, and iteratively training the environmental algorithm model according to each environmental sample data to optimize the model parameters of the environmental algorithm model, wherein the environmental requirements include one or more of lighting conditions, environmental conditions, and temperature conditions.

[0107] In some embodiments, the recognition requirements and challenges in different scenarios are analyzed in depth, and optimization and adjustment are performed for specific scenarios; and the adaptability of the target recognition model to different lighting conditions, weather conditions, obstacles and other environmental factors is improved to ensure high recognition accuracy in various complex environments.

[0108] Combination Figure 4 As shown, the embodiment of the present disclosure provides a vehicle control method for rainy and snowy weather, including:

[0109] Step S401, obtaining weather-related parameters corresponding to the target vehicle;

[0110] Wherein, the weather-related parameters include driving environment parameters and / or tire adhesion parameters;

[0111] Step S402, determine whether the weather-related parameters meet the first-level determination conditions, if yes, jump to step S403, if not, jump to step S401;

[0112] Among them, the first-level judgment conditions are determined by conducting vehicle tests in different test environments, including rainy and snowy weather environments;

[0113] Step S403, generating road condition information according to the vehicle-road cooperative data and the network map data, and broadcasting the road condition information to the user in the target vehicle;

[0114] Step S404, performing target recognition on the road surface on which the target vehicle is traveling, obtaining a road surface recognition result, and performing path planning according to the parameters of each sensor to obtain a vehicle driving path of the target vehicle;

[0115] Step S406, if the road surface recognition result includes recognizing a slippery road surface area, mapping the target vehicle and the slippery road surface area to the vehicle driving path, and simulating the driving of the target vehicle according to the vehicle driving path;

[0116] Step S407, determine whether the target vehicle meets the secondary determination condition, if yes, jump to step S408, if no, jump to step S404;

[0117] Among them, the secondary judgment conditions include that the target vehicle is in a slippery road area;

[0118] Step S408, predicting the collision risk of the target vehicle to obtain a collision risk level;

[0119] Step S409: Controlling the target vehicle to brake according to the collision risk level.

[0120] The vehicle control method for rainy and snowy weather provided by the embodiment of the present disclosure is adopted, by obtaining the weather-related parameters corresponding to the target vehicle, and if the weather-related parameters meet the preset first-level judgment conditions, the road surface on which the target vehicle is traveling is identified for snow, and the slippery road surface area in the road surface is obtained, so that when the target vehicle is traveling in the slippery road surface area, the target vehicle is controlled to start the automatic braking function. In this way, if the weather-related parameters meet the rainy and snowy weather conditions, it is judged that snow may accumulate on the road surface, so that the snow on the road surface on which the target vehicle is traveling is identified, and the slippery road surface area is obtained, and when the vehicle is in the slippery road surface area, the target vehicle is controlled to brake according to the collision risk level, realizing the automatic emergency braking of the vehicle in the slippery road surface area, providing obstacle avoidance support for the vehicle in the slippery road surface area, thereby improving the safety of vehicle driving.

[0121] Combination Figure 5 As shown, the embodiment of the present disclosure provides a system architecture for implementing a vehicle control method for rainy and snowy weather, including:

[0122] The sensor layer includes image sensors, radar sensors, tire speed sensors, temperature sensors, vehicle-road cooperative systems, and map systems. The image sensor is used to collect images of the target vehicle's surroundings to obtain environmental images; the radar sensor is used to collect point cloud data around the target vehicle; the tire speed sensor is used to collect the vehicle speed of the target vehicle; the temperature sensor is used to collect the ambient temperature of the target vehicle's environment; the vehicle-road cooperative system is used to collect environmental information around the vehicle; and the map system is used to collect map data from the server.

[0123] The first-level control layer is used to generate the road condition information of the target vehicle based on the vehicle-road cooperative data and network map data if the weather-related parameters meet the preset first-level judgment conditions; perform target recognition on the road surface on which the target vehicle is traveling to obtain road surface recognition results; map the target vehicle and slippery road area to the vehicle's driving path, and simulate the target vehicle's driving according to the vehicle's driving path.

[0124] The risk assessment layer is used to predict the collision risk of the target vehicle and obtain the collision risk level if the road surface recognition result meets the preset secondary judgment conditions.

[0125] The decision layer is used to control the target vehicle to brake according to the collision risk level.

[0126] Combination Figure 6 As shown, the embodiment of the present disclosure provides a vehicle control system for rainy and snowy weather, including an acquisition module 601, a primary determination module 602 and a secondary determination module 603.

[0127] The acquisition module 601 is used to acquire weather-related parameters corresponding to the target vehicle, wherein the weather-related parameters include driving environment parameters and / or tire adhesion parameters.

[0128] The first-level determination module 602 is used to identify the road surface on which the target vehicle is traveling and obtain a road surface identification result if the weather-related parameters meet the preset first-level determination conditions, wherein the first-level determination conditions are determined by performing vehicle tests in different test environments, including rainy and snowy weather environments.

[0129] The secondary determination module 603 is used to predict the collision risk of the target vehicle and obtain the collision risk level if the road surface recognition result meets the preset secondary determination conditions, so as to control the target vehicle to brake according to the collision risk level, wherein the secondary determination conditions include that the target vehicle is in a slippery road area.

[0130] The vehicle control system for rainy and snowy weather provided by the embodiment of the present disclosure obtains the weather-related parameters corresponding to the target vehicle, and if the weather-related parameters meet the preset first-level judgment conditions, the road surface on which the target vehicle is traveling is identified for snow, and the slippery road surface area in the road surface is obtained, so that when the target vehicle is traveling in the slippery road surface area, the target vehicle is controlled to start the automatic braking function. In this way, if the weather-related parameters meet the rainy and snowy weather conditions, it is judged that snow may accumulate on the road surface, so that the snow on the road surface on which the target vehicle is traveling is identified, and the slippery road surface area is obtained, and when the vehicle is in the slippery road surface area, the target vehicle is controlled to brake according to the collision risk level, realizing automatic emergency braking of the vehicle in the slippery road surface area, providing obstacle avoidance support for the vehicle in the slippery road surface area, thereby improving the safety of vehicle driving.

[0131] The embodiment of the present disclosure also provides a vehicle terminal, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the vehicle terminal executes the above method.

[0132] Figure 7 The structure diagram of the computer system of the vehicle terminal suitable for implementing the embodiment of the present application is shown. It should be noted that: Figure 7 The computer system 700 of the vehicle terminal shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0133] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage part 708 to the random access memory (RAM) 703, such as executing the method in the above embodiment. In the RAM 703, various programs and data required for system operation are also stored. The CPU 701, the ROM 702 and the RAM 703 are connected to each other through the bus 704. The input / output (I / O) interface 705 is also connected to the bus 704.

[0134] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed so that a computer program read therefrom is installed into the storage section 708 as needed.

[0135] The vehicle terminal disclosed in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the vehicle terminal executes each step of the above method.

[0136] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operation can be changed. Parts and sub-samples of some embodiments may be included in or replace parts and sub-samples of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of a stated sub-sample, whole, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other sub-samples, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0138] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some sub-samples can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0139] The flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A vehicle control method for rainy and snowy weather, characterized in that: include: Acquiring weather-related parameters corresponding to the target vehicle, wherein the weather-related parameters include driving environment parameters and / or tire adhesion parameters; If the weather-related parameters meet the preset first-level determination conditions, target recognition is performed on the road surface on which the target vehicle is traveling to obtain a road surface recognition result, wherein the first-level determination conditions are determined by performing vehicle tests in different test environments, and the test environment includes a rainy and snowy weather environment; If the road surface recognition result meets the preset secondary determination condition, the collision risk of the target vehicle is predicted to obtain a collision risk level, so as to control the target vehicle to brake according to the collision risk level, wherein the secondary determination condition includes that the target vehicle is in a slippery road area.

2. The method according to claim 1, characterized in that If the weather-related parameters meet the preset first-level determination conditions, the method further includes: Obtain vehicle-road collaboration data and network map data; Generate road condition information of the target vehicle according to the vehicle-road cooperative data and the network map data; The road condition information is broadcast to users in the target vehicle.

3. The method according to claim 1, characterized in that Whether the road surface recognition result meets the secondary judgment condition is determined by the following methods: Collecting data by using a plurality of vehicle sensors disposed on the target vehicle to obtain sensor parameters collected by each of the vehicle sensors; Perform path planning according to the sensor parameters to obtain a vehicle driving path of the target vehicle; If the road surface recognition result includes recognition of a slippery road surface area, the target vehicle and the slippery road surface area are mapped to the vehicle driving path, and the target vehicle driving is simulated according to the vehicle driving path to determine whether the target vehicle is driving in the slippery road surface area according to the simulation result.

4. The method according to claim 3, characterized in that Performing path planning according to the sensor parameters to obtain a vehicle driving path of the target vehicle includes: Classifying the sensor parameters according to different parameter types, and fusing the sensor parameters of the same parameter type to obtain fused data, wherein the fused data includes global data and real-time data, the global data includes map data and / or road condition data, and the implementation data includes image sensor data and / or radar sensor data; If the global data includes map data, a starting position and a target position are set in the map data, so as to perform path planning between the starting position and the target position according to the global data to obtain a vehicle travel path; Environmental perception data is obtained by performing environmental perception on the environment in which the target vehicle is located based on the real-time data, so as to adjust the vehicle's driving path based on the environmental perception data to avoid the target vehicle from colliding with the target obstacle, wherein the environmental perception data includes the obstacle position and / or obstacle motion trajectory corresponding to the target obstacle.

5. The method according to any one of claims 1 to 4, characterized in that: Predicting the collision risk of the target vehicle to obtain a collision risk level, and controlling the target vehicle to brake according to the collision risk level, including: Obtain a deceleration matching table, wherein the deceleration matching table stores a plurality of original parameters, a reference deceleration corresponding to each of the original parameters, and an advanced deceleration corresponding to each of the original parameters, wherein the reference deceleration corresponding to the original parameter is smaller than the advanced deceleration corresponding to the original parameter; In response to identifying a target obstacle corresponding to the target vehicle, obtaining a collision risk parameter, wherein the collision risk parameter includes at least one of a relative distance between the target vehicle and the target obstacle, a relative speed between the target vehicle and the target obstacle, a collision time between the target vehicle and the target obstacle, a tire friction coefficient of the target vehicle, and a tire slip rate of the target vehicle; determining a collision risk level according to the collision risk parameter; If the collision risk level is greater than a preset level threshold and the target vehicle is in a normal area, determining a target deceleration from the reference deceleration according to a matching result between the collision risk parameter and the original parameter, and controlling the target vehicle to brake according to the target deceleration, wherein the normal area is different from the slippery road area; If the collision risk level is greater than a preset level threshold and the target vehicle is in a slippery road area, a target deceleration is determined from the advanced deceleration according to a matching result between the collision risk parameter and the original parameter, and the target vehicle is controlled to brake according to the target deceleration.

6. The method according to claim 5, characterized in that Determining a target deceleration from the advanced deceleration according to a matching result between the collision risk parameter and the original parameter includes at least one of the following: Determining the intermediate decelerations corresponding to the respective collision risk parameters from the respective advanced decelerations according to the matching result between the collision risk parameters and the original parameters, and taking the maximum intermediate deceleration as the target deceleration according to the comparison result between the respective intermediate decelerations; Obtaining parameter priorities corresponding to the collision risk parameters respectively, and taking the collision risk parameter with the first priority as the current parameter according to the parameter priorities; In response to a current parameter, obtaining a reference parameter interval corresponding to the current parameter; If the current parameter is within the reference parameter range, the target parameter is determined from each of the advanced decelerations according to the matching result between the current parameter and the original parameter; if the current parameter is outside the reference parameter range, the collision risk parameter that is one order after the current parameter is used as the new current parameter according to the parameter priority.

7. The method according to claim 5, characterized in that If the road surface recognition result meets the preset secondary determination condition, the method further includes at least one of the following: generating road surface prompt information, wherein the road surface prompt information is used to prompt a user in the target vehicle that the target vehicle is in a slippery road area; If the relative distance between the target vehicle and the target obstacle is less than a preset warning distance, risk warning information is generated, wherein the risk warning information is used to prompt a user in the target vehicle that the target vehicle has a collision risk.

8. The method according to claim 5, characterized in that The method further comprises: Iteratively training the environment algorithm model according to preset training sample data to optimize the model parameters of the environment algorithm model, wherein the environment algorithm model includes a target recognition model and / or an environment perception model, the target recognition model is used to perform target recognition on the road surface on which the target vehicle is traveling, and the environment perception model is used to identify the target obstacle corresponding to the target vehicle; Optimizing parameters of the environment algorithm model according to user feedback data corresponding to the environment algorithm model; Iteratively updating the environment algorithm model according to the model accuracy corresponding to the environment algorithm model until the model accuracy reaches a preset accuracy; Environmental sample data corresponding to different environmental requirements are obtained, and the environmental algorithm model is iteratively trained according to each of the environmental sample data to optimize the model parameters of the environmental algorithm model, wherein the environmental requirements include one or more of lighting conditions, environmental conditions, and temperature conditions.

9. A vehicle control system for rainy and snowy weather, characterized in that: include: An acquisition module, used for acquiring weather-related parameters corresponding to the target vehicle, wherein the weather-related parameters include driving environment parameters and / or tire adhesion parameters; A primary determination module, configured to perform target recognition on the road surface on which the target vehicle is traveling to obtain a road surface recognition result if the weather-related parameter satisfies a preset primary determination condition, wherein the primary determination condition is determined by performing vehicle tests in different test environments, and the test environment includes a rainy and snowy weather environment; The secondary determination module is used to predict the collision risk of the target vehicle and obtain a collision risk level if the road surface recognition result meets a preset secondary determination condition, so as to control the target vehicle to brake according to the collision risk level, wherein the secondary determination condition includes that the target vehicle is in a slippery road area.

10. A vehicle terminal, characterized in that: include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the vehicle terminal executes the method according to any one of claims 1 to 8.

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