Automobile automatic emergency braking system based on fusion data and control method thereof

Abstract

The invention discloses an automobile automatic emergency braking system based on fusion data and a control method thereof, and belongs to the technical field of automobile braking control, and the control method specifically comprises the steps that driving environment information and automobile internal data are obtained through a sensor and an internet of things method, and the data are fused to obtain an accurate automobile state; predicting a dangerous scene by using a hierarchical early warning model and a deep learning algorithm, and giving early warning information of different levels according to a prediction result; when a collision or emergency braking early warning is received, the system adopts a target identification strategy to evaluate the danger degree and sends an emergency braking instruction; multi-vehicle cooperative braking is achieved through vehicle-to-vehicle communication, and automatic emergency braking is conducted through a self-adaptive control strategy; the system receives the feedback of the sensor, optimizes the control strategy in real time, and feeds back the control strategy to the traffic management system through the communication from the vehicle to the infrastructure, thereby improving the driving safety and the traffic efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile brake control, and in particular to an automobile automatic emergency brake system based on fusion data and a control method thereof. Background Art

[0002] With the development of economy, the increase of national income and the improvement of road traffic environment, cars have gradually become the preferred means of transportation for people's daily travel and are becoming common consumer goods for thousands of households. With the continuous increase in the number of cars, people have higher and higher requirements for the convenience of car operation, driving comfort and driving safety. Many electric vehicles are equipped with automatic emergency braking function to prevent dangerous vehicle collisions.

[0003] For example, the patent with publication number CN111845668A discloses an automatic emergency braking method for electric vehicles, including: the vehicle controller determines whether the braking torque T is less than or equal to the motor braking torque value calibrated when the vehicle is initialized; if so, when the remaining power of the power battery is detected to be less than 80% of the calibrated power, and there is no fault prohibiting charging, and the brake pedal opening is detected to be zero, the vehicle controller sends a braking torque request value T to the motor to implement motor braking; if not, the vehicle controller sends a torque request signal to the electronic stability module to directly implement hydraulic braking. The above technical solution is simple, practical, safe, reliable and low-cost; when the vehicle is automatically emergency braked, the motor braking method is used to recover energy and charge the power battery, which increases the battery life to a certain extent, reduces the wear of the ESC reversing valve, and extends its service life.

[0004] The above existing technologies all have the following problems: 1) lack of a comprehensive warning model; 2) difficulty in system maintenance; 3) lack of flexibility to adapt to different driving conditions and emergency situations, resulting in unsatisfactory or insufficient braking effects; 4) lack of the ability to coordinate braking with other vehicles. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes an automobile automatic emergency braking system based on fused data and a control method thereof, which obtains driving environment information and vehicle internal data through sensors and Internet of Things methods, and fuses these data to obtain accurate automobile status; uses a hierarchical warning model and a deep learning algorithm to predict dangerous scenarios, and gives different levels of warning information based on the prediction results; when receiving a collision or emergency braking warning, the system uses a target recognition strategy to assess the degree of danger and sends an emergency braking command; through vehicle-to-vehicle communication, multi-vehicle collaborative braking is achieved, and automatic emergency braking is performed using an adaptive control strategy; the system receives sensor feedback, optimizes the control strategy in real time, and feeds back to the traffic management system through vehicle-to-infrastructure communication, thereby improving driving safety and traffic efficiency.

[0006] To achieve the above object, the present invention provides the following technical solutions: The automobile automatic emergency braking system based on fusion data includes: a decision module and a control module; The decision module includes a target recognition unit and a braking decision unit; the target recognition unit is configured with a target recognition strategy based on contour and segmentation, and the target recognition strategy based on contour and segmentation is used to recognize and classify objects in the captured visual information, and distinguish pedestrians, vehicles and obstacles; the braking decision unit is configured with a priority strategy, and the priority strategy is used to set different priorities according to the urgency and importance of different factors and send an emergency braking instruction; The control module includes a braking control unit and a power control unit; the braking control unit is used to implement the emergency braking command issued by the decision module; the power control unit is configured with a rapid coordination control strategy, which includes responding in a short time when an emergency braking command is received and coordinating the engine and transmission operations.

[0007] The vehicle automatic emergency braking control method based on fusion data includes: Step S1: Acquire driving environment information and vehicle internal data through sensors and Internet of Things methods, and fuse the driving environment information and vehicle internal data from different sensors to obtain fused vehicle data; Step S2: construct a hierarchical warning model and a dangerous scene prediction model based on a deep learning algorithm, use the fused car data to predict the existing dangerous scenes, and according to the prediction results, combine the real-time traffic information to divide the warning level into safe driving level, collision warning level, and emergency braking level, and give the corresponding warning information; Step S3: If a warning message of a collision warning level or an emergency braking level is received, a target recognition strategy based on contour and segmentation is used to identify and classify objects in the captured visual information, distinguish between pedestrians, vehicles and obstacles, and evaluate the degree of danger based on the recognition result and the preset priority strategy. If the degree of danger reaches a preset threshold, an emergency braking command is sent; Step S4: according to the warning information and the vehicle dynamics model, an adaptive control strategy is formulated, multi-vehicle coordinated braking is performed through vehicle-to-vehicle communication, and the adaptive control strategy is sent to the brake in the braking control unit to perform automatic emergency braking. At the same time, when an emergency braking command is received, a fast coordinated control strategy is used to reduce the vehicle speed; Step S5: Receive feedback signals from sensors, monitor the braking effect and driving status in real time, and optimize and adjust the adaptive control strategy using machine learning algorithms based on the braking effect and driving status. Feedback the optimized adaptive control strategy to the traffic management system through vehicle-to-infrastructure communication.

[0008] Specifically, the specific steps of step S2 include: S2.1: Set the warning level to safe driving level, collision warning level, and emergency braking level. Based on the fused car data, the warning thresholds for each level are set according to the vehicle driving status, relative distance, and relative speed data. , , ; S2.2: According to the preset warning threshold , , , determine the warning level to which the current driving state belongs, and obtain a graded warning model based on the determination result; S2.3: Obtain a pre-trained deep learning prediction model, and at the same time, extract features from the fused vehicle data to obtain dangerous scene data, pre-process the dangerous scene data, and input the pre-processed dangerous scene data into the pre-trained deep learning model to obtain a dangerous scene prediction model; S2.4: Based on the prediction results output by the dangerous scenario prediction model and combined with the graded warning model, the current warning level is determined, and the corresponding warning information is output through sound and light alarm according to the warning level.

[0009] Specifically, the specific steps of step S3 include: S3.1: receiving warning information and extracting the warning level, determining whether the warning level is a collision warning level or an emergency braking level, and if so, capturing real-time image frames through a camera on the vehicle, and preprocessing the captured image frames; S3.2: Use the improved contour detection algorithm to extract the object contour in the image frame and obtain the contour image. The formula is: ; in, Represents an image frame f The second-order derivative of Represented in the image frame three-dimensional coordinate system x , y , z Direction i The coordinates of the dimensions, n Indicates the number of dimensions, Indicates i The weight of the contour image in dimensions, Indicates i The scale characteristics of the dimensions, Represents partial derivative.

[0010] Specifically, the specific steps of step S3 also include: S3.3: Set the second-order derivative threshold of the image frame to , obtain the threshold comparison result; like , then the coordinates The corresponding pixel belongs to the contour area; like , then the coordinates The corresponding pixel belongs to the background; S3.4: According to the threshold comparison result, the contour image is converted into a binary contour image, and the binary contour image is divided into m Class object, obtain the segmentation result of the binary contour image; S3.5: Extract features from the segmented objects and match the extracted features with the pre-trained random forest classifier to identify pedestrians, vehicles and obstacles in the picture.

[0011] Specifically, the specific steps of step S3 also include: S3.6: Set the priority strategy, according to the recognition results in S3.5, obtain Every The number of class objects , and assign a priority weight to each type of object according to the preset priority strategy; S3.7: For each object in each class, measure the distance from the object to the vehicle, normalize the distance, and calculate a danger score based on its type, priority weight, and normalized distance, using the formula: ; in, represents the total risk score, M represents the k the number of class objects, Indicates k Class object j The weight of an object, D represents the normalized distance, m Represents the object category, k≤m, j≤M;

[0012] S3.8: Set the risk score threshold H, if , emergency braking is triggered and an emergency braking command is sent to the vehicle braking system, and the vehicle braking system performs emergency braking according to the command.

[0013] Specifically, the priority strategy in S3.6 includes: ; in, Indicates the level of high-speed vehicles, Indicates the level of slow-moving pedestrians, Indicates the level of large vehicles and buildings, Indicates the level of small vehicles and pedestrians, Indicates pedestrians and vehicles moving in the same or opposite direction as the vehicle, and the distance between vehicles Meter level, Indicates the distance between vehicles Meters and in the direction of vehicle movement The level of pedestrians and vehicles at different angles, Indicates the distance between pedestrians and vehicles Meter level, Indicates the level of accelerating pedestrians and vehicles, Indicates the level of pedestrians or vehicles that are slowing down and moving at a constant speed. Indicates the level of vehicles on the highway, Indicates the level of pedestrians on the highway, Indicates the level of pedestrians on the sidewalk, Indicates the level of vehicles on the sidewalk.

[0014] Specifically, an electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a method for controlling an automatic emergency braking of an automobile based on fused data when executing the computer program.

[0015] Specifically, a computer-readable storage medium stores computer instructions, which, when executed, execute the steps of a method for controlling an automatic emergency braking of an automobile based on fusion data.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention proposes an automobile automatic emergency braking system based on fusion data, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0017] 2. The present invention proposes an automobile automatic emergency braking control method based on fused data, which obtains driving environment information and vehicle internal data in real time through sensors and Internet of Things methods, and predicts dangerous scenarios in combination with deep learning algorithms. It can give early warning when potential collision risks occur, reduce the driver's reaction time, and thus effectively avoid or mitigate collision accidents; when receiving collision warning level or emergency braking level warning information, the system can quickly identify the target and assess the degree of danger, and automatically send an emergency braking command when the degree of danger reaches a preset threshold, ensuring that the vehicle takes braking measures in the shortest time and reducing the possibility of accidents.

[0018] 3. The present invention proposes an automatic emergency braking control method for automobiles based on fusion data, which adopts an adaptive control strategy, combines the vehicle dynamics model and real-time traffic information, and can formulate a more accurate braking plan to avoid sudden shaking or excessive deceleration of the vehicle during emergency braking, and improve the comfort and stability of the braking process; reduce the vehicle speed through a rapid coordination control strategy, control the vehicle speed more quickly in an emergency, and reduce collision accidents caused by untimely braking or insufficient braking force; realize multi-vehicle coordinated braking through vehicle-to-vehicle communication, which can improve the safety and efficiency of road traffic. In an emergency, multi-vehicle coordinated braking can avoid or reduce chain collision accidents caused by inconsistent braking between vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is an architecture diagram of the automobile automatic emergency braking system based on fusion data of the present invention; Figure 2 A schematic diagram of an automobile automatic emergency braking control method based on fusion data of the present invention; Figure 3 This is a working flow chart of the automobile automatic emergency braking control method based on fusion data of the present invention; Figure 4 The present invention is a vehicle emergency braking flow chart of the vehicle automatic emergency braking control method based on fusion data. DETAILED DESCRIPTION

[0020] Example 1

[0021] See also Figure 1 ,An embodiment provided by the present invention: An automobile automatic emergency braking system based on fusion data, comprising: a perception module, a decision module, a control module, a communication module, and an interaction module; The perception module is used to collect information about the vehicle's surroundings, including vehicles ahead, pedestrians, obstacles, and road conditions; The decision module receives data from the perception module and uses advanced algorithms to analyze whether there is a risk of collision and decide whether to initiate emergency braking. The control module is used to execute the instructions issued by the decision module and control the operation of the braking system; The communication module is responsible for the communication between the modules within the system and the communication with the external system vehicle intelligent system; The interaction module is used for interaction between the system and the driver, such as displaying system status and receiving driver instructions.

[0022] The perception module includes: sensor unit, ranging unit, and data fusion unit; A sensor unit for collecting environmental information around the vehicle; The ranging unit is used to calculate the distance between the vehicle and the obstacle ahead through radar; The data fusion unit is used to integrate data from different sensors to improve the accuracy and robustness of the data.

[0023] The decision module includes a target recognition unit and a braking decision unit. The target recognition unit is configured with a target recognition strategy based on contours and segmentation. The target recognition strategy based on contours and segmentation is used to identify and classify objects in the captured visual information and distinguish between pedestrians, vehicles and obstacles. The braking decision unit is configured with a priority strategy. The priority strategy is used to set different priorities according to the urgency and importance of different factors and send emergency braking instructions. This decision takes into account multiple factors such as vehicle speed, distance from obstacles, and the nature of obstacles. Once it is determined that there is a risk of collision, a braking instruction is sent to the control module. The decision module also includes a data processing unit; the data processing unit is used to receive data from the perception module, perform in-depth analysis and processing on the data, and use machine learning algorithms to identify the existing collision risks and evaluate the consequences of different response strategies; The control module includes a brake control unit and a power control unit; the brake control unit is used to implement the emergency braking command issued by the decision module, control the vehicle's hydraulic or electronic braking system, and perform emergency braking at the fastest speed and maximum braking force to avoid or mitigate collisions; the power control unit is configured with a rapid coordination control strategy, which includes responding in a short time when an emergency braking command is received, and coordinating the adjustment of engine and transmission operations.

[0024] The communication module includes: an on-board communication unit and a roadside communication unit; The vehicle communication unit is responsible for communicating with nearby vehicles and infrastructure to obtain additional environmental information and transmit the vehicle's status information; The roadside communication unit is responsible for communicating with the traffic management center, receiving traffic status information, and also sending emergency braking events.

[0025] Example 2

[0026] See also Figure 2-Figure 4 Another embodiment provided by the present invention is a method for controlling an automatic emergency brake of an automobile based on fusion data, comprising: Step S1: Acquire driving environment information and vehicle internal data through sensors and Internet of Things methods, and fuse the driving environment information and vehicle internal data from different sensors to obtain fused vehicle data; Among them, driving environment information includes: road conditions ahead, obstacle locations, and vehicle speed; vehicle internal data includes: vehicle speed, engine status, and brake system status.

[0027] Step S2: construct a hierarchical warning model and a dangerous scene prediction model based on a deep learning algorithm, use the fused car data to predict the existing dangerous scenes, and according to the prediction results, combine the real-time traffic information to divide the warning level into safe driving level, collision warning level, and emergency braking level, and give the corresponding warning information; Dangerous scenarios include: pedestrians crossing, sudden deceleration of vehicles ahead, and road obstacles; Real-time traffic information includes: traffic flow, signal light status, road construction conditions; Warning information includes: road conditions, status of other vehicles, and obstacle information.

[0028] Step S3: If a warning message of a collision warning level or an emergency braking level is received, a target recognition strategy based on contour and segmentation is used to identify and classify objects in the captured visual information, distinguish between pedestrians, vehicles and obstacles, and evaluate the degree of danger based on the recognition result and the preset priority strategy. If the degree of danger reaches a preset threshold, an emergency braking command is sent; Step S4: according to the warning information and the vehicle dynamics model, an adaptive control strategy is formulated, multi-vehicle coordinated braking is performed through vehicle-to-vehicle communication, and the adaptive control strategy is sent to the brake in the braking control unit to perform automatic emergency braking. At the same time, when an emergency braking command is received, a fast coordinated control strategy is used to reduce the vehicle speed; Among them, the adaptive control strategy includes: braking timing, braking force distribution, and braking time control; the rapid coordination control strategy includes: adjusting engine torque and activating the electronic stability program.

[0029] Furthermore, the specific steps of step S4 include: (1) Receive and input warning information, vehicle status information, and vehicle dynamics model parameters; (2) Analyze, classify and calculate the input data to determine whether emergency braking measures are needed; (3) Formulate adaptive control strategies and rapid coordination control strategies based on the analysis results; (4) Convert the adaptive control strategy into specific braking instructions and send them to the brake through communication. The control strategy includes detailed instructions on how to adjust the braking force according to the current vehicle status, road conditions and predicted dangerous scenarios. These instructions are sent to the vehicle MCU through the vehicle controller, and the MCU further controls the output torque of the main motor and auxiliary motor. At the same time, the vehicle controller also directly controls the electronic hydraulic brake to output the corresponding braking torque; (5) The brake executes instructions to increase the friction between the brake disc and the brake pad to generate braking deceleration, slowing down or stopping the vehicle, while monitoring the system status and braking effect; (6) Optimize and adjust the adaptive control strategy based on real-time data during braking.

[0030] Step S5: Receive feedback signals from sensors, monitor the braking effect and driving status in real time, and optimize and adjust the adaptive control strategy using machine learning algorithms based on the braking effect and driving status. Feedback the optimized adaptive control strategy to the traffic management system through vehicle-to-infrastructure communication.

[0031] Among them, driving status includes: vehicle speed change, braking distance, and vehicle body posture.

[0032] The specific steps of step S2 include: S2.1: Set the warning level to safe driving level, collision warning level, and emergency braking level. Based on the fused car data, the warning thresholds for each level are set according to the vehicle driving status, relative distance, and relative speed data. , , ; S2.2: According to the preset warning threshold , , , determine the warning level to which the current driving state belongs, and obtain a graded warning model based on the determination result; S2.3: Obtain a pre-trained deep learning prediction model, and at the same time, extract features from the fused vehicle data to obtain dangerous scene data, pre-process the dangerous scene data, and input the pre-processed dangerous scene data into the pre-trained deep learning model to obtain a dangerous scene prediction model, wherein the method for obtaining the pre-trained deep learning prediction model is the prior art content in this field, and is not the inventive solution of the present application, and will not be elaborated here; S2.4: Based on the prediction results output by the dangerous scenario prediction model and combined with the graded warning model, the current warning level is determined, and the corresponding warning information is output through sound and light alarm according to the warning level.

[0033] The specific steps of step S3 include: S3.1: receiving warning information and extracting the warning level, determining whether the warning level is a collision warning level or an emergency braking level, and if so, capturing real-time image frames through a camera on the vehicle, and preprocessing the captured image frames; S3.2: Use the improved contour detection algorithm to extract the object contour in the image frame and obtain the contour image. The formula is: ; in, Represents an image frame f The second-order derivative of Represented in the image frame three-dimensional coordinate system x , y , z Direction i The coordinates of the dimensions, n Indicates the number of dimensions, Indicates i The weight of the contour image in dimensions, Indicates i The scale characteristics of the dimensions, Represents partial derivative.

[0034] The specific steps of step S3 also include: S3.3: Set the second-order derivative threshold of the image frame to , obtain the threshold comparison result; like , then the coordinates The corresponding pixel belongs to the contour area; like , then the coordinates The corresponding pixel belongs to the background; Among them, the image frame second-order derivative threshold It can be adjusted according to the specific image and the contour details that need to be extracted. If the threshold is set too high, important contour information will be ignored; if the threshold is set too low, too much noise or unnecessary details will be introduced.

[0035] S3.4: According to the threshold comparison result, the contour image is converted into a binary contour image, and the binary contour image is divided into m Classify the object, and obtain the segmentation result of the binary contour image, wherein the clustering method is the prior art content in this field, which is not the inventive solution of the present application and will not be described in detail here; When converting a contour image to a binary contour image, the values ​​of pixels belonging to the contour area are set to 1 (or 255, depending on the data type and representation of the image), and the values ​​of pixels belonging to the background area are set to 0.

[0036] S3.5: Extract features from the segmented objects and match the extracted features with the pre-trained random forest classifier to identify pedestrians, vehicles and obstacles in the picture.

[0037] The specific steps of step S3 also include: S3.6: Set the priority strategy, according to the recognition results in S3.5, obtain Every The number of class objects , and assign a priority weight to each type of object according to the preset priority strategy; S3.7: For each object in each class, measure the distance from the object to the vehicle, normalize the distance, and calculate a danger score based on its type, priority weight, and normalized distance, using the formula: ; in, represents the total risk score, M represents the k the number of class objects, Indicates k Class object j The weight of an object, D represents the normalized distance, m Indicates the object category, k≤m; j≤M;

[0038] S3.8: Set the risk score threshold H, if , emergency braking is triggered and an emergency braking command is sent to the vehicle braking system, and the vehicle braking system performs emergency braking according to the command.

[0039] The priority strategies in S3.6 include: ; in, Indicates the level of high-speed vehicles, Indicates the level of slow-moving pedestrians, Indicates the level of large vehicles and buildings, Indicates the level of small vehicles and pedestrians, Indicates pedestrians and vehicles moving in the same or opposite direction as the vehicle, and the distance between vehicles Meter level, Indicates the distance between vehicles Meters and in the direction of vehicle movement The level of pedestrians and vehicles at different angles, Indicates the distance between pedestrians and vehicles Meter level, Indicates the level of accelerating pedestrians and vehicles, Indicates the level of pedestrians or vehicles that are slowing down and moving at a constant speed. Indicates the level of vehicles on the highway, Indicates the level of pedestrians on the highway, Indicates the level of pedestrians on the sidewalk, Indicates the level of vehicles on the sidewalk.

[0040] It can be further summarized as follows: fast-moving objects have higher priority than slow-moving objects because they may cause danger in a shorter time; large obstacles have higher priority than small obstacles because they may take up more road space or cause greater damage; if the objects are moving in the same or opposite direction as the vehicle and are closer, they have higher priority than objects at a certain angle to the direction of movement of the vehicle; objects that are accelerating have higher priority than objects that are decelerating or moving at a constant speed because they may approach the vehicle faster; on highways, vehicles may have higher priority, while on sidewalks, pedestrians may have higher priority.

[0041] Example 3 An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a vehicle automatic emergency braking control method based on fusion data when executing the computer program.

[0042] A computer-readable storage medium stores computer instructions, which, when executed, execute the steps of a method for controlling an automatic emergency brake of an automobile based on fusion data.

[0043] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. The automatic emergency braking system for automobiles based on fusion data is characterized by: include: Decision module and control module; The decision module includes a target recognition unit and a braking decision unit; The target recognition unit is configured with a target recognition strategy based on contour and segmentation, and the target recognition strategy based on contour and segmentation is used to recognize and classify objects in the captured visual information and distinguish between pedestrians, vehicles and obstacles; The braking decision unit is configured with a priority strategy, which is used to set different priorities according to the urgency and importance of different factors and send an emergency braking instruction; The control module includes a braking control unit and a power control unit; the braking control unit is used to implement the emergency braking command issued by the decision module; the power control unit is configured with a rapid coordination control strategy, which includes responding in a short time when an emergency braking command is received and coordinating the engine and transmission operations.

2. A vehicle automatic emergency braking control method based on fusion data, implemented based on the vehicle automatic emergency braking system based on fusion data according to claim 1, characterized in that: include: Step S1: Acquire driving environment information and vehicle internal data through sensors and Internet of Things methods, and fuse the driving environment information and vehicle internal data from different sensors to obtain fused vehicle data; Step S2: construct a hierarchical warning model and a dangerous scene prediction model based on a deep learning algorithm, use the fused car data to predict the existing dangerous scenes, and according to the prediction results, combine the real-time traffic information to divide the warning level into safe driving level, collision warning level, and emergency braking level, and give the corresponding warning information; Step S3: If a warning message of a collision warning level or an emergency braking level is received, a target recognition strategy based on contour and segmentation is used to identify and classify objects in the captured visual information, distinguish between pedestrians, vehicles and obstacles, and evaluate the degree of danger based on the recognition result and the preset priority strategy. If the degree of danger reaches a preset threshold, an emergency braking command is sent; Step S4: according to the warning information and the vehicle dynamics model, an adaptive control strategy is formulated, multi-vehicle coordinated braking is performed through vehicle-to-vehicle communication, and the adaptive control strategy is sent to the brake in the braking control unit to perform automatic emergency braking. At the same time, when an emergency braking command is received, a fast coordinated control strategy is used to reduce the vehicle speed; Step S5: Receive feedback signals from sensors, monitor the braking effect and driving status in real time, and optimize and adjust the adaptive control strategy using machine learning algorithms based on the braking effect and driving status. Feedback the optimized adaptive control strategy to the traffic management system through vehicle-to-infrastructure communication.

3. The method for controlling the automatic emergency braking of an automobile based on fusion data as claimed in claim 2, characterized in that: The specific steps of step S2 include: S2.1: Set the warning level to safe driving level, collision warning level, and emergency braking level. Based on the fused car data, the warning thresholds for each level are set according to the vehicle driving status, relative distance, and relative speed data. , , ; S2.2: According to the preset warning threshold , , , determine the warning level to which the current driving state belongs, and obtain a graded warning model based on the determination result; S2.3: Obtain a pre-trained deep learning prediction model, and at the same time, extract features from the fused vehicle data to obtain dangerous scene data, pre-process the dangerous scene data, and input the pre-processed dangerous scene data into the pre-trained deep learning model to obtain a dangerous scene prediction model; S2.4: Based on the prediction results output by the dangerous scenario prediction model and combined with the graded warning model, the current warning level is determined, and the corresponding warning information is output through sound and light alarm according to the warning level.

4. The method for controlling the automatic emergency braking of an automobile based on fusion data as claimed in claim 3, characterized in that: The specific steps of step S3 include: S3.1: receiving warning information and extracting the warning level, determining whether the warning level is a collision warning level or an emergency braking level, and if so, capturing real-time image frames through a camera on the vehicle, and preprocessing the captured image frames; S3.2: Use the improved contour detection algorithm to extract the object contour in the image frame and obtain the contour image. The formula is: ； in, Represents an image frame f The second-order derivative of Represented in the image frame three-dimensional coordinate system x , y , z Direction i The coordinates of the dimensions, n Indicates the number of dimensions, Indicates i The weight of the contour image in dimensions, Indicates i The scale characteristics of the dimensions, Represents partial derivative.

5. The method for controlling the automatic emergency braking of an automobile based on fusion data as claimed in claim 4, characterized in that: The specific steps of step S3 also include: S3.3: Set the second-order derivative threshold of the image frame to , obtain the threshold comparison result; like , then the coordinates The corresponding pixel belongs to the contour area; like , then the coordinates The corresponding pixel belongs to the background; S3.4: According to the threshold comparison result, the contour image is converted into a binary contour image, and the binary contour image is divided into m Class object, obtain the segmentation result of the binary contour image; S3.5: Extract features from the segmented objects and match the extracted features with the pre-trained random forest classifier to identify pedestrians, vehicles and obstacles in the picture.

6. The method for controlling the automatic emergency braking of an automobile based on fusion data as claimed in claim 5, characterized in that: The specific steps of step S3 also include: S3.6: Set the priority strategy, according to the recognition results in S3.5, obtain Every The number of class objects , and assign a priority weight to each type of object according to the preset priority strategy; S3.7: For each object in each class, measure the distance from the object to the vehicle, normalize the distance, and calculate a danger score based on its type, priority weight, and normalized distance, using the formula: ； in, represents the total risk score, M represents the k the number of class objects, Indicates k Class object j The weight of an object, D represents the normalized distance, m Indicates the object category, k≤m; j≤M; S3.8: Set the risk score threshold H, if , emergency braking is triggered and an emergency braking command is sent to the vehicle braking system, and the vehicle braking system performs emergency braking according to the command.

7. The method for controlling the automatic emergency braking of an automobile based on fusion data as claimed in claim 6, characterized in that: The priority strategy in S3.6 includes: ； in, Indicates the level of high-speed vehicles, Indicates the level of slow-moving pedestrians, Indicates the level of large vehicles and buildings, Indicates the level of small vehicles and pedestrians, Indicates pedestrians and vehicles moving in the same or opposite direction as the vehicle, and the distance between vehicles Meter level, Indicates the distance between vehicles Meters and in the direction of vehicle movement The level of pedestrians and vehicles at different angles, Indicates the distance between pedestrians and vehicles, Meter level, Indicates the level of accelerating pedestrians and vehicles, Indicates the level of pedestrians or vehicles that are slowing down and moving at a constant speed. Indicates the level of vehicles on the highway, Indicates the level of pedestrians on the highway, Indicates the level of pedestrians on the sidewalk, Indicates the level of vehicles on the sidewalk.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the vehicle automatic emergency braking control method based on fusion data as described in any one of claims 2 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the steps of the vehicle automatic emergency braking control method based on fusion data as described in any one of claims 2 to 7 are executed.

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