Vehicle automatic emergency braking control method and device, equipment and medium

Abstract

The embodiment of the invention provides an automatic emergency braking control method and device for a vehicle, equipment and a medium, and the method comprises the steps: determining a visual modal road adhesion coefficient and a dynamic modal road adhesion coefficient of the vehicle, and determining a humidity modal road adhesion coefficient of the vehicle through a humidity sensor; then obtaining a first weight corresponding to the visual modal pavement adhesion coefficient, a second weight corresponding to the dynamic modal pavement adhesion coefficient and a third weight corresponding to the humidity modal pavement adhesion coefficient; determining a comprehensive pavement adhesion coefficient according to the pavement adhesion coefficient and the weight; an automatic emergency braking control instruction is generated according to the comprehensive road adhesion coefficient, and the vehicle is subjected to automatic emergency braking control according to the automatic emergency braking control instruction, so that the accuracy of obtaining the road adhesion coefficient is improved by comprehensively considering the three road adhesion coefficients; the method does not need to completely depend on a visual identification function, and improves the safety in the vehicle driving process.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic emergency braking control, and in particular to a vehicle automatic emergency braking control method, device, equipment and medium. Background Art

[0002] AEB (Automatic Emergency Braking) is an intelligent driving assistance system based on vehicle active safety technology. Its core function is to automatically detect the surrounding environment and assess the collision risk when the vehicle is about to collide or there are other emergency dangerous situations, and actively trigger the braking system to reduce the vehicle speed or even stop the vehicle completely, thereby avoiding or reducing the severity of a collision accident.

[0003] According to statistics, about 10% of traffic accidents that cause injuries are caused by slippery roads. Compared with dry roads, the adhesion coefficient of water, snow, and ice roads is greatly reduced, which in turn causes the braking distance to be extended several times. The reduction in the road adhesion coefficient limits the original effect of the braking force applied by the vehicle tires on the road. Only by obtaining accurate road adhesion coefficient data can the automatic emergency braking control help the vehicle shorten the braking distance, increase the safe distance from the vehicle in front, improve the driving safety index, and reduce vehicle accidents.

[0004] However, the technical solutions in the related art generally use visual recognition to obtain road adhesion coefficient data. However, under environmental conditions such as night, cloudy days or backlight, the visual recognition method is prone to inaccurate detection, which seriously affects the vehicle's automatic emergency braking control effect. Summary of the invention

[0005] In view of the above problems, a vehicle automatic emergency braking control method, device, equipment, and medium are proposed to overcome the above problems or at least partially solve the above problems, including:

[0006] A vehicle automatic emergency braking control method, wherein the vehicle is provided with a humidity sensor, the method comprising:

[0007] Determining a visual modal road adhesion coefficient and a dynamic modal road adhesion coefficient of the vehicle, and determining a humidity modal road adhesion coefficient of the vehicle through the humidity sensor;

[0008] Obtaining a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient; wherein the third weight is greater than the second weight, and the second weight is greater than the first weight;

[0009] Determining a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight;

[0010] An automatic emergency braking control instruction is generated according to the comprehensive road adhesion coefficient, and automatic emergency braking control is performed on the vehicle according to the automatic emergency braking control instruction.

[0011] Optionally, determining the humidity modal road adhesion coefficient of the vehicle by using the humidity sensor includes:

[0012] Determine the vibration frequency generated by the splashing water of the vehicle tire by the humidity sensor, and convert the vibration frequency into an electrical signal;

[0013] The degree of water accumulation on the road is determined according to the electrical signal, and the humidity modal road adhesion coefficient is determined according to the degree of water accumulation on the road.

[0014] Optionally, determining the visual modal road adhesion coefficient of the vehicle includes:

[0015] Acquire a road image in front of the vehicle, and perform feature extraction and road surface granularity information extraction on the road image in front;

[0016] The visual modality road adhesion coefficient is determined according to the feature extraction result and the road surface granularity information extraction result.

[0017] Optionally, before determining the comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight, the method further includes:

[0018] The first weight and the third weight are adjusted according to the degree of water accumulation on the road, the feature extraction result and the road surface granularity information extraction result.

[0019] Optionally, determining a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight includes:

[0020] According to the first weight, the second weight and the third weight, the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient are weighted averaged to obtain the comprehensive road adhesion coefficient.

[0021] Optionally, generating an automatic emergency braking control instruction according to the comprehensive road adhesion coefficient includes:

[0022] A risk target is determined, and the automatic emergency braking control instruction is generated based on the risk target, the dynamic model of the vehicle and the comprehensive road adhesion coefficient.

[0023] Optionally, the humidity sensor is arranged on a side of the vehicle wheel arch away from a normal driving direction of the vehicle.

[0024] A vehicle automatic emergency brake control device, wherein the vehicle is provided with a humidity sensor, and the device comprises:

[0025] a road adhesion coefficient determination module, used to determine the visual modal road adhesion coefficient and the dynamic modal road adhesion coefficient of the vehicle, and determine the humidity modal road adhesion coefficient of the vehicle through the humidity sensor;

[0026] A weight acquisition module, used to acquire a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient; wherein the third weight is greater than the second weight, and the second weight is greater than the first weight;

[0027] a comprehensive road adhesion coefficient determination module, configured to determine a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight;

[0028] The automatic emergency braking control module is used to generate an automatic emergency braking control instruction according to the comprehensive road adhesion coefficient, and perform automatic emergency braking control on the vehicle according to the automatic emergency braking control instruction.

[0029] Optionally, the road adhesion coefficient determination module includes:

[0030] A vibration frequency determination submodule, used to determine the vibration frequency generated by the splashing water of the vehicle tire through the humidity sensor, and convert the vibration frequency into an electrical signal;

[0031] The humidity modal road adhesion coefficient determination submodule is used to determine the degree of water accumulation on the road according to the electrical signal, and to determine the humidity modal road adhesion coefficient according to the degree of water accumulation on the road.

[0032] Optionally, the road adhesion coefficient determination module includes:

[0033] An information extraction submodule, used to obtain a road image in front of the vehicle, and perform feature extraction and road surface granularity information extraction on the road image in front;

[0034] The visual modality road surface adhesion coefficient determination submodule is used to determine the visual modality road surface adhesion coefficient according to the feature extraction result and the road surface granularity information extraction result.

[0035] Optionally, the device further comprises:

[0036] The weight adjustment module is used to adjust the first weight and the third weight according to the degree of water accumulation on the road, the feature extraction result and the road surface granularity information extraction result.

[0037] Optionally, the comprehensive road adhesion coefficient determination module includes:

[0038] The comprehensive road adhesion coefficient determination submodule is used to perform weighted averaging of the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient according to the first weight, the second weight and the third weight to obtain the comprehensive road adhesion coefficient.

[0039] Optionally, the automatic emergency braking control module includes:

[0040] The automatic emergency braking control instruction generation submodule is used to determine the risk target and generate the automatic emergency braking control instruction according to the risk target, the dynamic model of the vehicle and the comprehensive road adhesion coefficient.

[0041] An electronic device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the vehicle automatic emergency braking control method as described above when executed by the processor.

[0042] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle automatic emergency braking control method as described above is implemented.

[0043] The embodiments of the present invention have the following advantages:

[0044] In an embodiment of the present invention, a vehicle automatic emergency braking control method is provided. The vehicle is provided with a humidity sensor. The visual modal road adhesion coefficient and the dynamic modal road adhesion coefficient of the vehicle are determined, and the humidity modal road adhesion coefficient of the vehicle is determined by the humidity sensor. Then, a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient are obtained. Thus, a comprehensive road adhesion coefficient is determined according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight, and the third weight. Then, an automatic emergency braking control instruction is generated according to the comprehensive road adhesion coefficient, and the vehicle is automatically emergency braked according to the automatic emergency braking control instruction, so that the accuracy of obtaining the road adhesion coefficient is improved by comprehensively considering the three road adhesion coefficients. There is no need to completely rely on the visual recognition function, and the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient are comprehensively considered. The accuracy of obtaining the road adhesion coefficient is further improved based on a reasonable weight configuration, thereby ensuring the braking effect of the automatic emergency braking control and improving the safety of the vehicle during driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0046] Figure 1 is a flowchart of a vehicle automatic emergency braking control method provided by some embodiments of the present invention;

[0047] Figure 2 is an exemplary diagram of the installation position of the humidity sensor of the present invention provided by some embodiments of the present invention;

[0048] Figure 3 It is a schematic diagram of an overall execution flow of the present invention provided by some embodiments of the present invention;

[0049] Figure 4 It is a structural schematic diagram of a vehicle automatic emergency braking control device provided by some embodiments of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] AEB is an intelligent driving assistance system based on vehicle active safety technology. Its core function is to automatically detect the surrounding environment and assess the collision risk when the vehicle is about to collide or there are other emergency dangerous situations, and actively trigger the braking system to reduce the vehicle speed or even stop the vehicle completely, thereby avoiding or reducing the severity of the collision accident.

[0052] According to statistics, about 10% of traffic accidents that cause injuries are caused by slippery roads. Compared with dry roads, the adhesion coefficient of water, snow, and ice roads is greatly reduced, which in turn causes the braking distance to be extended several times. The reduction in the road adhesion coefficient limits the original effect of the braking force applied by the vehicle tires on the road. Only by obtaining accurate road adhesion coefficient data can the automatic emergency braking control help the vehicle shorten the braking distance, increase the safe distance from the vehicle in front, improve the driving safety index, and reduce vehicle accidents.

[0053] However, the technical solutions in the related art generally use visual recognition to obtain road adhesion coefficient data. However, under environmental conditions such as night, cloudy days or backlight, the visual recognition method is prone to inaccurate detection, which seriously affects the vehicle's automatic emergency braking control effect.

[0054] In view of the above problems, in an embodiment of the present invention, based on the core technical concept of comprehensively considering three road adhesion coefficients to improve the accuracy of obtaining the road adhesion coefficient, an improvement is made to the vehicle automatic emergency braking control method in the related art. The present invention will be described in detail below with reference to the accompanying drawings:

[0055] Reference Figure 1 , shows a flow chart of the steps of a vehicle automatic emergency braking control method provided by some embodiments of the present invention, wherein the vehicle is provided with a humidity sensor, and specifically may include the following steps:

[0056] Step 101, determining a visual modal road adhesion coefficient and a dynamic modal road adhesion coefficient of the vehicle, and determining a humidity modal road adhesion coefficient of the vehicle through the humidity sensor;

[0057] In a specific implementation, for the determination of the visual modality road adhesion coefficient, the image of the road ahead can be captured in real time by an on-board infrared and / or visible light camera, and fused by a dynamic fusion algorithm, and then sent to an image processing algorithm for processing; for example, an improved Yolo-v9 algorithm can be used for feature extraction to obtain road-related information, and the adhesion range of the road ahead can be obtained by table lookup after target detection and classification; on this basis, the road granularity classification result can be determined by road granularity extraction (such as dry, slippery or snowy roads), and then the visual modality road adhesion coefficient can be comprehensively determined in combination with the feature extraction process;

[0058] As for the determination of the dynamic modal road adhesion coefficient, tire dynamics can be used to estimate the adhesion of the vehicle's current road surface. For example, the tire longitudinal response information, vehicle longitudinal acceleration, lateral acceleration, yaw rate, vehicle wheel speed, slip rate and deceleration information can be obtained, and the dynamic modal road adhesion coefficient can be estimated based on this.

[0059] Furthermore, for the determination of the humidity modal road adhesion coefficient, such as Figure 2 As shown, a humidity sensor can be arranged on the side of the vehicle wheel cover away from the normal driving direction of the vehicle, so that water splashed by the tires when the vehicle is driving can impact the humidity sensor, so that the vibration frequency generated by the splashed water of the vehicle tires can be converted into an electrical signal by the humidity sensor, and then the degree of water accumulation on the road can be determined according to the electrical signal; for example, calibration can be carried out in advance at different road surfaces and different vehicle speeds to generate a comparison table or map of electrical signal-road water accumulation degree-humidity modal road adhesion coefficient, so that the degree of water accumulation on the road can be directly determined by the electrical signal by looking up the table; the water film thickness can be further calculated, and the humidity modal road adhesion coefficient can be calculated based on this.

[0060] In some embodiments of the present invention, determining the humidity modal road adhesion coefficient of the vehicle by means of the humidity sensor includes:

[0061] Determine the vibration frequency generated by the splashing water of the vehicle tire by the humidity sensor, and convert the vibration frequency into an electrical signal;

[0062] The degree of water accumulation on the road is determined according to the electrical signal, and the humidity modal road adhesion coefficient is determined according to the degree of water accumulation on the road.

[0063] In practical applications, as mentioned above, for the determination of the humidity modal road adhesion coefficient, a humidity sensor can be set on the side of the vehicle wheel arch away from the normal driving direction of the vehicle, so that the water splashed by the tires when the vehicle is driving can impact the humidity sensor, and the vibration frequency generated by the water splashed by the vehicle tires can be converted into an electrical signal by the humidity sensor, and then the degree of water accumulation on the road can be determined based on the electrical signal; for example, calibration can be carried out in advance at different road surfaces and different vehicle speeds to generate a comparison table or map of electrical signal-road water accumulation degree-humidity modal road adhesion coefficient, so that the degree of water accumulation on the road can be determined directly through the electrical signal by looking up the table, and the humidity modal road adhesion coefficient can be further determined based on the degree of water accumulation on the road; the water film thickness can also be further calculated, and the humidity modal road adhesion coefficient can be calculated based on this.

[0064] In some embodiments of the present invention, determining the visual modal road adhesion coefficient of the vehicle includes:

[0065] Acquire a road image in front of the vehicle, and perform feature extraction and road surface granularity information extraction on the road image in front;

[0066] The visual modality road adhesion coefficient is determined according to the feature extraction result and the road surface granularity information extraction result.

[0067] In practical applications, as mentioned above, for the determination of the visual modality road adhesion coefficient, the image of the road ahead can be captured in real time by the vehicle-mounted infrared and / or visible light camera, and fused by a dynamic fusion algorithm. After fusion, it can be sent to the image processing algorithm for processing; for example, the improved Yolo-v9 algorithm can be used for feature extraction to obtain road-related information, and the adhesion range of the road ahead can be obtained by table lookup after target detection and classification; on this basis, the road granularity classification result can be determined by road granularity extraction (such as dry, wet or snowy roads), and then the visual modality road adhesion coefficient can be comprehensively determined in combination with the feature extraction process.

[0068] In some embodiments of the present invention, the humidity sensor is disposed on a side of the vehicle wheel housing away from a normal driving direction of the vehicle.

[0069] In the specific implementation, Figure 2As shown, for the determination of the humidity modal road adhesion coefficient, a humidity sensor can be set on the side of the vehicle wheel cover away from the normal driving direction of the vehicle, so that the water splashed by the tires when the vehicle is driving can impact the humidity sensor, so that the vibration frequency generated by the water splashed by the vehicle tires can be converted into an electrical signal by the humidity sensor, and then the degree of water accumulation on the road can be determined according to the electrical signal; for example, calibration can be carried out in advance at different road surfaces and different vehicle speeds to generate a comparison table or map of electrical signal-road water accumulation degree-humidity modal road adhesion coefficient, so that the degree of water accumulation on the road can be directly determined by the electrical signal by looking up the table, and the humidity modal road adhesion coefficient can be further determined according to the degree of water accumulation on the road; the water film thickness can also be further calculated, and the humidity modal road adhesion coefficient can be calculated based on this.

[0070] Step 102, obtaining a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient; wherein the third weight is greater than the second weight, and the second weight is greater than the first weight;

[0071] In practical applications, since the accuracy of the visual recognition method is relatively low, the humidity modal road adhesion coefficient obtained by the humidity sensor is more in line with the current road wetness; therefore, the weight corresponding to the humidity modal road adhesion coefficient (third weight) can be set > the weight corresponding to the dynamic modal road adhesion coefficient (second weight) > the weight corresponding to the visual modal road adhesion coefficient (first weight); to ensure the accuracy of the comprehensive road adhesion coefficient, specifically, the actual weight can be determined by confidence calculation based on the above basic rules, and the corresponding weight can be adjusted in real time according to the actual road wetness, visual feature extraction results and road granularity information extraction results, so as to improve the rationality of the weight value setting.

[0072] Step 103, determining a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight;

[0073] In the specific implementation, after determining the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight, the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient can be weighted averaged according to the first weight, the second weight and the third weight to obtain a comprehensive road adhesion coefficient for subsequent guidance of the issuance of AEB control instructions.

[0074] In some embodiments of the present invention, before determining the comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight, the method further includes:

[0075] The first weight and the third weight are adjusted according to the degree of water accumulation on the road, the feature extraction result and the road surface granularity information extraction result.

[0076] In a specific implementation, since the accuracy of the visual recognition method is relatively low, and the humidity modal road adhesion coefficient obtained by the humidity sensor is more in line with the current road wetness and slipperiness, the weight corresponding to the humidity modal road adhesion coefficient (third weight) can be set to be greater than the weight corresponding to the dynamic modal road adhesion coefficient (second weight) and greater than the weight corresponding to the visual modal road adhesion coefficient (first weight). To ensure the accuracy of the comprehensive road adhesion coefficient, specifically, the actual weight can be determined by confidence calculation based on the above basic rules, and the corresponding weight can be adjusted in real time according to the actual road wetness and slipperiness, the visual feature extraction results and the road granularity information extraction results, so as to improve the rationality of the weight value setting.

[0077] For example, when the visual feature extraction results show that there is reflective phenomenon on the road surface or the road surface granularity information extraction results show that the road surface is snowy, the first weight corresponding to the visual modal road adhesion coefficient can be appropriately increased; or when the actual road condition is serious, the third weight corresponding to the humidity modal road adhesion coefficient can be appropriately increased.

[0078] In some embodiments of the present invention, determining a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight includes:

[0079] According to the first weight, the second weight and the third weight, the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient are weighted averaged to obtain the comprehensive road adhesion coefficient.

[0080] In practical applications, after determining the visual modal road adhesion coefficient, dynamic modal road adhesion coefficient, humidity modal road adhesion coefficient, the first weight, the second weight and the third weight, the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient can be weighted averaged according to the first weight, the second weight and the third weight to obtain a comprehensive road adhesion coefficient for subsequent guidance of the issuance of AEB control instructions.

[0081] Step 104 , generating an automatic emergency braking control instruction according to the comprehensive road adhesion coefficient, and performing automatic emergency braking control on the vehicle according to the automatic emergency braking control instruction.

[0082] In practical applications, the AEB perception system can predict the lateral and longitudinal distances, acceleration and deceleration, and trajectory planning of dangerous targets ahead, identify collision risks, and determine risk targets; then the AEB decision system can issue AEB braking commands (automatic emergency braking control commands) based on the target attributes of the risk targets identified by the perception system, combined with the vehicle dynamics model and the comprehensive road adhesion coefficient of the current road; for example, the vehicle longitudinal dynamics model can be used to analyze the force balance relationship of the vehicle to ensure that the braking force calculation meets the limitations of the comprehensive road adhesion coefficient, thereby avoiding excessive braking that causes tire slippage or insufficient braking that causes collision with risk targets.

[0083] Furthermore, the control system can control the vehicle brakes for braking according to the automatic emergency braking control command issued by the AEB decision system. During braking, the vehicle wheel speed, slip rate and deceleration at the braking moment can be detected in real time. If the braking force of the braking measure corresponding to the current automatic emergency braking control command is less than the planned force of the decision system, a larger deceleration can be re-issued for braking. For example, the calculation process of the subsequent comprehensive road adhesion coefficient can be optimized according to the current comprehensive road adhesion coefficient. For example, the optimized corresponding parameters, such as vehicle wheel speed, slip rate and deceleration, can be returned to the determination stage of the dynamic modal road adhesion coefficient in the aforementioned steps, thereby affecting the determination of the subsequent comprehensive road adhesion coefficient; the current comprehensive road adhesion coefficient can also be referred to when the comprehensive road adhesion coefficient is determined by weighted averaging in the subsequent process, so as to make the subsequently determined comprehensive road adhesion coefficient as close as possible to the current comprehensive road adhesion coefficient, so as to avoid excessive fluctuations while achieving the effect of adjusting the braking force.

[0084] In some embodiments of the present invention, generating an automatic emergency braking control instruction according to the comprehensive road adhesion coefficient includes:

[0085] A risk target is determined, and the automatic emergency braking control instruction is generated based on the risk target, the dynamic model of the vehicle and the comprehensive road adhesion coefficient.

[0086] In practical applications, the AEB perception system can predict the lateral and longitudinal distances, acceleration and deceleration, and trajectory planning of dangerous targets ahead, identify collision risks, and determine risk targets; then the AEB decision system can issue AEB braking commands (automatic emergency braking control commands) based on the target attributes of the risk targets identified by the perception system, combined with the vehicle dynamics model and the comprehensive road adhesion coefficient of the current road; for example, the vehicle longitudinal dynamics model can be used to analyze the force balance relationship of the vehicle to ensure that the braking force calculation meets the limitations of the comprehensive road adhesion coefficient, thereby avoiding excessive braking that causes tire slippage or insufficient braking that causes collision with risk targets.

[0087] The following combination Figure 3 The embodiments of the present invention are exemplarily described as follows:

[0088] Depend on Figure 3 It can be seen from the content that an overall execution process of the present invention when performing vehicle automatic emergency braking control can be summarized as follows:

[0089] S1: Use the visual perception system to identify the road image ahead in real time, use a neural network to extract features and road granularity, and extract and confirm the visual modal road adhesion coefficient;

[0090] S2: The road humidity sensor arranged on the vehicle body behind the tire is used to obtain the vibration frequency generated by the tire splashing water impacting the sensor surface, and the vibration frequency is converted into an electrical signal. The electrical signal spectrum collected by the sensor at different road surfaces and different vehicle speeds is analyzed to determine the degree of road water accumulation, calculate the water film thickness, and calculate the humidity modal road adhesion coefficient.

[0091] S3: Estimate the dynamic modal road adhesion coefficient through vehicle kinematics and tire longitudinal response information;

[0092] S4: combining the calculated values ​​of the three types of road adhesion coefficients, performing weighted average on the three types of road adhesion coefficients, and obtaining a comprehensive road adhesion coefficient;

[0093] S5: The AEB perception system predicts the lateral and longitudinal distances, acceleration and deceleration, and trajectory planning of the dangerous target ahead to identify the risk of collision;

[0094] S6: The AEB decision system issues an AEB braking command based on the target attributes of the risk target identified by the perception system, combined with the vehicle dynamics model and the comprehensive road adhesion coefficient of the current road;

[0095] S7: The control system controls the brakes according to the braking command issued by the decision system. At the same time, it combines S3 to detect the vehicle wheel speed, slip rate and deceleration at the braking moment in real time. If the braking force is less than the planning of the decision system, a larger deceleration is re-issued for braking.

[0096] In an embodiment of the present invention, a vehicle automatic emergency braking control method is provided. The vehicle is provided with a humidity sensor. The visual modal road adhesion coefficient and the dynamic modal road adhesion coefficient of the vehicle are determined, and the humidity modal road adhesion coefficient of the vehicle is determined by the humidity sensor. Then, a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient are obtained. Thus, a comprehensive road adhesion coefficient is determined according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight, and the third weight. Then, an automatic emergency braking control instruction is generated according to the comprehensive road adhesion coefficient, and the vehicle is automatically emergency braked according to the automatic emergency braking control instruction, so that the accuracy of obtaining the road adhesion coefficient is improved by comprehensively considering the three road adhesion coefficients. There is no need to completely rely on the visual recognition function, and the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient are comprehensively considered. The accuracy of obtaining the road adhesion coefficient is further improved based on a reasonable weight configuration, thereby ensuring the braking effect of the automatic emergency braking control and improving the safety of the vehicle during driving.

[0097] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0098] Reference Figure 4 , shows a schematic structural diagram of a vehicle automatic emergency brake control device provided by some embodiments of the present invention, wherein the vehicle is provided with a humidity sensor, which may specifically include the following modules:

[0099] The road adhesion coefficient determination module 401 is used to determine the visual modal road adhesion coefficient and the dynamic modal road adhesion coefficient of the vehicle, and determine the humidity modal road adhesion coefficient of the vehicle through the humidity sensor;

[0100] A weight acquisition module 402 is used to acquire a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient; wherein the third weight is greater than the second weight, and the second weight is greater than the first weight;

[0101] A comprehensive road adhesion coefficient determination module 403, configured to determine a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight;

[0102] The automatic emergency braking control module 404 is used to generate an automatic emergency braking control instruction according to the comprehensive road adhesion coefficient, and perform automatic emergency braking control on the vehicle according to the automatic emergency braking control instruction.

[0103] In some embodiments of the present invention, the road adhesion coefficient determination module 401 includes:

[0104] A vibration frequency determination submodule, used to determine the vibration frequency generated by the splashing water of the vehicle tire through the humidity sensor, and convert the vibration frequency into an electrical signal;

[0105] The humidity modal road adhesion coefficient determination submodule is used to determine the degree of water accumulation on the road according to the electrical signal, and to determine the humidity modal road adhesion coefficient according to the degree of water accumulation on the road.

[0106] In some embodiments of the present invention, the road adhesion coefficient determination module 401 includes:

[0107] An information extraction submodule, used to obtain a road image in front of the vehicle, and perform feature extraction and road surface granularity information extraction on the road image in front;

[0108] The visual modality road surface adhesion coefficient determination submodule is used to determine the visual modality road surface adhesion coefficient according to the feature extraction result and the road surface granularity information extraction result.

[0109] In some embodiments of the present invention, the device further comprises:

[0110] The weight adjustment module is used to adjust the first weight and the third weight according to the degree of water accumulation on the road, the feature extraction result and the road surface granularity information extraction result.

[0111] In some embodiments of the present invention, the comprehensive road adhesion coefficient determination module 403 includes:

[0112] The comprehensive road adhesion coefficient determination submodule is used to perform weighted averaging of the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient according to the first weight, the second weight and the third weight to obtain the comprehensive road adhesion coefficient.

[0113] In some embodiments of the present invention, the automatic emergency braking control module 404 includes:

[0114] The automatic emergency braking control instruction generation submodule is used to determine the risk target and generate the automatic emergency braking control instruction according to the risk target, the dynamic model of the vehicle and the comprehensive road adhesion coefficient.

[0115] Some embodiments of the present invention also provide an electronic device, which may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor, and when the computer program is executed by the processor, the above-mentioned vehicle automatic emergency braking control method is implemented.

[0116] Some embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned vehicle automatic emergency braking control method is implemented.

[0117] Some embodiments of the present invention further provide a computer program product, including a computer program, which implements the above vehicle automatic emergency braking control method when executed by a processor.

[0118] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0119] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0120] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0121] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0125] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the above elements.

[0126] The above is a detailed introduction to a vehicle automatic emergency braking control method, device, equipment, and medium provided. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technicians in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A vehicle automatic emergency braking control method, characterized in that: The vehicle is provided with a humidity sensor, and the method comprises: Determining a visual modal road adhesion coefficient and a dynamic modal road adhesion coefficient of the vehicle, and determining a humidity modal road adhesion coefficient of the vehicle through the humidity sensor; Obtaining a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient; wherein the third weight is greater than the second weight, and the second weight is greater than the first weight; Determining a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight; An automatic emergency braking control instruction is generated according to the comprehensive road adhesion coefficient, and automatic emergency braking control is performed on the vehicle according to the automatic emergency braking control instruction.

2. The method according to claim 1, characterized in that Determining the humidity modal road adhesion coefficient of the vehicle by means of the humidity sensor includes: Determine the vibration frequency generated by the splashing water of the vehicle tire by the humidity sensor, and convert the vibration frequency into an electrical signal; The degree of water accumulation on the road is determined according to the electrical signal, and the humidity modal road adhesion coefficient is determined according to the degree of water accumulation on the road.

3. The method according to claim 2, characterized in that Determining the visual modal road adhesion coefficient of the vehicle includes: Acquire a road image in front of the vehicle, and perform feature extraction and road surface granularity information extraction on the road image in front; The visual modality road adhesion coefficient is determined according to the feature extraction result and the road surface granularity information extraction result.

4. The method according to claim 3, characterized in that Before determining the comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight, the method further includes: The first weight and the third weight are adjusted according to the degree of water accumulation on the road, the feature extraction result and the road surface granularity information extraction result.

5. The method according to claim 1, characterized in that The step of determining a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight, and the third weight includes: According to the first weight, the second weight and the third weight, the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient and the humidity modal road adhesion coefficient are weighted averaged to obtain the comprehensive road adhesion coefficient.

6. The method according to claim 1, characterized in that The step of generating an automatic emergency braking control instruction according to the comprehensive road adhesion coefficient includes: A risk target is determined, and the automatic emergency braking control instruction is generated based on the risk target, the dynamic model of the vehicle and the comprehensive road adhesion coefficient.

7. The method according to claim 1 or 2, characterized in that: The humidity sensor is arranged on a side of the vehicle wheel cover away from a normal driving direction of the vehicle.

8. A vehicle automatic emergency brake control device, characterized in that: The device comprises: a road adhesion coefficient determination module, used to determine the visual modal road adhesion coefficient and the dynamic modal road adhesion coefficient of the vehicle, and determine the humidity modal road adhesion coefficient of the vehicle through the humidity sensor; A weight acquisition module, used to acquire a first weight corresponding to the visual modal road adhesion coefficient, a second weight corresponding to the dynamic modal road adhesion coefficient, and a third weight corresponding to the humidity modal road adhesion coefficient; wherein the third weight is greater than the second weight, and the second weight is greater than the first weight; a comprehensive road adhesion coefficient determination module, configured to determine a comprehensive road adhesion coefficient according to the visual modal road adhesion coefficient, the dynamic modal road adhesion coefficient, the humidity modal road adhesion coefficient, the first weight, the second weight and the third weight; The automatic emergency braking control module is used to generate an automatic emergency braking control instruction according to the comprehensive road adhesion coefficient, and perform automatic emergency braking control on the vehicle according to the automatic emergency braking control instruction.

9. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the vehicle automatic emergency braking control method as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle automatic emergency braking control method according to any one of claims 1 to 7 is implemented.

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