Artificial intelligence-based electric control braking method and system for vehicle

Abstract

The application discloses an automobile electric control brake method and system based on artificial intelligence and belongs to the technical field of electric control brake. The method comprises the following steps: obtaining historical driving behavior data corresponding to a current driver, calculating a predicted driving reaction time length of the current driver according to the historical driving behavior data, obtaining real-time road traffic data and real-time vehicle driving data, processing the predicted driving reaction time length, the real-time road traffic data and the real-time vehicle driving data through a trained minimum safe vehicle distance model to obtain a minimum safe vehicle distance, and issuing a warning to prompt the current driver to perform a braking operation when the minimum safe vehicle distance is greater than an actual distance between a current vehicle and a preceding vehicle. The method can perform braking warning according to road conditions, vehicle conditions, driving habits of the driver and actual driving conditions, improves the accuracy of determination of the minimum safe vehicle distance, reduces the risk of rear-end collision and solves the problems of low braking warning accuracy and influence on the driving experience of the driver.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric brake control, in particular to an electric brake control method and system for a vehicle based on artificial intelligence. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] In actual life, due to the difference in behavior habits and reaction ability of the driver, when the driver steps on the brake pedal but the brake performance is poor, the safe distance between the driving vehicle and the front vehicle is too close, and the front vehicle may suddenly stop, resulting in a rear-end collision event.

[0004] In the prior art, by using the feedback function of the electric drive system, according to the real-time vehicle speed and the safe distance, the vehicle is actively and smoothly stopped in front of the obstacle and the power source is cut off by controlling the smooth output of the feedback torque, realizing the front collision warning control of the vehicle. But it does not consider the actual road conditions and the behavior habits of the driver, which affects the accuracy of the warning and the driving experience of the driver. SUMMARY

[0005] In order to solve the problems of the prior art, the present application provides an electric brake control method and system for a vehicle based on artificial intelligence, an electronic device, a computer storage medium and a computer program product, which combines road conditions, vehicle conditions, driver behavior habits and actual driving situations to predict the minimum safe distance, so as to improve the accuracy of brake warning and the driving experience of the driver and passenger.

[0006] In a first aspect, the present application provides an electric brake control method for a vehicle based on artificial intelligence;

[0007] An electric brake control method for a vehicle based on artificial intelligence, comprising:

[0008] Obtaining historical driving behavior data corresponding to the current driver, and calculating the predicted driving reaction time of the current driver according to the historical driving behavior data;

[0009] Obtaining real-time road traffic data and real-time vehicle driving data, and processing the predicted driving reaction time, the real-time road traffic data and the real-time vehicle driving data through the trained minimum safe distance model to obtain the minimum safe distance;

[0010] When the minimum safe distance is greater than the actual distance between the current vehicle and the front vehicle, a warning is issued to prompt the current driver to perform brake operation.

[0011] In some embodiments, the calculating the predicted driving reaction time length of the current driver according to the historical driving behavior data specifically comprises: constructing a driving reaction time length set added with dynamic time labels based on the historical driving behavior data and calculating the predicted driving reaction time length of the current driver.

[0012] In some embodiments, the processing the predicted driving reaction time length, the real-time road traffic data and the real-time vehicle driving data by the trained minimum safe car distance model specifically comprises:

[0013] According to the vehicle driving speed, the predicted driving reaction time length, the delay time length of the brake and the vehicle driving acceleration, a distance traveled by the vehicle within a time period from the early warning to the braking of the vehicle is obtained.

[0014] A driving speed at the start of the braking of the vehicle, a maximum reverse acceleration of the braking and a time length from the start of the braking to the stop of the braking of the vehicle are obtained, and a minimum braking distance of the vehicle is obtained according to the driving speed at the start of the braking of the vehicle, the maximum reverse acceleration of the braking and the time length from the start of the braking to the stop of the braking of the vehicle.

[0015] According to the distance traveled by the vehicle within the time period from the early warning to the braking of the vehicle and the minimum braking distance of the vehicle, the minimum safe car distance is obtained.

[0016] In some embodiments, the obtaining the driving speed at the start of the braking of the vehicle, the maximum reverse acceleration of the braking and the time length from the start of the braking to the stop of the braking of the vehicle specifically comprises:

[0017] According to the vehicle driving speed, the vehicle driving acceleration and the delay time length of the brake, the driving speed at the start of the braking of the vehicle is obtained.

[0018] According to the road slope, the road flatness and the wear degree of the brake pad, the maximum reverse acceleration of the braking is obtained, and according to the driving speed at the start of the braking of the vehicle and the maximum reverse acceleration of the braking, the time length from the start of the braking to the stop of the braking of the vehicle is calculated.

[0019] In some embodiments, the training the minimum safe car distance model specifically comprises: constructing a training set by using historical road traffic data and historical vehicle driving data, updating the minimum safe car distance model by the training set with the average and minimum residual error of the predicted value of the minimum safe car distance and the actual distance between the vehicle and the preceding vehicle as the target.

[0020] In some embodiments, the minimum safe car distance is represented as:

[0021] SSR = SS + R;

[0022] SS = v(s + T) + a(s + T) 2 ;

[0023] R = v' y0 - a' y0 2 ;

[0024] Wherein, SSR represents the optimal safety distance prediction value, SS represents the distance of the vehicle running in the time period from the reminder device reminding to the vehicle braking, the reminder device is used to prompt the driver whether the vehicle distance from the front vehicle reaches the safety distance; R represents the minimum braking distance of the vehicle, v represents the driving speed of the vehicle, s represents the actual driving reaction time, T represents the delay time of the brake, a represents the driving acceleration of the vehicle, a' represents the maximum reverse acceleration of braking, v' represents the driving speed when the vehicle starts braking, y0 represents the time length from braking to the vehicle stopping braking.

[0025] In a second aspect, the present application provides an artificial intelligence-based automobile electric control braking system.

[0026] An artificial intelligence-based automobile electric control braking system, comprising:

[0027] An identification analysis module configured to: obtain historical driving behavior data corresponding to the current driver, and calculate a predicted driving reaction time of the current driver according to the historical driving behavior data.

[0028] A prediction processing module configured to: obtain real-time road traffic data and real-time vehicle driving data, and process the predicted driving reaction time, the real-time road traffic data and the real-time vehicle driving data through the trained minimum safety distance model to obtain a minimum safety distance.

[0029] An early warning module configured to: when the minimum safety distance is greater than the actual distance between the current vehicle and the front vehicle, issue a warning to prompt the current driver to brake.

[0030] In a third aspect, the present application provides an electronic device.

[0031] An electronic device comprising a memory, a processor and a computer program stored on the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned artificial intelligence-based automobile electric control braking method.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium.

[0033] A computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction is executed by a processor to implement the steps of the above-mentioned artificial intelligence-based automobile electric control braking method.

[0034] In a fifth aspect, the present application provides a computer program product.

[0035] A computer program product comprises computer programs / instructions which, when executed by a processor, implement the steps of the above-mentioned artificial intelligence-based automobile electric control braking method.

[0036] Compared with the prior art, the beneficial effects of the present application are:

[0037] 1、The technical scheme provided by the present application takes into account that different drivers have different driving habits and reaction abilities, and based on different drivers, a specific driver's driving reaction time length set is constructed, the driving reaction time length of the same driver at the same time point is predicted, and the accuracy of driving reaction time length prediction is improved.

[0038] 2、The technical scheme provided by the present application takes into account that the influence of road conditions and vehicle conditions on vehicle driving is used to predict the minimum safety distance, and the accuracy of the prediction is improved; at the same time, the driver's behavior habits are combined to predict the minimum safety distance, which improves the driving and riding experience and reduces the risk of rear-end collision with the front vehicle under the premise of safety. BRIEF DESCRIPTION OF DRAWINGS

[0039] The drawings accompanying the specification of this application form a part thereof, serve to provide further understanding of the present application, and together with the description of the exemplary embodiments of the present application and the explanation thereof serve to explain the present application, and do not constitute improper limitations on the present application.

[0040] Figure 1 A flowchart of the artificial intelligence-based automobile electric control braking method provided by the embodiment of the present application is shown in the figure;

[0041] Figure 2 An architecture diagram of the artificial intelligence-based automobile electric control braking system provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0042] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise indicated, all technical and scientific terms used in the present application have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0043] It should be noted that the terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form, and in addition, it should be understood that the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0044] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0045] Embodiment one

[0046] In the prior art, the vehicle is braked based on the real-time driving speed and the safety distance of the vehicle, and the influence of the road state, the vehicle state and the driving habit of the driver on the driving of the vehicle is ignored, which affects the accuracy of the braking warning and the driving experience of the driver; therefore, the present application provides an artificial intelligence-based automobile electric control braking method, which comprehensively considers the influence of the road state and the vehicle state on the driving, and determines the minimum safe vehicle distance according to the driving habit of different drivers to reduce the risk of rear-end collision.

[0047] Next, combined with Figure 1 , a kind of artificial intelligence-based automobile electric control braking method disclosed in the present embodiment is described in detail.The artificial intelligence-based automobile electric control braking method comprises:

[0048] S1, the historical driving behavior data corresponding to the current driver is acquired, and the predicted driving reaction length of the current driver is calculated according to the historical driving behavior data.

[0049] Before S1 is executed, it further includes: after user authorization, a driving storage database is constructed, and road traffic data, driving behavior data and vehicle driving data are collected and stored;Wherein, the road traffic data includes the slope of the road, the flatness of the road and the actual distance between the vehicle and the front vehicle, the driving behavior data includes the driver's face data and the driving reaction time, and the vehicle driving data includes the driving acceleration, the driving speed, the delay time of the brake and the wear degree of the brake pad.

[0050] Here, the actual distance between the vehicle and the front vehicle is identified in the sensing range by the radar sensor, the driver's face data and the flatness of the road are collected by the image acquisition sensor;The road slope is sensed by the gyroscope, the driving reaction time is identified by the pedal pressure sensor and the time sensor, the driving speed and the driving acceleration are identified by the wheel rotation sensor, the delay time of the brake and the wear degree of the brake pad are obtained by the brake device, and the driver is prompted whether the vehicle distance from the front vehicle reaches the safe distance by the prompting device.

[0051] Considering that different drivers have different driving reaction lengths at different time nodes, in order to accurately predict the driving reaction length of the driver, as an implementation manner, S1 specifically includes:

[0052] S101, the face data of the current driver is identified based on artificial intelligence, the driving storage database of the current driver is matched, and the driving behavior data is extracted.

[0053] In this embodiment, the face data of the current driver is collected by the image collection sensor (such as a camera) on the vehicle, the face data is recognized by using the yolo target detection algorithm, and then matched to the driving storage database of the current driver to extract the historical driving behavior data. The existing technology is used for face recognition and matching in this embodiment, and no improvement is made in this embodiment, which will not be described here.

[0054] S102, based on the driving reaction time length of the dynamic time in the historical driving behavior data, a driving reaction time length set S = {s n |n∈(1,N)} is constructed, and the dynamic time label is added to the driving reaction time length set, denoted as S|t={s n |n∈(1,N)},wherein S|t represents the historical data set of the driving reaction time length of the driver at the dynamic time t, the dynamic time t represents the time point at which the vehicle prompts the driver to perform the brake operation, s n represents the nth driving reaction time length historical data, and N represents the number of elements in the driving reaction time length set.

[0055] S103, based on the driving reaction time length set, the predicted driving reaction time length is calculated, denoted as:

[0056]

[0057] For example, in the historical data of the driving storage database of the current driver, when the time point t at which the vehicle prompts the driver to perform the brake operation is 8 o'clock, the driving reaction time length set is S|t={1,1.2,1.01,1.02,0.9}, and the predicted driving reaction time length S′|t=(1+1.2+1.01+1.02+0.9) / 5=1.026 seconds;

[0058] In the historical data of the driving storage database of the current driver, when the time point t at which the vehicle prompts the driver to perform the brake operation is 9 o'clock, the driving reaction time length set is S|t={0.8,0.85,0.9,0.86,0.88}, and the predicted driving reaction time length S′|t=(0.8+0.85+0.9+0.86+0.88) / 5=0.858 seconds.

[0059] S2, real-time road traffic data and real-time vehicle driving data are obtained, and the predicted driving reaction time length, the real-time road traffic data and the real-time vehicle driving data are processed by using the trained minimum safe vehicle distance model to obtain the minimum safe vehicle distance. Specifically, it includes:

[0060] S201, according to the vehicle speed, the predicted driving reaction time length, the delay time length of the brake and the vehicle running acceleration, the distance of the vehicle running in the time period from the warning to the vehicle braking is obtained; denoted as:

[0061] SS = v(s + T) + a(s + T) 2 ;

[0062] wherein v represents the driving speed of the vehicle, s represents the actual driving reaction time, T represents the delay time of the brake, and a represents the driving acceleration of the vehicle.

[0063] S202, acquiring the driving speed at the start of the vehicle braking, the maximum reverse acceleration of the braking, and the time length from the start of the braking to the stop of the braking of the vehicle, and acquiring the minimum braking distance of the vehicle according to the driving speed at the start of the vehicle braking, the maximum reverse acceleration of the braking, and the time length from the start of the braking to the stop of the braking of the vehicle; represented as:

[0064] R = v y0 - a y0 2 ;

[0065] wherein v' represents the driving speed at the start of the vehicle braking, y0 represents the time length from the start of the braking to the stop of the braking of the vehicle, and a' represents the maximum reverse acceleration of the braking.

[0066] v' is represented as:

[0067] v' = v + a(s + T);

[0068] a' is represented as:

[0069] a' = a1p + a2e -u + a3e -β + a4;

[0070] y0 is represented as:

[0071]

[0072] wherein p represents the slope of the road, u represents the flatness of the road, β represents the wear degree of the brake pad, y0 represents the time length from the start of the braking to the stop of the braking of the vehicle, a1, a2, a3 represent regression coefficients, and a4 represents random error under the influence of p, u, and β.

[0073] S203, acquiring the minimum safety distance according to the distance of the vehicle running in the time period from the pre-warning to the braking of the vehicle and the minimum braking distance of the vehicle; represented as:

[0074] SSR = SS + R.

[0075] As an implementation mode, before S2 is performed, the minimum safety distance model needs to be trained, and the specific process is as follows:

[0076] (1) Construct the minimum safety distance model, represented as:

[0077] SSR = SS + R.

[0078] SS = v(s + T) + a(s + T) 2 ;

[0079] R = v'y0 - a'y0 2 ;

[0080] a' = a1p + a2e -u + a3e -β + a4

[0081] v' = v + a(s + T)

[0082]

[0083] In the formula, SSR represents the optimal safe distance prediction value, SS represents the distance of the vehicle running in the time period from the reminder of the reminder device to the braking of the vehicle, the reminder device is used to prompt the driver whether the distance between the vehicle and the front vehicle reaches the safe distance; R represents the minimum braking distance of the vehicle, v represents the driving speed of the vehicle, s represents the driving reaction time, T represents the delay time of the brake, a represents the driving acceleration of the vehicle, a' represents the maximum reverse acceleration of the brake, v' represents the driving speed when the vehicle starts to brake, y0 represents the time length from the start of braking to the stop of the vehicle; a1, a2, a3 represent the regression coefficient, and a4 represents the random error under the influence of p, u and β.

[0084] (2) Using historical road traffic data and historical vehicle driving data to construct a training set, constantly updating the regression coefficient and random error, using the least square method to calculate the residual average sum of the minimum safe distance prediction value and the actual distance of the vehicle and the front vehicle, until the residual average sum reaches the minimum; The residual average sum is represented as:

[0085]

[0086] In the formula, SSR(i) represents the fth minimum safe distance prediction value in the training data, D(f) represents the fth actual distance from the front vehicle, H represents the residual square sum of the minimum safe distance prediction value and the actual distance from the front vehicle, and F represents the number of training data.

[0087] S3, when the minimum safe distance is greater than the actual distance of the current vehicle and the front vehicle, a warning is issued to prompt the current driver to brake.

[0088] Example two

[0089] In combination Figure 2 , the embodiment discloses an automobile electric control brake system based on artificial intelligence, comprising:

[0090] The identification analysis module is configured to: acquire historical driving behavior data corresponding to the current driver, and calculate a predicted driving reaction time length of the current driver according to the historical driving behavior data.

[0091] The prediction processing module is configured to: acquire real-time road traffic data and real-time vehicle driving data, and process the predicted driving reaction time length, the real-time road traffic data and the real-time vehicle driving data by using the trained minimum safe vehicle distance model to acquire a minimum safe vehicle distance.

[0092] The early warning module is configured to: when the minimum safe vehicle distance is greater than an actual distance between the current vehicle and a preceding vehicle, issue a warning to prompt the current driver to perform a braking operation.

[0093] It should be noted that the identification analysis module, the prediction processing module and the early warning module correspond to the steps in Embodiment One, and the above modules and the examples and application scenarios realized by the corresponding steps are the same, but are not limited to the content disclosed in Embodiment One. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0094] Embodiment Three

[0095] Embodiment Three of the present application provides an electronic device, which includes a memory and a processor, and computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned artificial intelligence-based automobile electric control braking method are completed.

[0096] Embodiment Four

[0097] Embodiment Four of the present application provides a computer readable storage medium for storing computer instructions. When the computer instructions are executed by the processor, the steps of the above-mentioned artificial intelligence-based automobile electric control braking method are completed.

[0098] Embodiment Five

[0099] Embodiment Five of the present application provides a computer program product, which includes a computer program / instruction. When the computer program / instruction is executed by the processor, the steps of the above-mentioned artificial intelligence-based automobile electric control braking method are realized.

[0100] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0101] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0102] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0103] The above description of the various embodiments can have emphasized different features and variations. Details that are not expressly provided herein but are obvious to one skilled in the art are intended to be within the scope of the embodiments. The description of features or aspects within each embodiment should be considered as available for use in other similar embodiments, and that the application is applicable to other embodiments and / or situations.

[0104] The application described above is merely preferred embodiments of the application, and the application is not limited thereto. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the scope of the application.

Claims

1. An artificial intelligence-based electric control braking method for a vehicle, characterized by, The method comprises the following steps: obtaining historical driving behavior data corresponding to the current driver, and calculating a predicted driving reaction time length of the current driver according to the historical driving behavior data; obtaining real-time road traffic data and real-time vehicle driving data, and processing the predicted driving reaction time length, the real-time road traffic data and the real-time vehicle driving data through a trained minimum safe vehicle distance model to obtain a minimum safe vehicle distance; when the minimum safe vehicle distance is greater than an actual distance between the current vehicle and a preceding vehicle, issuing a warning to prompt the current driver to perform a braking operation; the processing of the predicted driving reaction time length, the real-time road traffic data and the real-time vehicle driving data through the trained minimum safe vehicle distance model specifically comprises: obtaining a distance traveled by the vehicle within a time period from the warning to the braking of the vehicle according to the vehicle speed, the predicted driving reaction time length, the delay time length of the brake and the vehicle travel acceleration; obtaining the vehicle speed at the start of braking, the maximum reverse acceleration of braking and the time length from the start of braking to the stop of braking of the vehicle, and obtaining the minimum braking distance of the vehicle according to the vehicle speed at the start of braking, the maximum reverse acceleration of braking and the time length from the start of braking to the stop of braking of the vehicle; obtaining the minimum safe vehicle distance according to the distance traveled by the vehicle within the time period from the warning to the braking of the vehicle and the minimum braking distance of the vehicle; training the minimum safe vehicle distance model specifically comprises: constructing a training set by using historical road traffic data and historical vehicle driving data, taking the average and minimum residual of the predicted value of the minimum safe vehicle distance and the actual distance between the vehicle and the preceding vehicle as the target, and updating the minimum safe vehicle distance model through the training set. 2.The artificial intelligence-based automobile electric control braking method of claim 1, wherein, The calculation of the predicted driving reaction time length of the current driver according to the historical driving behavior data specifically comprises: constructing a driving reaction time length set with dynamic time labels added based on the historical driving behavior data and calculating the predicted driving reaction time length of the current driver. 3.The artificial intelligence-based automobile electric control braking method of claim 1, wherein, The obtaining of the vehicle speed at the start of braking, the maximum reverse acceleration of braking and the time length from the start of braking to the stop of braking of the vehicle specifically comprises: obtaining the vehicle speed at the start of braking according to the vehicle speed, the vehicle travel acceleration and the delay time length of the brake; obtaining the maximum reverse acceleration of braking according to the road slope, the road flatness and the wear degree of the brake pad, and calculating the time length from the start of braking to the stop of braking of the vehicle according to the vehicle speed at the start of braking and the maximum reverse acceleration of braking. 4.The AI-based automobile electric control brake method of claim 1, wherein, The minimum safe vehicle distance is represented as: SSR = SS + R. ； ； wherein SSR represents an optimal safe distance prediction value, SS represents a distance traveled by the vehicle during a time period from the reminder device reminding to the vehicle braking, R represents a minimum braking distance of the vehicle, v represents a driving speed of the vehicle, s represents a prediction driving reaction time length, T represents a delay time length of the brake, and a represents a driving acceleration of the vehicle, represents a maximum reverse acceleration of braking, represents a driving speed at the time when the vehicle starts braking, represents a time length from the start of braking to the stop of braking of the vehicle.

5. An artificial intelligence-based electric control brake system for a vehicle, characterized by, The method for realizing the method for controlling the braking of an automobile by using artificial intelligence according to any one of claims 1-4 comprises: an identification analysis module configured to obtain historical driving behavior data corresponding to the current driver, and calculate a predicted driving reaction time length of the current driver according to the historical driving behavior data; a prediction processing module configured to obtain real-time road traffic data and real-time vehicle driving data, and process the predicted driving reaction time length, the real-time road traffic data and the real-time vehicle driving data through a trained minimum safe vehicle distance model to obtain a minimum safe vehicle distance; The early warning module is configured to issue a warning to prompt the current driver to brake when the minimum safe vehicle distance is greater than the actual distance between the current vehicle and the preceding vehicle.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, causes the electronic device to perform the method of any one of claims 1 to 5. The processor executes the computer program to implement the steps of the artificial intelligence-based automobile electric control braking method of any one of claims 1-4.

7. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instructions, when executed by the processor, implement the steps of the artificial intelligence-based automobile electric control braking method of any one of claims 1-4.

8. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions, when executed by the processor, implement the steps of the artificial intelligence-based automobile electric control braking method of any one of claims 1-4. The computer program / instructions, when executed by the processor, implement the steps of the artificial intelligence-based automobile electric control braking method of any one of claims 1-4.

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