Vehicle anti-collision method, system, electronic device, vehicle and storage medium

Abstract

The application provides a vehicle anti-collision method, system, electronic equipment, vehicle and storage medium, and belongs to the technical field of vehicle safety. The scheme comprises the following steps: acquiring current environment information and first driving information of a first vehicle, wherein the first driving information comprises geographical position and first dynamic data of the first vehicle; acquiring second dynamic data of a second vehicle; determining a vehicle working condition of the first vehicle according to the first dynamic data; judging whether a warning condition occurs according to the current environment information, the first driving information and the vehicle working condition; when the warning condition occurs, judging whether an emergency collision condition occurs; when the emergency collision condition occurs, inputting the current environment information, the first dynamic data, the second dynamic data and the vehicle working condition into an anti-collision model to obtain electromagnetic force parameters, and controlling an electromagnetic device of the first vehicle to generate electromagnetic force according to the electromagnetic force parameters, so that the first vehicle and the second vehicle repel each other, the occurrence of collision can be reduced, and the safety of the vehicle during driving can be improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle safety technology, and in particular to a vehicle collision avoidance method, system, electronic device, vehicle, and storage medium. Background Technology

[0002] With socio-economic development, more and more people can afford to buy cars. This surge in the number of vehicles has increased the complexity of road traffic, raising the risk of collisions to some extent. Vehicle safety technology employs active braking systems to reduce collisions in emergency situations, but these systems may not be able to bring the vehicle to a complete stop at high speeds, still failing to prevent a collision. Furthermore, if a driver accidentally accelerates in an emergency, or if a vehicle suddenly changes lanes, causing other vehicles to suddenly appear in the driver's lane, the active braking system may fail, leading to a collision. Summary of the Invention

[0003] The main objective of this application is to provide a vehicle collision avoidance method, system, electronic device, vehicle, and storage medium, which aims to effectively reduce the occurrence of collisions and improve vehicle safety during driving.

[0004] To achieve the above objectives, one aspect of this application provides a vehicle collision avoidance method, the vehicle collision avoidance method comprising:

[0005] Obtain current environmental information and first driving information of the first vehicle, wherein the first driving information includes the geographical location and first dynamic data of the first vehicle;

[0006] Acquire second dynamic data of the second vehicle, wherein the second vehicle and the first vehicle are located in the same lane, and the second dynamic data includes the relative position and relative speed of the second vehicle and the first vehicle;

[0007] The vehicle operating condition of the first vehicle is determined based on the first dynamic data;

[0008] Determine whether a warning condition has occurred based on the current environmental information, the first driving information, and the vehicle operating condition.

[0009] When the aforementioned warning condition occurs, determine whether an emergency collision condition has occurred;

[0010] When the emergency collision condition occurs, the current environmental information, the first dynamic data, the second dynamic data, and the vehicle operating condition are input into the collision avoidance model to obtain electromagnetic force parameters;

[0011] The electromagnetic device of the first vehicle is controlled according to the electromagnetic force parameters to generate electromagnetic force so that the first vehicle and the second vehicle repel each other.

[0012] In some embodiments, obtaining the current environmental information and the first driving information of the first vehicle includes the following steps:

[0013] The first dynamic data is obtained by collecting the vehicle speed, gear, engine speed, brake pedal opening, accelerator pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle and steering wheel angular velocity of the first vehicle through the sensing components.

[0014] The geographical location of the first vehicle is obtained through a positioning system, and the geographical location and the first dynamic data are determined as the first driving information;

[0015] Based on the geographical location, the current road surface temperature, road surface roughness, road surface slope, air temperature, humidity, altitude, air pressure, rainfall, and wind force are obtained to obtain the current environmental information.

[0016] In some embodiments, acquiring the second dynamic data of the second vehicle includes the following steps:

[0017] The image sequence around the first vehicle is acquired using a camera;

[0018] Identify the second vehicle located in the same lane based on the image sequence;

[0019] The relative position and relative speed of the second vehicle relative to the first vehicle are calculated based on the image sequence to obtain second dynamic data, wherein the relative position includes relative direction and relative distance.

[0020] In some embodiments, determining the vehicle operating condition of the first vehicle based on the first dynamic data includes the following steps:

[0021] The vehicle status of the first vehicle is determined based on the vehicle speed and the gear.

[0022] When the vehicle is in driving mode, the vehicle operating condition of the first vehicle is determined based on the vehicle speed, gear, engine speed, brake pedal opening, accelerator pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle, and steering wheel angular velocity.

[0023] In some embodiments, determining whether a warning condition has occurred based on the current environmental information, the first driving information, and the vehicle operating condition includes the following steps:

[0024] The corresponding collision warning model is determined based on the current environmental information and the vehicle operating condition, wherein the input layer of the collision warning model has preset weights for each input parameter;

[0025] Each parameter in the first dynamic data is input into the collision warning model to obtain a collision warning result, wherein the collision warning result includes the warning activation timing;

[0026] Determine whether an early warning condition has occurred based on the aforementioned early warning activation timing.

[0027] In some embodiments, determining whether an emergency collision condition has occurred includes the following steps:

[0028] The safe distance is obtained based on the vehicle speed, the relative speed, and the longitudinal acceleration. The safe distance and the relative distance are compared to determine whether the first sub-condition has occurred.

[0029] Determine whether the second sub-condition has occurred based on the accelerator pedal opening;

[0030] Determine whether a third sub-condition has occurred based on the aforementioned geographical location;

[0031] The emergency collision condition occurs when one of the first, second, and third sub-conditions is met, wherein the first sub-condition is that the safe distance is greater than the relative distance, the second sub-condition is that the accelerator pedal opening is not equal to zero, and the third sub-condition is that an accident has occurred on the current road segment and the first vehicle is approaching the accident site.

[0032] In some embodiments, the collision avoidance model is obtained through the following steps:

[0033] Collect real-vehicle collision data and label the real-vehicle collision data, wherein the label of the real-vehicle collision data is electromagnetic force parameter;

[0034] A training dataset is formed based on the labeled real-vehicle collision data.

[0035] The training dataset is input into the initialized collision avoidance model for training, resulting in a trained collision avoidance model.

[0036] In some embodiments, the electromagnetic device controlling the first vehicle to generate electromagnetic force according to the electromagnetic force parameters includes the following steps:

[0037] When the relative direction is such that the second vehicle is in front of the first vehicle, the electromagnetic device at the front of the first vehicle is controlled to generate electromagnetic force according to the electromagnetic force parameters.

[0038] Alternatively, when the relative direction is such that the second vehicle is behind the first vehicle, the electromagnetic device at the rear of the first vehicle is controlled to generate electromagnetic force according to the electromagnetic force parameters.

[0039] To achieve the above objectives, another aspect of this application provides a vehicle collision avoidance system, the vehicle collision avoidance system comprising:

[0040] The first module is used to acquire current environmental information and first driving information of the first vehicle, wherein the first driving information includes the geographical location and first dynamic data of the first vehicle;

[0041] The second module is used to acquire second dynamic data of the second vehicle, wherein the second vehicle and the first vehicle are located in the same lane, and the second dynamic data includes the relative position and relative speed of the second vehicle and the first vehicle.

[0042] The third module is used to determine the vehicle operating condition of the first vehicle based on the first dynamic data.

[0043] The fourth module is used to determine whether a warning condition has occurred based on the current environmental information, the first driving information, and the vehicle operating condition.

[0044] The fifth module is used to determine whether an emergency collision condition has occurred when the aforementioned warning condition is met.

[0045] The sixth module is used to input the current environmental information, the first dynamic data, the second dynamic data and the vehicle operating condition into the collision avoidance model when the emergency collision condition occurs, so as to obtain electromagnetic force parameters.

[0046] The seventh module is used to control the electromagnetic device of the first vehicle to generate electromagnetic force according to the electromagnetic force parameters, so as to make the first vehicle and the second vehicle repel each other.

[0047] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0048] To achieve the above objectives, another aspect of this application provides a vehicle that includes the aforementioned vehicle collision avoidance system or electronic equipment.

[0049] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0050] The embodiments of this application include at least the following beneficial effects: The main objective of the embodiments of this application is to propose a vehicle collision avoidance method, system, electronic device, vehicle, and storage medium. This solution obtains current environmental information and first driving information of a first vehicle, wherein the first driving information includes the geographical location and first dynamic data of the first vehicle; obtains second dynamic data of a second vehicle; determines the vehicle operating condition of the first vehicle based on the first dynamic data; determines whether a warning condition has occurred based on the current environmental information, the first driving information, and the vehicle operating condition; if a warning condition occurs, determines whether an emergency collision condition has occurred; if an emergency collision condition occurs, inputs the current environmental information, the first dynamic data, the second dynamic data, and the vehicle operating condition into the collision avoidance model to obtain electromagnetic force parameters, and controls the electromagnetic device of the first vehicle to generate electromagnetic force based on the electromagnetic force parameters, so that the first vehicle and the second vehicle repel each other, thereby reducing the occurrence of collisions and improving vehicle safety during driving. Attached Figure Description

[0051] Figure 1 This is a flowchart of the vehicle collision avoidance method provided in the embodiments of this application;

[0052] Figure 2 This is a flowchart illustrating the specific method steps of step S101 provided in the embodiments of this application;

[0053] Figure 3 This is a flowchart illustrating the specific method steps of step S102 provided in the embodiments of this application;

[0054] Figure 4 This is a flowchart illustrating the specific method steps of step S103 provided in the embodiments of this application;

[0055] Figure 5 This is a flowchart illustrating the specific method steps of step S104 provided in the embodiments of this application;

[0056] Figure 6 This is a flowchart illustrating the specific method steps of step S105 provided in the embodiments of this application;

[0057] Figure 7 This is a flowchart of constructing an anti-collision model provided in an embodiment of this application;

[0058] Figure 8 This is a flowchart illustrating the specific method steps of step S107 provided in the embodiments of this application;

[0059] Figure 9 This is a flowchart of a vehicle collision avoidance method provided in another embodiment of this application;

[0060] Figure 10 This is a schematic diagram of the vehicle collision avoidance system provided in the embodiments of this application;

[0061] Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of systems and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0063] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0064] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0066] The method provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal may be a touch display terminal, a multi-functional operating terminal, or a vehicle-mounted terminal, but is not limited thereto; the server may be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server may also be a node server in a blockchain network; the software may be an application implementing the method, but is not limited to the above forms.

[0067] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0068] Reference Figure 1 , Figure 1 This is an optional flowchart of the vehicle collision avoidance method provided in the embodiments of this application. The vehicle collision avoidance method may include, but is not limited to, steps S101 to S107.

[0069] Step S101: Obtain current environmental information and first driving information of the first vehicle, wherein the first driving information includes the geographical location and first dynamic data of the first vehicle.

[0070] Step S102: Obtain the second dynamic data of the second vehicle, wherein the second vehicle and the first vehicle are located in the same lane, and the second dynamic data includes the relative position and relative speed of the second vehicle and the first vehicle.

[0071] Step S103: Determine the vehicle operating condition of the first vehicle based on the first dynamic data.

[0072] Step S104: Determine whether a warning condition has occurred based on the current environmental information, the first driving information, and the vehicle's operating condition.

[0073] Step S105: When a warning condition occurs, determine whether an emergency collision condition has occurred.

[0074] Step S106: When an emergency collision occurs, the current environmental information, the first dynamic data, the second dynamic data, and the vehicle operating conditions are input into the collision avoidance model to obtain electromagnetic force parameters.

[0075] Step S107: Control the electromagnetic device of the first vehicle to generate electromagnetic force according to the electromagnetic force parameters so that the first vehicle and the second vehicle repel each other.

[0076] In this embodiment, the current environmental information and the first driving information of the first vehicle are first acquired. The first driving information includes the geographical location and dynamic data of the first vehicle. Specifically, the first vehicle is a vehicle driven by a user. When the user gets into the vehicle and starts the vehicle, the current environmental information and the first driving information of the first vehicle can be acquired to achieve safety warning and collision avoidance control. By understanding the environment around the vehicle and the vehicle's own driving situation in real time, potential safety risks can be detected in a timely manner, thereby taking corresponding measures to reduce the occurrence of accidents.

[0077] Next, other vehicles in the same lane as the first vehicle are identified as the second vehicle, and second dynamic data of the second vehicle is obtained. This second dynamic data includes the relative position and relative speed of the second vehicle relative to the first vehicle. During vehicle travel, the trajectories of the second vehicle and the first vehicle in the same lane overlap. A sudden deceleration or stop of the first vehicle, or a rapid approach of a following vehicle, could lead to a collision. The second vehicle is the primary source of collision risk. Therefore, other vehicles located in front of or behind the first vehicle are designated as the target vehicles for collision detection (i.e., the second vehicle). Based on the speed and position of the first vehicle, the relative position and relative speed of the second vehicle relative to the first vehicle are determined, resulting in the second dynamic data of the second vehicle.

[0078] Understandably, the target vehicle can be one or two vehicles. When there are other vehicles in front of and behind the first vehicle, the second dynamic data of the front and rear vehicles can be obtained separately for subsequent targeted collision warning and collision avoidance control.

[0079] After obtaining the first driving information of the first vehicle, the vehicle operating condition of the first vehicle is determined based on the first dynamic data. The vehicle operating condition refers to the state of the vehicle during operation, including driving conditions such as starting, accelerating, constant speed, deceleration, turning, going uphill and downhill, and stopping, as well as operating conditions under gear shifting, coasting, braking, throttle speed control, steering, and reversing light control modes. The changes in the first dynamic data of the vehicle under different operating conditions have different patterns. Collision avoidance calculations are performed through different vehicle operating conditions to adapt to the collision avoidance requirements in different scenarios and improve the safety of the vehicle during driving.

[0080] Next, based on the current environmental information, the first driving information, and the vehicle's operating condition, it is determined whether a warning condition has occurred. In related technologies, the focus is often solely on the distance to obstacles and the vehicle's speed to determine the risk of collision, neglecting the differences in braking performance under different operating conditions and environments. While the first dynamic data in the first driving information directly reflects the vehicle's current driving situation, its relationship with collision risk is not a simple linear one. The vehicle collision avoidance method of this application determines whether a warning condition has occurred based on the current environmental information, the first driving information, and the vehicle's operating condition. It can adaptively adjust the timing of the warning activation under different environments and operating conditions, thereby determining whether a warning condition has occurred for different situations.

[0081] When a warning condition is triggered, an emergency collision condition is assessed. Triggering a warning condition indicates a potential collision while the vehicle is in motion, but the collision risk under these conditions is usually low. After receiving the warning, the vehicle's automatic braking system or the driver's application of the brake pedal can prevent a collision. However, at excessive speeds, the automatic braking system may not be able to bring the vehicle to a complete stop. Furthermore, if the driver reacts irrationally due to excessive psychological pressure and mistakenly presses the accelerator, or if an accident occurs ahead in the lane and a second vehicle in front of this vehicle (the first vehicle) can detect and avoid it, but this vehicle is obstructed by the second vehicle and cannot detect the accident in advance, the automatic braking system may fail if the second vehicle suddenly changes lanes, causing another vehicle to suddenly appear in this lane, thus failing to prevent a collision.

[0082] To address the problems mentioned above, this application embodiment further determines whether an emergency collision condition has occurred when a warning condition is triggered. An emergency collision condition indicates that the vehicle is about to collide, the active braking system is unable to prevent the collision, and other collision avoidance measures need to be taken to mitigate the consequences of the collision or reduce the possibility of a collision.

[0083] In the event of an emergency collision, the current environmental information, first dynamic data, second dynamic data, and vehicle operating conditions are input into the collision avoidance model to obtain electromagnetic force parameters. Specifically, the collision avoidance model is pre-trained using deep learning based on real-vehicle collision data. It can learn the electromagnetic force parameters required to avoid vehicle collisions under different environments and operating conditions, and adjust the electromagnetic force parameters in real time according to the input data.

[0084] It should be noted that electromagnetic force parameters can be magnetic field strength, or factors related to magnetic field strength such as current intensity and the number of coil turns. In electromagnets or electromagnetic actuators, the greater the current intensity, the stronger the magnetic field generated, and consequently, the greater the electromagnetic force. When designing electromagnetic devices, multiple independent coil groups can be included, each with a different number of turns. By controlling the on / off state of different coil groups with electronic switches, the number of coil turns can be indirectly adjusted to achieve the purpose of adjusting the magnetic field strength and electromagnetic force.

[0085] For example, in high-altitude areas, users may experience altitude sickness while driving, leading to sluggish reactions and requiring more external intervention to reduce the occurrence of collisions. Therefore, based on the learning results trained using real-vehicle collision data, it can be determined that the mapping relationship between altitude and electromagnetic force parameters is directly proportional. Alternatively, when driving on roads with low roughness, the friction between the vehicle and the ground also decreases, similarly requiring more external intervention. In this case, it can be determined that the mapping relationship between road roughness and electromagnetic force is inversely proportional. Based on current environmental information, first dynamic data, second dynamic data, and the mapping relationship between vehicle operating conditions and electromagnetic force parameters, the magnitude of the electromagnetic force parameters required to reduce the occurrence of collisions is calculated, so as to subsequently generate the electromagnetic force used to control vehicle deceleration.

[0086] Finally, the electromagnetic device of the first vehicle is controlled according to the electromagnetic force parameters to generate electromagnetic force, so that the first vehicle and the second vehicle repel each other. Optionally, the electromagnetic device can be installed on the front and rear anti-collision beams and around the body of the first vehicle. The electromagnetic device can generate electromagnetic force according to the electromagnetic force parameters, thereby assisting the vehicle to decelerate smoothly, effectively reducing the occurrence of collisions or the damage caused by collisions, and improving the safety of the vehicle during driving.

[0087] Reference Figure 2 In some embodiments, step S101 may include, but is not limited to, steps S201 to S203.

[0088] Step S201: The vehicle speed, gear, engine speed, brake pedal opening, accelerator pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle and steering wheel angular velocity of the first vehicle are collected by the sensing components to obtain the first dynamic data.

[0089] Step S202: Obtain the geographical location of the first vehicle through the positioning system, and determine the geographical location and the first dynamic data as the first driving information.

[0090] Step S203: Obtain the road surface temperature, road surface roughness, road surface slope, air temperature, humidity, altitude, air pressure, rainfall and wind force of the current road section based on the geographical location to obtain the current environmental information.

[0091] In this embodiment, the vehicle speed, gear, engine speed, brake pedal opening, accelerator pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle and steering wheel angular velocity of the first vehicle are first collected by the sensing components to obtain the first dynamic data.

[0092] Specifically, a sensing component is provided on the first vehicle to collect first dynamic data of the first vehicle. The sensing component includes a variety of sensors. For example, the vehicle speed is calculated by measuring the rotational speed of the wheels or the rotational speed of the drive shaft using a vehicle speed sensor installed on the vehicle's transmission system or wheels; the gear position sensor is detected by detecting the current gear of the transmission using a gear position sensor installed on the transmission; the engine speed is measured by an engine speed sensor installed on the engine; the opening of the brake pedal and accelerator pedal are detected by pedal sensors installed under the pedals respectively; the longitudinal and lateral acceleration of the vehicle is measured by an acceleration sensor installed on the vehicle chassis or body; the rotational speed of the vehicle about its vertical axis is measured by a yaw rate sensor based on the principle of rotational inertia of the vehicle; and the steering wheel angle and steering wheel angular velocity are collected by an angular velocity sensor installed on the steering wheel mechanism.

[0093] Next, the geographical location of the first vehicle is obtained through the positioning system, and the geographical location and the first dynamic data are determined as the first driving information. Specifically, the geographical location of the first vehicle is obtained through the vehicle's GPS, which includes the vehicle's current longitude and latitude, and the current road segment. The geographical location and the first dynamic data are then determined as the first driving information.

[0094] Based on the geographical location, the system obtains current road surface temperature, surface roughness, road slope, air temperature, humidity, altitude, air pressure, rainfall, and wind speed to acquire current environmental information. Specifically, after obtaining the geographical location, the system can access the current road surface conditions, such as surface temperature, surface roughness, road slope, air temperature, humidity, altitude, air pressure, rainfall, and wind speed, via network connection for subsequent collision avoidance calculations.

[0095] Reference Figure 3 In some embodiments, step S102 may include, but is not limited to, steps S301 to S303.

[0096] Step S301: Acquire an image sequence around the first vehicle using a camera.

[0097] Step S302: Identify the second vehicle located in the same lane based on the image sequence.

[0098] Step S303: Calculate the relative position and relative speed of the second vehicle relative to the first vehicle based on the image sequence to obtain the second dynamic data, wherein the relative position includes the relative direction and the relative distance.

[0099] In this embodiment, an image sequence around the first vehicle can be acquired by a camera, target detection can be performed on the image sequence, other vehicles in the image sequence can be extracted, a second vehicle located in the same lane can be identified, and the relative position and relative speed of the second vehicle relative to the first vehicle can be calculated based on the image sequence to obtain second dynamic data. The relative position includes relative direction and relative distance. The relative direction refers to whether the second vehicle is in front of or behind the first vehicle. Optionally, the front in the camera's field of view is determined as the front of the first vehicle, and the rear in the camera's field of view is determined as the rear of the first vehicle.

[0100] In other embodiments, to cope with complex application scenarios, the first vehicle may be equipped with multiple cameras, each responsible for capturing image sequences from different directions, and the relative directions are determined by combining image processing technology and algorithms.

[0101] For example, a camera is set in the middle of the front and the middle of the rear of the first vehicle. The camera acquires an image sequence around the first vehicle, identifies lane lines and vehicles in the image sequence, determines the two lane lines in the middle of the image sequence as the lane lines of the first vehicle, and determines the vehicle in the middle of the two lane lines as the second vehicle. According to the shooting direction of the camera, the second vehicle captured by the front camera is located in front of the first vehicle, and the second vehicle captured by the rear camera is located behind the first vehicle.

[0102] Reference Figure 4 In some embodiments, step S103 may include, but is not limited to, steps S401 to S402.

[0103] Step S401: Determine the vehicle status of the first vehicle based on its speed and gear.

[0104] Step S402: When the vehicle is in driving mode, the vehicle operating condition of the first vehicle is determined based on the vehicle speed, gear, engine speed, brake pedal opening, accelerator pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle, and steering wheel angular velocity.

[0105] In this embodiment, the vehicle status of the first vehicle is first determined based on the vehicle speed and gear. When the user gets in the vehicle and starts the vehicle, the vehicle speed and gear are detected. If the current gear is a gear other than parking or neutral, and the vehicle speed is not equal to zero, the vehicle status of the first vehicle is determined to be driving; otherwise, the vehicle status of the first vehicle is determined to be non-driving.

[0106] When the vehicle is in a non-driving state, it means that the user is not driving. The vehicle will not actively drive on the road when it is in a non-driving state, so there is no need to judge the current environmental information.

[0107] When the vehicle is in driving mode, the vehicle operating condition of the first vehicle is determined based on the vehicle speed, gear, engine speed, brake pedal opening, accelerator pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle, and steering wheel angular velocity.

[0108] Reference Figure 5 In some embodiments, step S104 may include, but is not limited to, steps S501 to S503.

[0109] Step S501: Determine the corresponding collision warning model based on the current environmental information and vehicle operating conditions. The input layer of the collision warning model has preset weights for each input parameter.

[0110] Step S502: Input each parameter in the first dynamic data into the collision warning model as input parameters to obtain the collision warning result, wherein the collision warning result includes the warning activation time.

[0111] Step S503: Determine whether a warning condition has occurred based on the warning activation timing.

[0112] In this embodiment, firstly, a corresponding collision warning model is determined based on the current environmental information and vehicle operating conditions. The input layer of the collision warning model has preset weights for each input parameter. Next, each parameter from the first dynamic data is input into the collision warning model to obtain a collision warning result. This result includes the warning activation timing. Finally, the occurrence of a warning condition is determined based on the warning activation timing.

[0113] For example, in typhoon weather, this embodiment can adaptively adjust the timing of the warning activation based on the wind force and direction in the current environmental information, providing an early warning when the wind will accelerate a collision. Furthermore, during vehicle turning, this embodiment can consider the impact of turning on the braking position based on the vehicle's operating conditions. When turning, the vehicle is subjected to centrifugal force, causing the tire load on the inner wheel to decrease and the tire load on the outer wheel to increase. This change in load distribution directly affects the friction between the wheel and the ground, thus affecting braking performance; therefore, an early warning is also necessary.

[0114] In other embodiments, to determine whether the current road segment is a collision-prone area and whether a collision has recently occurred, historical accident data for the current road segment can be further obtained from a database based on geographical location. This historical accident data includes the number of historical collisions and the types of historical collisions within a preset time period. Collision types can be categorized based on the severity or cause of the collision. When the number of historical collisions exceeds a preset collision count threshold, the current road segment is determined to be a collision-prone area. Optionally, the historical collision data can also be categorized by historical collision type, and the number of historical collisions for each type can be counted to determine the types of collisions prone to occur on the current road segment. The preset weights of each input parameter in the input layer of the collision warning model can then be adjusted based on these types of collisions.

[0115] Reference Figure 6 In some embodiments, step S105 may include, but is not limited to, steps S601 to S604.

[0116] Step S601: Obtain the safe distance based on vehicle speed, relative speed, and longitudinal acceleration; compare the safe distance with the relative distance to determine whether the first sub-condition has occurred.

[0117] Step S602: Determine whether the second sub-condition has occurred based on the accelerator pedal opening.

[0118] Step S603: Determine whether a third sub-condition has occurred based on the geographical location.

[0119] Step S604: When one of the first, second, and third sub-conditions is met, an emergency collision condition occurs. The first sub-condition is that the safe distance is greater than the relative distance, the second sub-condition is that the accelerator pedal opening is not equal to zero, and the third sub-condition is that an accident has occurred on the current driving segment and the first vehicle is approaching the accident site.

[0120] In this embodiment, the emergency collision condition includes a first sub-condition, a second sub-condition, and a third sub-condition. The first sub-condition is that the safe distance is greater than the relative distance, which indicates that the current vehicle speed is too high and the collision cannot be avoided by relying on the vehicle's active braking system. Therefore, the safe distance is obtained based on the vehicle speed, relative speed, and longitudinal acceleration, and the safe distance and the relative distance are compared to determine whether the first sub-condition has occurred.

[0121] The second sub-condition is that the accelerator pedal opening is not equal to zero. This indicates that the user makes an irrational reaction due to excessive psychological pressure and accidentally presses the accelerator, which increases the vehicle's kinetic energy. When a collision occurs, greater kinetic energy means greater impact force. Therefore, the accelerator pedal opening is used to determine whether the second sub-condition has occurred.

[0122] The third sub-condition is that an accident has occurred on the current road segment and the first vehicle is approaching the accident site. This indicates that the current vehicle's view is easily obstructed by vehicles ahead, making it difficult to detect potential safety hazards in time. Specifically, based on real-time traffic information obtained from the geographical location, if an accident occurs ahead, vehicles ahead, whose view is not obstructed, can detect the accident in time and take evasive action. However, the current vehicle can only detect the accident after the vehicles ahead have swerved, and the user may not have enough reaction time, leading to a rear-end collision. Therefore, if the geographical location indicates that an accident has occurred on the current road segment and the first vehicle is approaching the accident site, a collision may occur due to insufficient reaction time.

[0123] An emergency collision condition occurs when one of the first, second, or third sub-conditions is met, requiring other collision avoidance measures to be taken.

[0124] Reference Figure 7 In some embodiments, obtaining the anti-collision model may include, but is not limited to, steps S701 to S703.

[0125] Step S701: Collect real vehicle collision data and label the real vehicle collision data. The label for the real vehicle collision data is electromagnetic force parameter.

[0126] Step S702: A training dataset is formed based on the labeled real-vehicle collision data.

[0127] Step S703: Input the training dataset into the initialized collision avoidance model for training to obtain the trained collision avoidance model.

[0128] In this embodiment, real-vehicle collision data is collected and labeled. The labels for the real-vehicle collision data are electromagnetic force parameters. Specifically, real-vehicle collision data of vehicles colliding under different environments and vehicle operating conditions are collected, and electromagnetic force parameters are used as labels to annotate the real-vehicle collision data. This allows the collision avoidance model to learn the relationship between the input data and the set electromagnetic force parameters. It can be understood that the electromagnetic force parameters are obtained based on input data such as vehicle speed, distance to vehicles in front and behind, relative speed, and environmental information from the real-vehicle collision data. These parameters are used to control the electromagnetic device to generate electromagnetic force to decelerate the vehicle and avoid a collision.

[0129] Next, a training dataset is formed based on the labeled real-vehicle collision data. Optionally, the labeled real-vehicle collision data can be divided into a training set, a validation set, and a test set to obtain the training dataset. The training set is used to train the collision avoidance model, enabling the model to learn how to map the relationship between vehicle speed, distance to vehicles in front and behind, relative speed, and environmental information and electromagnetic force parameters. The validation set is used to evaluate the model's performance during training so that training parameters can be adjusted or the model structure optimized in a timely manner. The test set is used to evaluate the model's generalization ability, i.e., its predictive ability on new data, after the model has been trained.

[0130] Finally, the training dataset is input into the initialized collision avoidance model for training, resulting in a trained collision avoidance model. Specifically, a deep learning algorithm is used to learn patterns in the data through training with a large amount of real-vehicle collision data. The training dataset is input into the model for iterative training. In each iteration, the loss function is calculated based on the difference between the model's prediction results and the true labels, and the model is updated through an optimization algorithm. This iterative process is repeated until the model's performance on the validation set reaches a preset standard or the maximum number of iterations is reached, resulting in a trained collision avoidance model.

[0131] Reference Figure 8 In some embodiments, step S107 may include, but is not limited to, step S801 or step S802.

[0132] Step S801: When the relative direction is that the second vehicle is in front of the first vehicle, the electromagnetic device at the front of the first vehicle is controlled to generate electromagnetic force according to the electromagnetic force parameters.

[0133] Step S802: When the relative direction is that the second vehicle is behind the first vehicle, the electromagnetic device at the rear of the first vehicle is controlled to generate electromagnetic force according to the electromagnetic force parameters.

[0134] In this embodiment, based on the direction of the impending collision, if the relative direction is that the second vehicle is in front of the first vehicle, the electromagnetic device at the front of the first vehicle is controlled to generate electromagnetic force according to the electromagnetic force parameters; if the relative direction is that the second vehicle is behind the first vehicle, the electromagnetic device at the rear of the first vehicle is controlled to generate electromagnetic force according to the electromagnetic force parameters.

[0135] The following is a detailed introduction and explanation of the solutions in the embodiments of the present invention, with reference to specific application examples.

[0136] Reference Figure 9 After the user gets in the car and starts the vehicle, the system identifies the current vehicle status and driving scenario: it identifies and judges whether the vehicle is currently in a driving state and the working conditions and environment in the driving state, as a condition for judging that the vehicle is about to have an emergency collision.

[0137] Specifically, vehicle status confirmation includes confirming vehicle gear information and vehicle speed information. If the current gear is other than parking or neutral and the vehicle speed is not zero, the vehicle is considered to be in a driving state; otherwise, the vehicle is considered to be in a non-driving state, and no environmental status confirmation is made.

[0138] Operating condition confirmation includes determining the vehicle's operating status during operation based on the vehicle's current and historical speed, gear, engine speed, brake pedal opening, accelerator pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle, and steering wheel angular velocity. This includes driving conditions such as starting, accelerating, constant speed, deceleration, turning, going uphill or downhill, and parking, as well as operating conditions under control modes such as gear shifting, coasting, braking, throttle speed control, steering, and reversing.

[0139] When the system determines that the current vehicle is in a driving state, it initiates environmental status confirmation, including using cameras to identify whether there are vehicles driving in front and behind the vehicle, determining the relative speed and distance to the vehicle in front or behind, and connecting to the network in real time to obtain environmental information such as current geographical location, road surface temperature, road roughness, road longitudinal slope, road lateral slope, air temperature, humidity, weather (rainfall, wind force, direction, etc.), altitude, and atmospheric pressure, as well as the real-time traffic status of the current road segment, whether it is a collision-prone area, whether other vehicles have recently been involved in collisions, the number of collisions, and the type of collision.

[0140] Based on the above information, it can be determined whether the vehicle is currently in a safe driving state, which can serve as a basis for judging whether the user has engaged in an emergency collision.

[0141] According to the vehicle collision avoidance method provided in this application, an emergency collision recognition system trained with a large amount of historical accident data using artificial intelligence algorithms such as deep learning can subdivide the environment and operating conditions. Based on the input vehicle status, operating conditions, environmental information, and historical accident data, the emergency collision recognition system outputs whether to issue a warning and activate the collision avoidance system. The emergency collision recognition system can identify the characteristic information of different environments and operating conditions to select an appropriate collision warning model, thereby selecting the input weights and changing the timing of the warning and emergency collision avoidance system activation. For example, in operating conditions such as starting, acceleration, turning, and downhill driving, as well as when the road surface is slippery or the wind force and direction will accelerate the collision, the emergency collision recognition system will adjust the parameter weights and issue an early warning. When reversing, the system mainly considers the distance and speed parameters of the vehicle behind, setting relatively large weights; when moving forward, the system mainly considers the distance and speed parameters of the vehicle in front, setting relatively large weights; in turning situations, the system considers the impact of turning on the braking direction and speed parameters. Based on the current environmental information and operating conditions, the emergency collision recognition system automatically adjusts the weights of different inputs and identifies whether the vehicle is about to engage in an emergency collision based on the current vehicle status.

[0142] When the system detects an impending emergency collision, it issues a warning and activates the emergency collision avoidance system. This system uses numerous real-world collision examples as training input, enabling it to learn how to set electromagnetic force parameters based on vehicle speed, distance to other vehicles, relative speed, and environmental information. It then adjusts these parameters in real-time according to actual conditions, reducing the likelihood of a collision and maintaining relative stability. When activated, the system sets the electromagnetic force based on current speed, distance to other vehicles, relative speed, and environmental information. As the distance to the vehicle in front or behind gradually approaches a safe level, the electromagnetic device on the vehicle generates an electromagnetic force that repels the vehicle, achieving smooth deceleration.

[0143] Reference Figure 10 This application also provides a vehicle collision avoidance system that can implement the above-described vehicle collision avoidance method. The system includes:

[0144] The first module is used to obtain current environmental information and first driving information of the first vehicle, wherein the first driving information includes the geographical location and first dynamic data of the first vehicle.

[0145] The second module is used to acquire the second dynamic data of the second vehicle, wherein the second vehicle and the first vehicle are located in the same lane, and the second dynamic data includes the relative position and relative speed of the second vehicle and the first vehicle.

[0146] The third module is used to determine the vehicle operating condition of the first vehicle based on the first dynamic data.

[0147] The fourth module is used to determine whether a warning condition has occurred based on the current environmental information, the initial driving information, and the vehicle's operating condition.

[0148] The fifth module is used to determine whether an emergency collision condition has occurred when a warning condition is triggered.

[0149] The sixth module is used to input the current environmental information, the first dynamic data, the second dynamic data, and the vehicle operating conditions into the collision avoidance model to obtain electromagnetic force parameters when an emergency collision occurs.

[0150] The seventh module is used to control the electromagnetic device of the first vehicle to generate electromagnetic force according to the electromagnetic force parameters, so that the first vehicle and the second vehicle repel each other.

[0151] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0152] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described vehicle collision avoidance method. This electronic device can be any intelligent terminal, including an in-vehicle computer.

[0153] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0154] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0155] The processor 111 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0156] The memory 112 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 112 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 112 and is called and executed by the processor 111 to execute the vehicle collision avoidance method of the embodiments of this application.

[0157] Input / output interface 113 is used to implement information input and output.

[0158] The communication interface 114 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0159] Bus 115 transmits information between various components of the device, such as processor 111, memory 112, input / output interface 113, and communication interface 114.

[0160] The processor 111, memory 112, input / output interface 113 and communication interface 114 are connected to each other within the device via bus 115.

[0161] This application also provides a vehicle that includes an electric drive assembly for the aforementioned vehicle collision avoidance system or electronic equipment. Specifically, the vehicle can be a private car, such as a sedan, SUV, MPV, or pickup truck. The vehicle can also be a commercial vehicle, such as a van, bus, small truck, or large semi-trailer. The vehicle can be a gasoline-powered vehicle or a new energy vehicle. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle.

[0162] It is understood that the content of the above method embodiments is applicable to this vehicle embodiment. The specific functions implemented in this vehicle embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0163] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described vehicle collision avoidance method.

[0164] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0165] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0166] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0167] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0168] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0170] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0171] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A vehicle anti-collision method, characterized by, The method comprises the following steps: obtaining current environment information and first driving information of a first vehicle, wherein the first driving information comprises geographical position and first dynamic data of the first vehicle; obtaining second dynamic data of a second vehicle, wherein the second vehicle is located in the same lane as the first vehicle, and the second dynamic data comprises relative position and relative speed of the second vehicle and the first vehicle; determining a vehicle working condition of the first vehicle according to the first dynamic data; judging whether a pre-warning condition occurs according to the current environment information, the first driving information and the vehicle working condition; when the pre-warning condition occurs, judging whether an emergency collision condition occurs; when the emergency collision condition occurs, inputting the current environment information, the first dynamic data, the second dynamic data and the vehicle working condition into a collision avoidance model to obtain electromagnetic force parameters; controlling an electromagnetic device of the first vehicle to generate electromagnetic force according to the electromagnetic force parameters, so that the first vehicle repels the second vehicle; the judging whether the pre-warning condition occurs according to the current environment information, the first driving information and the vehicle working condition comprises the following steps: determining a corresponding collision pre-warning model according to the current environment information and the vehicle working condition, wherein the input layer of the collision pre-warning model is pre-set with weights of each input parameter; inputting each parameter in the first dynamic data as an input parameter into the collision pre-warning model to obtain a collision pre-warning result, wherein the collision pre-warning result comprises a pre-warning starting time; judging whether the pre-warning condition occurs according to the pre-warning starting time; the collision avoidance model is obtained by the following steps: collecting real vehicle collision data and labeling the real vehicle collision data, wherein the label of the real vehicle collision data is electromagnetic force parameters; forming a training data set according to the labeled multiple real vehicle collision data, wherein the forming a training data set according to the labeled multiple real vehicle collision data comprises: dividing the labeled multiple real vehicle collision data into a training set, a verification set and a test set to obtain the training data set, wherein the training set is used to train the collision avoidance model, so that the collision avoidance model learns the mapping relationship between vehicle speed, distance between front and rear vehicles, relative speed, environment information and electromagnetic force parameters; the verification set is used to evaluate the performance of the collision avoidance model in the training process, so as to adjust the training parameters or optimize the model structure; the test set is used to evaluate the generalization ability, i.e. the prediction ability of new data, after the model training is completed; The training data set is input into the initialized anti-collision model for training to obtain a trained anti-collision model.

2. The method of claim 1, wherein, The first driving information of the first vehicle is obtained by the following steps: The speed, gear, engine speed, brake pedal opening, throttle pedal opening, longitudinal acceleration, lateral acceleration, yaw rate, steering wheel angle and steering wheel angular velocity of the first vehicle are collected by the sensing assembly to obtain first dynamic data; The geographic position of the first vehicle is obtained by the positioning system, and the geographic position and the first dynamic data are determined as the first driving information; The road surface temperature, road surface roughness, road surface slope, air temperature, humidity, altitude, air pressure, rainfall and wind force of the current road section are obtained according to the geographic position to obtain the current environment information.

3. The method of claim 2, wherein, The second dynamic data of the second vehicle is obtained by the following steps: An image sequence around the first vehicle is obtained by the camera; The second vehicle located on the same lane is identified according to the image sequence; The relative position and relative speed of the second vehicle relative to the first vehicle are calculated according to the image sequence to obtain the second dynamic data, wherein the relative position includes relative direction and relative distance.

4. The method of claim 2, wherein, The vehicle working condition of the first vehicle is determined according to the first dynamic data by the following steps: The vehicle state of the first vehicle is determined according to the speed and the gear; When the vehicle state is the driving state, the vehicle working condition of the first vehicle is determined according to the speed, the gear, the engine speed, the brake pedal opening, the throttle pedal opening, the longitudinal acceleration, the lateral acceleration, the yaw rate, the steering wheel angle and the steering wheel angular velocity.

5. The method of claim 3, wherein, The judgment of whether the emergency collision condition occurs includes the following steps: The safety distance is obtained according to the speed, the relative speed and the longitudinal acceleration, and the first sub-condition is judged by comparing the safety distance and the relative distance; The second sub-condition is judged according to the throttle pedal opening; The third sub-condition is judged according to the geographic position; When one of the first sub-condition, the second sub-condition and the third sub-condition is established, the emergency collision condition occurs, wherein the first sub-condition is that the safety distance is greater than the relative distance, the second sub-condition is that the throttle pedal opening is not equal to zero, and the third sub-condition is that an accident occurs on the current driving road section and the first vehicle is approaching the accident site.

6. The method of claim 3, wherein, The first vehicle's electromagnetic device is controlled according to the electromagnetic force parameter to generate electromagnetic force, including the following steps: When the relative direction is that the second vehicle is in front of the first vehicle, the electromagnetic device at the front of the first vehicle is controlled according to the electromagnetic force parameter to generate electromagnetic force; Or, when the relative direction is that the second vehicle is behind the first vehicle, the electromagnetic device at the rear of the first vehicle is controlled according to the electromagnetic force parameter to generate electromagnetic force.

7. A vehicle collision avoidance system characterized by, The vehicle anti-collision system is used to implement the method of any one of claims 1 to 6, and the system comprises: A first module is configured to acquire current environment information and first driving information of the first vehicle, wherein the first driving information comprises geographical position and first dynamic data of the first vehicle; A second module is configured to acquire second dynamic data of a second vehicle, wherein the second vehicle is in the same lane as the first vehicle, and the second dynamic data comprises relative position and relative speed of the second vehicle and the first vehicle; A third module is configured to determine vehicle working condition of the first vehicle according to the first dynamic data; A fourth module is configured to determine whether a pre-warning condition occurs according to the current environment information, the first driving information and the vehicle working condition; A fifth module is configured to determine whether an emergency collision condition occurs when the pre-warning condition occurs; A sixth module is configured to input the current environment information, the first dynamic data, the second dynamic data and the vehicle working condition into an anti-collision model to obtain an electromagnetic force parameter when the emergency collision condition occurs; A seventh module is configured to control the first vehicle's electromagnetic device according to the electromagnetic force parameter to generate electromagnetic force, so that the first vehicle repels the second vehicle.

8. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of any one of claims 1 to 6 when executing the computer program.

9. A vehicle characterized by comprising: The vehicle comprises the vehicle anti-collision system of claim 7 or the electronic device of claim 8.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the method of any one of claims 1 to 6. The computer program is executed by the processor to implement the method of any one of claims 1 to 6.

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