Control method of vehicle braking system, vehicle, and storage medium

Abstract

The application discloses a control method of a vehicle braking system, a vehicle and a storage medium. The method comprises the following steps: obtaining state information and environment perception information of the vehicle; identifying a scene in which the vehicle is located according to the state information, and obtaining a scene identification result; determining a working state of the vehicle braking system according to the state information and the environment perception information, wherein the working state is used to indicate whether the vehicle braking system works normally; in response to the working state indicating that the vehicle braking system works normally, determining a braking trigger parameter according to the scene identification result; and controlling the vehicle braking system based on the scene identification result and the braking trigger parameter. The application solves the technical problem that the existing braking system has a single and fixed braking mode, and it is difficult to balance the effectiveness of braking response and the smoothness of driving in different driving scenes, thereby leading to low safety performance of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a control method for a vehicle braking system, a vehicle, and a storage medium. Background Technology

[0002] As a core component of the automotive active safety system, the Automatic Emergency Braking (AEB) system aims to monitor obstacles ahead in real time through onboard sensors and automatically trigger the vehicle's brakes if the driver fails to take timely braking action, thereby mitigating or avoiding collisions.

[0003] However, existing AEB technology still has significant shortcomings when dealing with complex and ever-changing real-world road environments. On the one hand, existing systems mostly use fixed trigger thresholds for braking decisions, lacking the ability to adapt to different driving scenarios. This makes it difficult to balance the timeliness of braking response with the smoothness of driving, and it is prone to false triggering or missed triggering. On the other hand, existing systems often lack real-time perception and evaluation mechanisms for sensor operating status. The system cannot accurately determine whether the AEB function has failed, which may lead to the execution of incorrect braking commands or failure to brake in time when sensor data is distorted, thereby reducing the reliability of the vehicle's active safety performance.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a control method for a vehicle braking system, a vehicle, and a storage medium to at least solve the technical problem that existing braking systems have a single and fixed braking mode, making it difficult to balance the effectiveness of braking response and the smoothness of driving in different driving scenarios, thus resulting in low vehicle safety performance.

[0006] According to one aspect of the embodiments of this application, a control method for a vehicle braking system is provided, comprising: acquiring vehicle state information and environmental perception information; identifying the scene in which the vehicle is located based on the state information to obtain a scene recognition result; determining the operating state of the vehicle braking system based on the state information and environmental perception information, wherein the operating state is used to indicate whether the vehicle braking system is working normally; in response to the operating state indicating that the vehicle braking system is working normally, determining braking trigger parameters based on the scene recognition result; and controlling the vehicle braking system based on the scene recognition result and the braking trigger parameters.

[0007] Optionally, the status information includes the vehicle's speed, acceleration, and braking status. The scene recognition result is obtained by identifying the scene of the vehicle based on the status information, including: determining the vehicle's driving state based on the vehicle speed, acceleration, and braking status; and determining the scene recognition result based on the driving state.

[0008] Optionally, the environmental perception information includes meteorological environmental information and obstacle status. The meteorological environmental information includes weather status. The scene recognition result is determined based on the driving state as follows: in response to the driving state being a first state, the scene recognition result is determined to be a speed switching scene, wherein the first state indicates that the vehicle speed decreases from a first speed to a second speed and the vehicle is in a braking state, the first speed is higher than a preset safe speed, and the second speed is lower than a preset safe speed; in response to the driving state not being a first state and the weather status being preset rain or snow weather, the scene recognition result is determined to be a rain or snow scene; in response to the driving state being a second state and the weather status not being preset rain or snow weather, the scene recognition result is determined to be a high-speed cruise scene; in response to the driving state being a third state, the obstacle status being an obstacle accumulation state, and the weather status not being preset rain or snow weather, the scene recognition result is determined to be an urban congestion scene, wherein the vehicle speed corresponding to the second state is greater than the vehicle speed corresponding to the third state, and the obstacle accumulation state indicates that there are multiple continuously moving obstacles within the target range of the vehicle.

[0009] Optionally, the meteorological environment information also includes rainfall and snowfall and meteorological environment images, and the status information also includes the wiper setting. Determining the operating status of the vehicle braking system based on the status information and environmental perception information includes: determining the initial coverage rate based on the rainfall and snowfall; correcting the initial coverage rate based on the wiper setting and meteorological environment images to obtain the target coverage rate, wherein the target coverage rate is used to represent the failure probability of the vehicle braking system; and determining the operating status of the vehicle braking system based on the target coverage rate.

[0010] Optionally, determining the initial coverage rate based on the amount of rain and snow includes: determining the initial coverage rate as a first coverage rate in response to the amount of rain and snow being less than a first rain and snow threshold; determining the initial coverage rate as a second coverage rate in response to the amount of rain and snow being greater than or equal to the first rain and snow threshold and less than a second rain and snow threshold; and determining the initial coverage rate as a third coverage rate in response to the amount of rain and snow being greater than or equal to the second rain and snow threshold, wherein the first coverage rate is less than the second coverage rate and the third coverage rate is greater than the second coverage rate.

[0011] Optionally, determining the braking trigger parameters based on the scene recognition result includes: determining a first braking trigger parameter in response to the scene recognition result being a high-speed cruise scene, wherein the first braking trigger parameter includes a first collision time threshold, a first deceleration threshold, and a first braking intensity; determining a second braking trigger parameter in response to the scene recognition result being an urban congestion scene, wherein the second braking trigger parameter includes a second collision time threshold, a second deceleration threshold, and a second braking intensity; determining a third braking trigger parameter in response to the scene recognition result being a rain / snow scene, wherein the third braking trigger parameter includes a third collision time threshold, a third deceleration threshold, and a third braking intensity; and determining a fourth braking trigger parameter in response to the scene recognition result being a speed switching scene, wherein the fourth braking trigger parameter includes a fourth braking intensity.

[0012] Optionally, controlling the vehicle braking system based on the scene recognition result and braking trigger parameters includes: responding to the scene recognition result being a high-speed cruise scenario, the vehicle's current collision time being less than or equal to a first collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to a first deceleration threshold, braking control of the vehicle braking system based on a first braking intensity; responding to the scene recognition result being an urban congestion scenario, the vehicle's current collision time being less than or equal to a second collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to a second deceleration threshold, braking control of the vehicle braking system based on a second braking intensity; responding to the scene recognition result being a rain / snow scenario, the vehicle's current collision time being less than or equal to a third collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to a third deceleration threshold, braking control of the vehicle braking system based on a third braking intensity; and responding to the scene recognition result being a speed switching scenario, continuously braking control of the vehicle braking system based on a fourth braking intensity until the collision hazard is eliminated or the vehicle comes to a stop.

[0013] Optionally, the method further includes: generating target reminder information based on the operating status of the vehicle braking system; playing the target reminder information through an acoustic reminder device; and / or displaying the target reminder information through an optical display device.

[0014] According to another aspect of the embodiments of this application, a control device for a vehicle braking system is also provided, comprising: an acquisition module for acquiring vehicle status information and environmental perception information; an identification module for identifying the scene in which the vehicle is located based on the status information and obtaining a scene identification result; a first determination module for determining the operating state of the vehicle braking system based on the status information and environmental perception information, wherein the operating state indicates whether the vehicle braking system is operating normally; a second determination module for determining braking trigger parameters based on the scene identification result in response to the operating state indicating that the vehicle braking system is operating normally; and a control module for controlling the vehicle braking system based on the scene identification result and the braking trigger parameters.

[0015] In this embodiment, a vehicle braking system control method is adopted. This method acquires vehicle status information and environmental perception information; identifies the vehicle's current scenario based on the status information, obtaining a scenario identification result; determines the operating state of the vehicle braking system based on the status information and environmental perception information, where the operating state indicates whether the vehicle braking system is operating normally; responds to the operating state indicating normal operation of the vehicle braking system, determines braking trigger parameters based on the scenario identification result; and controls the vehicle braking system based on the scenario identification result and braking trigger parameters. This achieves the goal of dynamically matching and optimizing braking strategies for different driving scenarios and evaluating system availability in real time. This results in improved driving smoothness while ensuring timely braking response, and effectively reduces false braking and missed braking. Furthermore, it solves the technical problem that existing braking systems have a single and fixed braking mode, making it difficult to balance the effectiveness of braking response and driving smoothness under different driving scenarios, thus leading to lower vehicle safety performance. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a hardware structure block diagram of a computing terminal for an optional control method for a vehicle braking system according to an embodiment of this application.

[0018] Figure 2 This is a flowchart of a control method for a vehicle braking system according to an embodiment of this application;

[0019] Figure 3 This is a structural block diagram of an optional vehicle braking control system according to an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of an optional vehicle braking system control method according to an embodiment of this application;

[0021] Figure 5 This is a flowchart illustrating the system sensor failure determination of an optional vehicle braking system control method according to an embodiment of this application.

[0022] Figure 6 This is a speed switching flowchart of an optional vehicle braking system control method according to an embodiment of this application;

[0023] Figure 7 This is a structural block diagram of a control device for a vehicle braking system according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] As a core component of the automotive active safety system, Automatic Emergency Braking (AEB) primarily relies on onboard sensors to perceive obstacles ahead in real time and automatically triggers the vehicle's braking if the driver fails to take timely braking measures, thereby effectively mitigating or avoiding collisions. However, existing AEB technology still has significant shortcomings in practical applications: First, traditional systems often use fixed trigger thresholds, lacking dynamic adaptability to different driving scenarios such as high-speed cruising, urban congestion, and rainy / snowy weather. This can lead to ineffective obstacle avoidance due to response lag in high-speed scenarios, or false braking due to oversensitivity in urban congestion, thus causing safety hazards. Second, existing systems have weak adaptability to specific environments such as heavy rain and fog. Sensors are prone to blinding due to environmental interference, and there is a lack of accurate mechanisms for determining system failure states, making it difficult to guarantee driving safety under specific conditions. In addition, some systems are prone to drastic fluctuations in braking intensity during the switching between high-speed and low-speed braking modes, which not only reduces the smoothness of vehicle driving but may also increase the risk of secondary collisions. While some current technical solutions, such as neural network information processing or blind spot detection technology, have achieved optimizations in single areas, they have not yet formed an integrated solution that combines intelligent scene recognition, accurate failure determination, and smooth switching between high and low speeds. This makes it difficult to comprehensively address issues such as false triggering, missed triggering, and poor environmental adaptability, and thus fails to meet the high-level requirements of automotive active safety. Therefore, developing an automatic emergency braking system and control method for vehicles that can adapt to multiple driving scenarios, possesses self-failure determination capabilities, and achieves smooth switching between high and low speed braking modes has become a crucial issue that urgently needs to be addressed by those skilled in the art.

[0027] According to an embodiment of this application, a method embodiment for controlling a vehicle braking system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a hardware structure block diagram of a computing terminal for an optional control method of a vehicle braking system according to an embodiment of this application, such as... Figure 1 As shown, a computing terminal (e.g., a computer terminal, a mobile smart terminal, a vehicle terminal, or a cloud computing virtual terminal) may include: one or more processors 102, a memory 104, and a transmission device 106 configured to implement communication functions. Each processor 102 may include, but is not limited to, a processing component such as a microprocessor (MCU) or a programmable gate array (FPGA).

[0029] The aforementioned computing terminal may further include: a display device 110, an input / output device 108, a Universal Serial Bus (USB) port (which can be used as one of the ports of a computer bus, not shown in the figure), a network interface (not shown in the figure), a power supply (not shown in the figure), and a camera (not shown in the figure). Those skilled in the art will understand that... Figure 1 The structure of the computing terminal shown is for illustrative purposes only and does not impose strict limitations on the structure of the computing terminal described above. For example, the computing terminal may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits may be embodied, in whole or in part, as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the vehicle terminal (or mobile device).

[0031] The memory 104 may be configured to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the vehicle braking system control method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned vehicle braking system control method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a computing terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The transmission device 106 is configured to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computing terminal. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, configured to communicate with the Internet wirelessly.

[0033] Under the above operating environment, the embodiments of this application provide the following: Figure 2 The control method of the vehicle braking system shown is as follows: Figure 2 This is a flowchart of a control method for a vehicle braking system according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following implementation steps S201 to S205.

[0034] Step S201: Obtain vehicle status information and environmental perception information.

[0035] In this embodiment, the vehicle braking control system acquires the vehicle's state information and environmental perception information, and the vehicle braking control system is the executing entity of the control method of the vehicle braking system in this application.

[0036] Among them, status information refers to physical quantity data that characterizes the vehicle's own motion state and handling behavior, such as real-time vehicle speed, longitudinal acceleration, steering wheel angle, brake pedal travel, and windshield wiper working position.

[0037] Environmental perception information refers to data on surrounding traffic participants and meteorological and road conditions obtained by vehicles through external sensors, such as the distance and relative speed of obstacles ahead, current rainfall, and road images captured by forward-facing cameras.

[0038] It can be seen that the vehicle braking control system reads and summarizes the vehicle's own operating parameters and externally perceived environmental parameters from various sensors and actuators at the current moment.

[0039] For example, the vehicle braking control system reads the current vehicle speed as 60 km / h through the vehicle speed sensor, reads the current rainfall as 5 mm / h through the rain sensor, and obtains the road image data ahead from the forward-facing camera.

[0040] Therefore, this application provides a comprehensive and real-time basic data source for subsequent scene recognition and braking decisions.

[0041] Step S202: Identify the scene where the vehicle is located based on the status information and obtain the scene identification result.

[0042] In this embodiment, the vehicle braking control system identifies the scene in which the vehicle is located based on the status information and obtains the scene identification result.

[0043] The scenario refers to the specific driving environment or operating condition in which the vehicle is currently located. The scenario recognition result is a label or classification data that represents the specific scenario in which the vehicle is currently located, such as highway cruising scenario, urban congestion scenario, rain and snow scenario, etc.

[0044] It can be seen that the vehicle braking control system uses preset scene recognition logic to analyze and judge the state information obtained in the aforementioned steps, thereby determining the specific driving scenario category of the vehicle at present.

[0045] For example, the vehicle braking control system detects that the vehicle speed is greater than 60 km / h and there are no continuous obstacles in front, so it identifies the current scene as a high-speed cruise scene and outputs the identification result.

[0046] Therefore, this application achieves accurate classification of the vehicle's current driving environment, providing a basis for the subsequent formulation of differentiated braking strategies. Compared with traditional solutions, this application, by dynamically identifying specific driving scenarios, can more accurately match the current scenario and road conditions, effectively reducing the probability of false or missed triggering of the vehicle's braking system due to poor scenario adaptability.

[0047] Step S203: Determine the operating status of the vehicle braking system based on the status information and environmental perception information, wherein the operating status is used to indicate whether the vehicle braking system is working normally.

[0048] In this embodiment, the vehicle braking control system determines the operating state of the vehicle braking system based on status information and environmental perception information.

[0049] The operating status indicates whether the vehicle's braking system is functioning normally. The operating status reflects whether the vehicle's braking system has the ability to perform braking functions normally under current environmental conditions. The operating status can be either ineffective or functioning normally.

[0050] It can be seen that, based on the status information and environmental perception information of the aforementioned steps, the vehicle braking control system assesses whether the current braking system has reliable working conditions through preset logical judgments or model analysis.

[0051] For example, the vehicle braking control system detects that the current rainfall is 30 mm / h and the windshield wipers are in high speed mode. Based on the image recognition results, it determines that the environmental visibility is extremely low, and thus determines that the current working state of the vehicle braking system is malfunctioning.

[0052] Therefore, this application enables real-time assessment of the availability of the vehicle braking system under special environments, avoiding blind execution of braking commands in cases of sensor failure such as sensor blindness. Compared with traditional solutions, this application avoids blindly triggering braking when the vehicle braking system is blind by assessing sensor availability in real time under special environments, thereby effectively preventing the risk of false triggering or missed triggering due to misjudgment.

[0053] Step S204: In response to the working status indicating that the vehicle braking system is working normally, determine the braking trigger parameters based on the scene recognition result.

[0054] In this embodiment of the application, when the working status indicates that the vehicle braking system is working normally, the vehicle braking control system will determine the braking trigger parameters based on the scene recognition result.

[0055] Braking trigger parameters refer to the key numerical or logical conditions used to determine whether to activate automatic emergency braking and to determine the braking intensity. These typically include the Estimated Time To Collision (ETTC), Acceleration Safe (Asafe) deceleration required for collision avoidance, and target braking deceleration. For example, in high-speed cruising scenarios, ETTC ≤ 2.5s and braking intensity 0.6g; in urban congestion scenarios, ETTC ≤ 1.5s and braking intensity 0.4g; and in rain and snow scenarios, ETTC ≤ 2.0s and braking intensity 0.3-0.5g.

[0056] As can be seen, assuming that the vehicle braking system is working normally, the specific braking trigger parameters corresponding to the current scene are determined based on the scene recognition results output by the previous steps.

[0057] Therefore, this application achieves dynamic adaptation of braking strategies for different driving scenarios, effectively balancing the timeliness of braking response and driving smoothness. Compared with traditional solutions, this application avoids rigid control of fixed thresholds under complex road conditions by dynamically matching braking parameters based on the availability status of the vehicle's braking control system and the specific driving scenario, thereby effectively reducing the probability of false triggering and missed triggering.

[0058] Step S205: Control the vehicle braking system based on the scene recognition results and braking trigger parameters.

[0059] In this embodiment, the vehicle braking control system controls the vehicle braking system based on scene recognition results and braking trigger parameters.

[0060] It can be seen that after obtaining the scene recognition results and the corresponding braking trigger parameters, the vehicle braking control system sends specific braking control commands to the vehicle braking system to implement automatic emergency braking operations.

[0061] For example, the vehicle braking control system identifies a high-speed cruise scenario and meets the braking triggering conditions, and then sends a command with a braking intensity of 0.6g to the vehicle braking system, which executes the command to decelerate the vehicle.

[0062] Therefore, this application achieves precise braking execution based on dynamic scene parameters, ensuring that the braking action matches the current driving environment and risk level.

[0063] In summary, this application employs a vehicle braking system control method that acquires vehicle state information and environmental perception information. This provides a comprehensive and real-time data source for subsequent scene recognition and braking decisions. By identifying the vehicle's current scene based on the state information, a scene recognition result is obtained, enabling accurate classification of the vehicle's current driving environment and providing a basis for the formulation of differentiated braking strategies. The operating state of the vehicle braking system is determined based on the state and environmental perception information, indicating whether the system is functioning normally. This allows for real-time assessment of system availability in special environments, avoiding blind execution of braking commands in cases of sensor failure such as blindness, and effectively preventing the risk of false or missed triggering due to misjudgment. Responding to the operating state indicating normal operation of the vehicle braking system, braking trigger parameters are determined based on the scene recognition result. This enables dynamic adaptation of braking strategies for different driving scenarios, effectively balancing the timeliness of braking response and driving smoothness. Controlling the vehicle braking system based on the scene recognition result and braking trigger parameters achieves precise braking execution based on dynamic scene parameters, ensuring that the braking action matches the current driving environment and risk level.

[0064] In this embodiment, a vehicle braking system control method is adopted. This method acquires vehicle status information and environmental perception information; identifies the vehicle's current scenario based on the status information, obtaining a scenario identification result; determines the operating state of the vehicle braking system based on the status information and environmental perception information, where the operating state indicates whether the vehicle braking system is operating normally; responds to the operating state indicating normal operation of the vehicle braking system, determines braking trigger parameters based on the scenario identification result; and controls the vehicle braking system based on the scenario identification result and braking trigger parameters. This achieves the goal of dynamically matching and optimizing braking strategies for different driving scenarios and evaluating system availability in real time. This results in improved driving smoothness while ensuring timely braking response, and effectively reduces false braking and missed braking. Furthermore, it solves the technical problem that existing braking systems have a single and fixed braking mode, making it difficult to balance the effectiveness of braking response and driving smoothness under different driving scenarios, thus leading to lower vehicle safety performance.

[0065] The methods described in the embodiments of this application will be further described below.

[0066] Optionally, the state information includes the vehicle's speed, acceleration, and braking status. Based on the state information, the scene in which the vehicle is located is identified, and the scene identification results include:

[0067] Step S2021: Determine the vehicle's driving state based on vehicle speed, acceleration, and braking status;

[0068] Step S2022: Determine the scene recognition result based on the driving status.

[0069] Among them, braking status refers to the signal status that reflects the current operation of the vehicle's braking system or the driver's intention to brake. It is used to identify whether the vehicle is performing a braking operation and the intensity or mode of braking, and is a key input parameter for judging the vehicle's motion behavior.

[0070] Braking states include, for example, no braking (brake pedal not depressed or braking system not applying braking force), normal braking (brake pedal partially depressed or braking system applying moderate braking force), and emergency braking (brake pedal quickly depressed to the floor or braking system applying large braking force). Some vehicle braking systems also include a brake holding state.

[0071] In this embodiment, the vehicle braking control system first determines the vehicle's driving state based on vehicle speed, acceleration, and braking status.

[0072] Driving state refers to the macroscopic motion characteristics of a vehicle, comprehensively reflected by its current kinematic parameters (such as speed and acceleration) and operational status (such as whether braking is in progress), and is used to characterize the vehicle's current dynamic behavior pattern. Driving states include, for example, high-speed driving, low-speed driving, emergency braking, or stationary standby.

[0073] It can be seen that the vehicle braking control system directly uses the collected vehicle speed value, acceleration value and the current working status signal of the vehicle braking system to determine the specific driving state of the vehicle through logical judgment or classification algorithm.

[0074] For example, the vehicle braking control system detects that the vehicle speed is 80 km / h, the acceleration is 0 m / s², and the braking state is not braking, thereby determining that the current driving state of the vehicle is high-speed driving.

[0075] Therefore, this application provides objective and accurate underlying data support for subsequent scene recognition by quantifying the motion characteristics of vehicles.

[0076] Then, the vehicle braking control system determines the scene recognition result based on the driving status.

[0077] It can be seen that the vehicle braking control system matches the preset scene classification rules or neural network model based on the driving state obtained above, and finally outputs the specific scene recognition result of the current vehicle.

[0078] For example, the vehicle braking control system determines that the current driving state is high-speed driving based on a preset model, and combined with the camera's recognition that there are no continuous obstacles in front, determines that the current scene recognition result is a high-speed cruise scene.

[0079] Furthermore, the preset model can be a scene recognition neural network based on deep learning, which is used to fuse multi-dimensional sensor data such as vehicle speed, acceleration, and braking status collected in real time by the vehicle, as well as environmental features extracted by the camera. Through nonlinear mapping relationships, it identifies and outputs the specific driving scenario category in which the vehicle is currently located, such as highway cruising, urban congestion, or rain and snow scenarios, thereby providing accurate scenario basis for subsequent dynamic adjustment of braking trigger parameters.

[0080] In terms of updates, the preset model can be trained and optimized by the cloud server using stored historical braking data of multiple vehicle models (including comfort braking and emergency braking status information). The optimized new model version is periodically (e.g., quarterly) sent to the scene recognition module on the vehicle via wireless communication network to achieve continuous iteration of model parameters and adaptive improvement of braking strategy.

[0081] Thus, this application realizes the transformation from motion data to scene, enabling the vehicle braking system to execute subsequent control strategies in a targeted manner.

[0082] Optionally, the environmental perception information includes meteorological environmental information and obstacle status. The meteorological environmental information includes weather conditions. The scene recognition result determined based on the driving status includes:

[0083] Step S20221: In response to the driving state being the first state, the scene recognition result is determined to be a speed switching scene. The first state indicates that the vehicle speed is reduced from the first speed to the second speed and the vehicle is in a braking state. The first speed is higher than the preset safe speed and the second speed is lower than the preset safe speed.

[0084] Step S20222: In response to the driving state not being the first state and the weather state being the preset rain and snow weather, the scene recognition result is determined to be a rain and snow scene.

[0085] Step S20223: In response to the driving state being the second state and the weather state not being the preset rain or snow weather, the scene recognition result is determined to be a high-speed cruise scene.

[0086] Step S20224: In response to the driving state being the third state, the obstacle state being the obstacle aggregation state, and the weather state not being the preset rain or snow weather, the scene recognition result is determined to be an urban traffic congestion scene. Here, the vehicle speed corresponding to the second state is greater than the vehicle speed corresponding to the third state, and the obstacle aggregation state is used to indicate that there are multiple continuously moving obstacles within the target range of the vehicle.

[0087] Meteorological environmental information refers to the data set collected by vehicle-mounted sensors (such as rain sensors, cameras, and temperature sensors) that reflects the current external atmospheric environment conditions of the vehicle. It is mainly used to assess the degree of interference of the environment on the vehicle's driving stability and sensor perception capabilities. Weather status refers to the specific weather category or level determined based on meteorological environmental information. It is usually used as a key branch condition in scene recognition logic to distinguish driving scenarios under different weather conditions (such as normal weather and preset rain and snow weather).

[0088] Obstacle status refers to a comprehensive description of the spatial distribution, motion characteristics, and density of potential collision targets (such as other vehicles, pedestrians, non-motorized vehicles, static objects, etc.) on the road, obtained through perception and analysis of the environment ahead. It is used to assess the complexity of the current traffic environment and the risk of collision.

[0089] In this embodiment of the application, when the driving state is the first state, the vehicle braking control system determines the scene recognition result as a speed switching scene.

[0090] The first state indicates that the vehicle speed decreases from a first speed to a second speed and the vehicle is in a braking state. The first speed is higher than the preset safe speed and the second speed is lower than the preset safe speed.

[0091] A preset safe speed refers to a speed threshold pre-set within the vehicle's braking system. This threshold defines the boundary between high-speed and low-speed scenarios and triggers specific control logic for braking smoothness. For example, a preset safe speed could be set to 30 km / h. When the vehicle speed decreases from above 30 km / h to below 30 km / h, this threshold is crossed, triggering the corresponding scenario recognition logic.

[0092] Speed ​​switching scenarios (i.e. high-low speed switching scenarios) refer to the driving state corresponding to the vehicle speed crossing a preset safe speed threshold during braking. This state mainly focuses on the smoothness control during braking, rather than simply the need for collision avoidance.

[0093] It can be seen that when the vehicle braking control system detects that the vehicle is currently in the process of deceleration and braking, and the real-time vehicle speed decreases from above the preset safe speed to below the preset safe speed, this specific transition phase is identified as a speed switching scenario.

[0094] For example, if the vehicle's current speed is 50 km / h and it is braking to decelerate, and the speed is about to exceed the threshold of 30 km / h, the vehicle braking control system determines that it is currently in the braking phase of transitioning from high speed to low speed, thus identifying it as a speed switching scenario.

[0095] Therefore, this application accurately identifies the key transition phases in the braking process, providing a scenario basis for subsequently adopting specific smoothness control strategies (such as maintaining continuous deceleration).

[0096] When the driving state is not the first state and the weather state is the preset rain and snow weather, the vehicle braking control system determines the scene recognition result as a rain and snow scene.

[0097] Preset rain and snow weather refers to weather conditions that are determined to meet specific rain and snow characteristics by established rules or models, such as rainfall greater than 10 mm / h or rain and fog interference detected by camera images.

[0098] Rain and snow scenarios refer to the driving state when the current weather environment is identified as preset rain and snow weather conditions when the vehicle is not in a speed switching state. In this scenario, the vehicle braking system needs to focus on the increased braking distance and the risk of sideslip caused by the reduced road surface adhesion.

[0099] It can be seen that, after excluding speed switching scenarios, if the system detects that the current weather condition matches the preset rain and snow weather characteristics, it will identify the current scene as a rain and snow scene.

[0100] For example, the vehicle is not in a rapid deceleration transition period from high speed to low speed, but the rain sensor detects a rainfall of 15 mm / h and the windshield wipers are in medium speed mode. The vehicle braking control system determines that the current scenario is rain or snow.

[0101] Therefore, this application identifies and confirms rain and snow scenarios by meteorological conditions, which helps the system to adjust its braking strategy to adapt to slippery road surfaces and prevent vehicle loss of control due to excessive braking.

[0102] When the driving state is in the second state and the weather condition is not the preset rain or snow weather, the vehicle braking control system determines the scene recognition result as a high-speed cruise scene.

[0103] The second state can be set to a driving state with a high speed and no braking operation, such as a speed of 60km / h or higher.

[0104] High-speed cruising refers to a driving state in which a vehicle travels at a relatively high speed and remains relatively stable in non-rainy or snowy weather. In this scenario, the vehicle's braking system needs to balance braking response speed and driving comfort.

[0105] It can be seen that after excluding speed switching scenarios and rain / snow scenarios, if the vehicle's current speed is relatively high and the weather is normal, the scenario is identified as a high-speed cruise scenario.

[0106] For example, the vehicle is traveling at a constant speed of 65 km / h in clear weather, and the vehicle's braking control system determines that the current scenario is a high-speed cruise.

[0107] Therefore, this application distinguishes between high-speed cruising scenarios, which helps to set standardized trigger thresholds for road conditions in high-speed cruising scenarios in the future.

[0108] When the driving state is in the third state, the obstacle state is the obstacle accumulation state, and the weather state is not the preset rain or snow weather, the vehicle braking control system determines the scene recognition result as an urban traffic congestion scene.

[0109] In this context, the vehicle speed corresponding to the second state is greater than the vehicle speed corresponding to the third state, and the obstacle aggregation state is used to indicate that there are multiple continuously moving obstacles within the target range of the vehicle.

[0110] The third state can be set to a driving state with a low speed and no braking operation. An example of the vehicle speed corresponding to the third state is below 30km / h.

[0111] Urban congestion scenarios refer to driving at a low speed (third state) when there are multiple continuously moving obstacles ahead (obstacle cluster state) in non-rainy / snowy weather and non-speed switching conditions. In this scenario, the vehicle's braking system needs to focus on preventing accidental braking caused by frequent acceleration and deceleration of the vehicle in front.

[0112] It can be seen that, after excluding other scenarios, if the vehicle is currently in a low-speed driving state (third state), and the environmental perception shows that there are multiple continuously moving obstacles ahead (obstacle cluster), and the weather is normal, then the scenario is identified as an urban traffic congestion scenario.

[0113] For example, a vehicle is following another vehicle at a low speed of 20 km / h, with three vehicles in front of it closely spaced. The weather is sunny, and the vehicle's braking control system determines that the current situation is an urban traffic jam.

[0114] Therefore, by identifying congested road conditions, this application helps to set a more conservative late-trigger mode for the vehicle's braking system.

[0115] Optionally, meteorological environmental information also includes rainfall and snowfall amounts and meteorological environmental images, and status information includes windshield wiper settings. Determining the operating status of the vehicle braking system based on the status information and environmental perception information includes:

[0116] Step S2031: Determine the initial coverage rate based on the amount of rain and snow;

[0117] Step S2032: Correct the initial coverage rate based on the wiper setting and the weather environment image to obtain the target coverage rate, wherein the target coverage rate is used to represent the failure probability of the vehicle braking system.

[0118] Step S2033: Determine the operating status of the vehicle braking system based on the target coverage.

[0119] Rainfall and snowfall refers to the cumulative depth or intensity of precipitation (rain or snow) per unit time detected in real time by vehicle-mounted rain sensors. It is used to quantify the current precipitation level and is the basic data for assessing the impact of meteorological environment on sensors and vehicle operation.

[0120] Meteorological environment images refer to visual image data collected in real time by vehicle cameras, which includes current external weather conditions (such as raindrops, snowflakes, fog, visibility, etc.). These images are used to extract meteorological features through image recognition algorithms to help correct sensor data.

[0121] In this embodiment of the application, the vehicle braking control system first determines the initial coverage based on the amount of rain and snow.

[0122] The initial coverage rate refers to the preliminary probability estimate of the vehicle braking system (AEB system) caused by environmental interference due to sensing failure or braking performance degradation, which is directly mapped from real-time collected rain and snow data. The initial coverage rate reflects the basic impact of a single meteorological factor on the availability of the vehicle braking system.

[0123] It can be seen that the system reads the current rain and snow amount value obtained by the rain sensor and determines a basic failure probability value as the initial coverage rate.

[0124] For example, the rain sensor detects that the current rainfall is 15 mm / h, and the vehicle braking control system determines that the initial coverage corresponding to this rainfall is 60%.

[0125] Therefore, this application provides basic data support for a more accurate environmental adaptability assessment by quantifying the impact of rain and snow on the vehicle braking system.

[0126] Then, the vehicle braking control system corrects the initial coverage rate based on the wiper setting and the weather environment image to obtain the target coverage rate, which is used to represent the failure probability of the vehicle braking system.

[0127] The target coverage rate refers to the final estimated failure probability obtained by comprehensively correcting the initial coverage rate based on the actual operating speed of the windshield wipers and visual features in the meteorological image. The target coverage rate more comprehensively reflects the actual perception capability of the vehicle's braking system under complex weather conditions. For example, with an initial coverage rate of 60%, if the wipers are at a high speed and the image detects severe rain and fog interference, the corrected target coverage rate may increase to 90%. If the wipers are at a low speed and the image is clear, the corrected target coverage rate may remain at 60% or slightly lower.

[0128] It can be seen that the vehicle braking control system analyzes the current gear status of the windshield wipers and the meteorological environment image features obtained from the camera, and performs weighted or logical correction on the initial coverage rate determined in the aforementioned steps, thereby generating a target coverage rate that better reflects the actual failure risk.

[0129] For example, with an initial coverage rate of 60%, the vehicle braking control system detects that the windshield wipers are in medium speed mode, and image analysis shows that the rain and fog interference is moderate. Therefore, the initial coverage rate is corrected and determined to be 75% as the target coverage rate.

[0130] Therefore, this application improves the accuracy and reliability of determining vehicle braking system failure under specific environments through multi-dimensional data fusion and correction.

[0131] Finally, the vehicle braking control system determines the operating status of the vehicle braking system based on the target coverage.

[0132] The operational status of a vehicle braking system refers to whether the system is currently available, limited, or completely unavailable, based on whether the target coverage rate has reached a preset threshold. The operational status directly determines whether the system sends a failure signal and whether it performs braking actions. Operational statuses include, for example, normal operation (target coverage rate below 70%), limited functionality (target coverage rate within a specific range, such as 70%-90%), or unavailable (target coverage rate greater than or equal to 90%).

[0133] It can be seen that the vehicle braking control system compares the calculated target coverage with the preset judgment threshold, and determines whether the vehicle braking system is currently in a normal working state or a failed state based on the comparison result.

[0134] For example, if the target coverage rate is 75%, which is lower than the preset failure threshold of 90%, the vehicle braking control system determines that the vehicle braking system is functioning normally and allows the braking logic to continue. If the target coverage rate is 92%, it is determined to be a failure.

[0135] Therefore, this application enables accurate determination of the failure state of the vehicle braking system, avoiding false triggering or missed triggering under specific weather conditions, and ensuring driving safety.

[0136] Optionally, determining the initial coverage based on rainfall and snowfall includes:

[0137] Step S20311: In response to the rain and snow amount being less than the first rain and snow amount threshold, the initial coverage rate is determined to be the first coverage rate;

[0138] Step S20312: In response to the fact that the amount of rain and snow is greater than or equal to the first rain and snow threshold and less than the second rain and snow threshold, the initial coverage rate is determined to be the second coverage rate.

[0139] Step S20313: In response to the rain and snow amount being greater than or equal to the second rain and snow amount threshold, the initial coverage rate is determined to be the third coverage rate, wherein the first coverage rate is less than the second coverage rate and the third coverage rate is greater than the second coverage rate.

[0140] In this embodiment of the application, when the amount of rain and snow is less than the first rain and snow threshold, the vehicle braking control system determines the initial coverage rate as the first coverage rate.

[0141] The first precipitation threshold is a preset low precipitation intensity limit for the vehicle's braking control system, used to distinguish between light precipitation environments. For example, the first precipitation threshold is 0 to 10 mm / h; rainfall or snowfall below this value is considered light precipitation.

[0142] The first coverage rate refers to the baseline probability estimate of whether a vehicle's braking system will experience sensor failure or reduced braking performance due to environmental disturbances under light precipitation conditions. This baseline probability estimate is relatively low. For example, a first coverage rate of 30% indicates a low risk of vehicle braking system failure under light precipitation.

[0143] It can be seen that when the vehicle braking control system detects that the real-time rain and snow amount is lower than the first preset threshold, the first coverage rate is determined as the current initial coverage rate.

[0144] For example, if the rain sensor detects that the current rainfall is 5 mm / h, which is below the first threshold of 10 mm / h, the vehicle braking control system directly determines the initial coverage rate to be 30%.

[0145] Therefore, this application adopts a low failure probability preset for light precipitation scenarios, avoiding overly conservative restrictions on vehicle braking system functions in low-risk environments.

[0146] When the amount of rain and snow is greater than or equal to the first rain and snow threshold and less than the second rain and snow threshold, the vehicle braking control system determines the initial coverage rate to be the second coverage rate.

[0147] The second rain / snow threshold is a preset limit for moderate precipitation intensity in the vehicle's braking control system, used to distinguish moderate precipitation environments. For example, the second rain / snow threshold is set to 30 mm / h, and precipitation between 10 mm / h and 30 mm / h is considered moderate precipitation.

[0148] The second coverage rate refers to the baseline probability estimate of vehicle braking system failure or reduced braking performance due to environmental disturbances under moderate precipitation conditions. For example, a second coverage rate of 60% indicates a moderate risk of vehicle braking system failure under moderate precipitation.

[0149] It can be seen that when the vehicle braking control system detects that the real-time rain and snow amount is greater than or equal to the first threshold and less than the second threshold, the preset medium probability value (second coverage) is determined as the current initial coverage, reflecting the medium impact of moderate precipitation on the performance of the vehicle braking system.

[0150] For example, the rain sensor detects that the current rainfall is 20 mm / h, which is in the range of 10 mm / h to 30 mm / h, and the vehicle braking control system determines that the initial coverage is 60%.

[0151] Therefore, this application enables the vehicle braking system to better meet the actual safety requirements during moderate precipitation by setting a tiered failure probability.

[0152] When the amount of rain and snow is greater than or equal to the second rain and snow threshold, the vehicle braking control system determines the initial coverage rate to be the third coverage rate.

[0153] The third coverage rate refers to the high-probability failure estimate determined when the real-time rainfall and snowfall reach or exceed the second preset threshold. This value represents an extremely high risk of vehicle braking system failure under severe precipitation conditions, typically approaching or reaching the critical point where the vehicle braking system becomes unusable. The first coverage rate is less than the second coverage rate, and the third coverage rate is greater than the second coverage rate.

[0154] It can be seen that when the vehicle braking control system detects that the real-time rain and snow amount is greater than or equal to the second preset threshold, it determines the preset high probability value as the current initial coverage rate, prompting the vehicle braking system to enter the high-risk failure judgment logic.

[0155] For example, the rain sensor detects that the current rainfall is 40 mm / h, which exceeds the second threshold of 30 mm / h, and the vehicle braking control system determines that the initial coverage is 90%.

[0156] Therefore, this application overestimates the probability of failure under severe weather conditions, prompting the vehicle braking system to enter failure protection or limitation mode in advance, thereby preventing serious safety accidents caused by perceived failure.

[0157] Optionally, determining the braking trigger parameters based on the scene recognition results includes:

[0158] Step S2041: In response to the scene recognition result being a high-speed cruise scene, determine the first braking trigger parameters, wherein the first braking trigger parameters include a first collision time threshold, a first deceleration threshold, and a first braking intensity.

[0159] Step S2042: In response to the scene recognition result being an urban congestion scene, determine the second braking trigger parameters, wherein the second braking trigger parameters include a second collision time threshold, a second deceleration threshold, and a second braking intensity.

[0160] Step S2043: In response to the scene recognition result being a rain and snow scene, determine the third braking trigger parameters, wherein the third braking trigger parameters include the third collision time threshold, the third deceleration threshold, and the third braking intensity.

[0161] Step S2044: In response to the scene recognition result being a speed switching scene, determine the fourth braking trigger parameter, wherein the fourth braking trigger parameter includes the fourth braking intensity.

[0162] In this embodiment of the application, when the scene recognition result is a high-speed cruise scene, the vehicle braking control system determines the first braking trigger parameter.

[0163] The first braking trigger parameter refers to the set of strict braking standards set by the vehicle braking control system to ensure sufficient braking distance and safety when the scenario is identified as a high-speed cruise scenario. These standards include a high collision time threshold, a high deceleration requirement, and a strong initial braking intensity.

[0164] The first braking trigger parameters include a first collision time threshold, a first deceleration threshold, and a first braking intensity. For example, the first braking trigger parameters include a higher first collision time threshold (ETTC) of 2.5 seconds, a higher first deceleration threshold (Asafe) of 4 m / s², and a stronger first braking intensity of 0.6g. An early triggering mode is employed to allow the vehicle's braking system to intervene earlier, when there is sufficient time and distance from the obstacle, ensuring sufficient braking distance at high speeds.

[0165] It can be seen that when the vehicle braking control system determines that the vehicle is currently in a high-speed cruising scenario, the vehicle braking control system directly calls the preset high-speed scenario-specific parameters, namely the first collision time threshold, the first deceleration threshold, and the first braking intensity, as the reference parameters for subsequent braking calculations.

[0166] For example, the scene is identified as a high-speed cruise scene, and the vehicle braking control system determines the braking trigger parameters as ETTC≤2.5s, Asafe≥4m / s², and braking intensity 0.6g.

[0167] Therefore, this application sets a high safety redundancy parameter to ensure that the vehicle braking system can intervene in advance and generate sufficient braking force to avoid collisions in high-speed scenarios.

[0168] When the scene recognition result is an urban traffic congestion scene, the vehicle braking control system determines the second braking trigger parameter.

[0169] The second braking trigger parameter refers to a relatively lenient set of braking standards set by the vehicle braking control system to reduce the probability of false triggering and improve driving smoothness when the scenario is identified as urban congestion. These standards include a lower collision time threshold, a lower deceleration requirement, and a weaker initial braking intensity.

[0170] The second braking triggering parameters include a second collision time threshold, a second deceleration threshold, and a second braking intensity. For example, the second braking triggering parameters may include a lower second collision time threshold (ETTC) of 1.5 seconds, a lower second deceleration threshold (Asafe) of 3 m / s², and a weaker second braking intensity of 0.4g. A late-triggering mode is employed to delay braking intervention, triggering only when the collision risk is extremely high, thus avoiding false reactions to frequent acceleration and deceleration of vehicles ahead.

[0171] It can be seen that when the vehicle braking control system determines that the vehicle is currently in an urban congestion scenario, the vehicle braking control system directly calls the preset urban congestion scenario-specific parameters, namely the second collision time threshold, the second deceleration threshold, and the second braking intensity, as the reference parameters for subsequent braking calculations.

[0172] For example, when the scene recognition result is an urban traffic congestion scene, the vehicle braking control system determines the braking trigger parameters as ETTC≤1.5s, Asafe≥3m / s², and braking intensity 0.4g.

[0173] Therefore, by relaxing the triggering conditions and reducing the braking intensity, this application effectively reduces frequent false braking in urban congested road conditions and improves driving comfort.

[0174] When the scene recognition result is a rain or snow scene, the vehicle braking control system determines the third braking trigger parameter.

[0175] Among them, the third braking trigger parameter refers to the set of corrected braking standards set by the vehicle braking control system to adapt to the anti-skid requirements of wet and slippery road surfaces and overcome sensor interference when the scene is identified as rain or snow. It includes the corrected collision time threshold, moderate deceleration requirements and low braking intensity.

[0176] The third braking trigger parameters include the third collision time threshold, the third deceleration threshold, and the third braking intensity. For example, the third braking trigger parameters include: a third collision time threshold (ETTC) of 2.0 seconds, a third deceleration threshold (Asafe) of 3.5 m / s², and a third braking intensity of a flexible 0.3g to 0.5g.

[0177] It can be seen that when the vehicle braking control system determines that the vehicle is currently in a rain or snow scene, the vehicle braking control system directly calls the preset rain and snow scene-specific parameters, namely the third collision time threshold, the third deceleration threshold, and the third braking intensity, as the reference parameters for subsequent braking calculations.

[0178] For example, when the scene recognition result is a rain and snow scene, the vehicle braking control system determines the braking trigger parameters as ETTC≤2.0s and Asafe≥3.5m / s², and determines the braking intensity as 0.4g based on the rain and snow intensity.

[0179] Therefore, this application balances braking safety and vehicle stability on rainy and snowy roads by reducing braking intensity and modifying the trigger threshold, preventing skidding caused by excessive braking.

[0180] When the scene recognition result is a speed switching scene, the vehicle braking control system determines the fourth braking trigger parameter.

[0181] The fourth braking trigger parameter refers to the parameter used to maintain braking continuity when a speed switching scenario is identified. It mainly involves the non-zero deceleration requirement to be maintained when the vehicle speed exceeds the preset safe speed, so as to ensure smooth braking.

[0182] The fourth braking trigger parameter includes the fourth braking intensity. For example, the fourth braking intensity is maintained between 0.2g and 0.3g, and braking is applied at the fourth braking intensity when the vehicle speed drops from a higher speed to a critical value of 30km / h until the danger is eliminated or the vehicle comes to a stop.

[0183] It can be seen that when the vehicle is currently in a speed switching scenario, the vehicle braking control system determines a fourth braking trigger parameter for smooth transition. This fourth braking trigger parameter mainly reflects the specific braking intensity maintenance requirements.

[0184] For example, during the process of a vehicle decelerating from 60km / h to 20km / h, the scene recognition result is a speed switching scene, and the vehicle braking control system continuously brakes with a braking intensity of 0.25g to avoid braking interruption.

[0185] Therefore, this application solves the problem of braking intensity fluctuation when switching speed modes by specifically setting the switching scene parameters, thereby improving the smoothness of the braking process.

[0186] Optionally, controlling the vehicle braking system based on scene recognition results and braking trigger parameters includes:

[0187] Step S2051: In response to the scene recognition result being a high-speed cruise scene, the vehicle's current collision time being less than or equal to a first collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to a first deceleration threshold, braking control of the vehicle braking system is performed based on the first braking intensity.

[0188] Step S2052: In response to the scene recognition result being an urban congestion scene, the vehicle's current collision time being less than or equal to the second collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to the second deceleration threshold, braking control of the vehicle braking system is performed based on the second braking intensity.

[0189] Step S2053: In response to the scene recognition result being a rain and snow scene, the vehicle's current collision time being less than or equal to the third collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to the third deceleration threshold, braking control of the vehicle braking system is performed based on the third braking intensity.

[0190] In step S2054, in response to the scene recognition result being a speed switching scene, the vehicle braking system is continuously braked based on the fourth braking intensity until the collision hazard is eliminated or the vehicle comes to a stop.

[0191] In this embodiment of the application, when the scene recognition result is a high-speed cruise scene, the current collision time of the vehicle is less than or equal to the first collision time threshold, and the current collision avoidance deceleration of the vehicle is greater than or equal to the first deceleration threshold, the system performs braking control on the vehicle braking system based on the first braking intensity.

[0192] The current collision time of a vehicle refers to the estimated remaining time of collision calculated in real time based on the relative speed and distance between the vehicle and the obstacle in front.

[0193] The vehicle's current collision avoidance deceleration refers to the longitudinal deceleration value required to avoid a collision, calculated in real time by an algorithm based on the relative motion state between the vehicle and the obstacle in front.

[0194] It can be seen that when the scene recognition result is a high-speed cruise scene, and the vehicle braking control system detects that the current collision time is less than or equal to the first collision time threshold, and the current collision avoidance deceleration requirement is greater than or equal to the first deceleration threshold, the vehicle braking control system directly applies the first braking intensity to perform braking control on the vehicle.

[0195] For example, when the vehicle is cruising at high speed, the current collision time is calculated to be 2.0 seconds (≤2.5 seconds), and the required collision avoidance deceleration is 4.5 m / s² (≥4 m / s²). Based on this, the vehicle braking control system applies braking with a first braking intensity of 0.6g.

[0196] Therefore, this application ensures that sufficient braking force can be quickly established to avoid collisions in high-risk situations through strict threshold judgment and high braking intensity in high-speed cruising scenarios.

[0197] When the scene recognition result is an urban congestion scene, the vehicle's current collision time is less than or equal to the second collision time threshold, and the vehicle's current collision avoidance deceleration is greater than or equal to the second deceleration threshold, the vehicle braking control system performs braking control on the vehicle braking system based on the second braking intensity.

[0198] It can be seen that when the scene recognition result is an urban congestion scene, and the vehicle braking control system detects that the current collision time is less than or equal to the second threshold, and the current collision avoidance deceleration requirement is greater than or equal to the second deceleration threshold, the vehicle braking control system applies the second braking intensity to perform braking control on the vehicle.

[0199] For example, if a vehicle is in a congested urban environment, the calculated collision time is 1.2 seconds (≤1.5 seconds), and the required collision avoidance deceleration is 3.2 m / s² (≥3 m / s²). Based on this, the vehicle braking control system applies a second braking intensity of 0.4 g.

[0200] Therefore, by shortening the trigger time and reducing the braking intensity, this application effectively reduces the number of accidental braking events in urban congestion scenarios and improves driving smoothness.

[0201] When the scene recognition result is a rain or snow scene, the vehicle's current collision time is less than or equal to the third collision time threshold, and the vehicle's current collision avoidance deceleration is greater than or equal to the third deceleration threshold, the vehicle braking control system performs braking control on the vehicle braking system based on the third braking intensity.

[0202] It can be seen that when the scene recognition result is a rain or snow scene, and the vehicle braking control system detects that the current collision time is less than or equal to the third threshold, and the current collision avoidance deceleration requirement is greater than or equal to the third deceleration threshold, the vehicle braking control system applies the third braking intensity to perform braking control on the vehicle.

[0203] For example, in a rain or snow scenario, the current collision time is calculated to be 1.8 seconds (≤2.0 seconds), and the required collision avoidance deceleration is 3.6 m / s² (≥3.5 m / s²). Based on this, the vehicle braking control system applies braking with a third braking intensity of 0.4g.

[0204] Therefore, this application ensures that the vehicle can effectively avoid collisions and maintain driving stability on slippery roads in rain and snow scenarios by modifying the collision time threshold and limiting deceleration.

[0205] When the scene recognition result is a speed switching scene, the vehicle braking control system continuously brakes the vehicle braking system based on the fourth braking intensity until the collision danger is eliminated or the vehicle comes to a stop.

[0206] It can be seen that when the scene recognition result is a speed switching scene, regardless of the current collision time and the specific value of deceleration, the vehicle braking control system continuously brakes the vehicle based on the fourth braking intensity until the collision danger is eliminated or the vehicle comes to a stop.

[0207] For example, when a vehicle speeds down from 32 km / h to 28 km / h, this is identified as a speed change scenario. The vehicle braking control system continues to apply the brakes at a fourth braking intensity of 0.25g until the vehicle comes to a stop or safety is confirmed.

[0208] Therefore, this application avoids braking interruption or shock caused by parameter jumps during high-speed and low-speed mode switching by forced continuous braking, thus improving the smoothness of the switching process.

[0209] Optionally, the method further includes:

[0210] Step S206: Generate target reminder information based on the working status of the vehicle braking system;

[0211] Step S207: Play target reminder information through an acoustic reminder device, and / or display target reminder information through an optical display device.

[0212] In this embodiment, the vehicle braking control system first generates target reminder information based on the working status of the vehicle braking system.

[0213] Targeted alert information refers to specific warnings or status data generated based on the current operating status of the vehicle's braking system (such as normal operation, failure, or braking in progress), used to communicate the current operating status and potential risks of the vehicle's braking system to the driver. Targeted alert information may include text or code signals such as "AEB Failure Warning," "AEB is Braking," or "Please Take Over Vehicle Management," as well as corresponding audio or visual signal trigger commands.

[0214] It can be seen that the vehicle braking control system monitors the working status of the vehicle braking system in real time and generates corresponding target reminder information based on the status, so as to be subsequently transmitted to the driver.

[0215] For example, when the failure determination module determines that the vehicle braking system has failed, the vehicle braking control system generates a target reminder message of "AEB Failure". When the braking control module performs braking, it generates a target reminder message of "AEB Braking in Progress".

[0216] Therefore, this application generates target reminder information on the vehicle braking system status, which helps to provide the driver with an intuitive status later.

[0217] Then, the vehicle braking control system plays target warning information through an acoustic warning device and / or displays the target warning information through an optical display device.

[0218] Acoustic alert devices refer to devices that can emit sound signals, such as buzzers or loudspeakers, to convey emergency or important information to drivers through hearing.

[0219] Optical display devices are devices that can display visual signals, such as dashboard warning lights or central control screens, and are used to convey status information to the driver visually.

[0220] It can be seen that the vehicle braking control system calls the acoustic reminder device to play the generated target reminder information, and / or calls the optical display device to display the generated target reminder information, thereby reminding the driver through one or more sensory channels.

[0221] For example, the vehicle braking control system plays a buzzer sound and illuminates a red warning light on the instrument panel to alert the driver that the vehicle braking system has failed, or displays the text "AEB braking in progress" on the instrument panel.

[0222] Therefore, this application uses a combination of sound and light to ensure that the driver can obtain timely and accurate information about the working status of the vehicle's braking system, thereby enhancing driving safety.

[0223] According to an embodiment of this application, a system embodiment of a vehicle braking control system is also provided. Figure 3 This is a structural block diagram of an optional vehicle braking system according to an embodiment of this application, such as... Figure 3 As shown, the vehicle braking system includes a perception module, a scene recognition module, a failure determination module, a braking control module, a human-machine interaction module, and a cloud server. These modules work together to achieve automatic emergency braking.

[0224] Each functional module establishes a bidirectional communication connection via the Controller Area Network Flexible Data-Rate (CAN-FD) bus to achieve efficient data interaction and collaborative control. The specific connections are as follows: The perception module connects to the millimeter-wave radar, forward-looking high-definition camera, rain sensor, vehicle speed sensor, and wiper gear detection unit to collect real-time environmental signals from outside the vehicle and the vehicle's own status signals. The perception module connects to the scene recognition module, failure determination module, and braking control module to synchronously distribute the collected data. The scene recognition module establishes a wireless communication link with the cloud server to receive the scene recognition neural network model sent from the cloud. The failure determination module connects to the human-machine interface module to output a failure warning signal when a system failure is detected. The braking control module receives scene recognition results from the scene recognition module, failure status signals from the failure determination module, and obstacle hazard information provided by the perception module. After comprehensive processing, it outputs braking commands to the vehicle's braking actuators. The human-machine interface module includes an audio alert unit, an instrument warning unit, and a touch setting unit, used to send audio-visual alerts to the driver and provide a manual adjustment interface for the vehicle's braking system sensitivity.

[0225] Furthermore, the perception module includes millimeter-wave radar, a high-definition camera, a rain sensor, a vehicle speed sensor, and a windshield wiper position detector, which are used to collect information on the vehicle's own status, the environment ahead, and the road. Specifically, the millimeter-wave radar and the high-definition camera work together to collect the position, speed, and displacement information of obstacles ahead (including vehicles, pedestrians, and non-motorized vehicles); the rain sensor collects the actual rainfall in the current environment; the vehicle speed sensor collects the vehicle's real-time speed; and the windshield wiper position detector detects the actual position of the windshield wipers.

[0226] The scene recognition module receives various types of information collected by the perception module and, in conjunction with the training model distributed by the cloud server, identifies the current driving scene, which includes highway cruising, urban congestion, and rain / snow. The module then outputs the scene recognition results to the downstream modules.

[0227] The failure determination module identifies the actual amount of rain and snow in the current environment based on the actual rainfall, wiper gear status and environmental images collected by the sensing module, and matches the trigger coverage rate of vehicle braking system failure. When the trigger coverage rate reaches a preset threshold, the vehicle braking system is determined to have failed and a failure signal is sent.

[0228] The braking control module receives scene recognition results from the scene recognition module, judgment signals from the failure judgment module, and obstacle information from the perception module. It then dynamically adjusts the braking trigger parameters (including trigger timing and braking intensity) to achieve smooth switching between high-speed and low-speed braking modes. Specifically, the vehicle braking control system has a preset safe speed (i.e., preset high-speed and low-speed thresholds) of 30 km / h. When the vehicle speed decreases to this threshold, the braking control module determines whether the previous braking deceleration demand signal was 0. If it is not 0, it directly sends a non-zero deceleration demand signal to avoid braking interruption.

[0229] The human-machine interface module receives failure signals from the failure detection module and sends failure alerts via acoustic warning devices (such as buzzers) and optical display devices (such as dashboard warning lights). It also allows the driver to adjust the vehicle's braking system sensitivity (including early, normal, and late modes) to suit different driving habits. The cloud server stores braking status information for multiple vehicle models (including comfort braking and emergency braking status information), trains and distributes specified neural network models for use by the scene recognition module and braking control module, thereby achieving dynamic optimization of the braking strategy.

[0230] According to an embodiment of this application, an embodiment of a control method for a vehicle braking system is also provided. Figure 4 This is a schematic diagram of an optional vehicle braking system control method according to an embodiment of this application. Figure 4 As shown, this method comprises six steps in sequence: information acquisition, scene recognition, failure determination, braking parameter adjustment, hazard assessment, and strategy optimization. First, real-time information acquisition is performed, obtaining vehicle status, environmental, and road information through a perception module. Next, intelligent recognition of multiple driving scenarios is conducted to determine the current driving scenario. Then, a specific environmental sensor failure determination is performed. If a sensor failure is detected, the vehicle's braking system cannot function properly, and the driver is alerted that the AEB function is unavailable, while data is uploaded and the model is optimized. If the sensor is not detected as faulty and the vehicle's braking system is functioning normally, the braking trigger threshold and braking intensity are dynamically adjusted based on the identified driving scenario. After parameter adjustment, a hazard assessment is performed. If a collision hazard is detected, AEB braking is executed or the driver is prompted to take over braking. If no collision hazard is detected, the relevant data from this braking process is directly uploaded to a cloud server for iterative optimization of the neural network model, thereby improving the accuracy of subsequent scene recognition and braking strategies.

[0231] According to an embodiment of this application, an embodiment of a system sensor failure determination method for a vehicle braking system control method is also provided. Figure 5 This is a flowchart illustrating the system sensor failure determination of an optional vehicle braking system control method according to an embodiment of this application, as shown below. Figure 5 As shown, the method comprises four steps: data acquisition, feature extraction and matching, threshold determination, and intervention time correction. First, the sensing module collects real-time rainfall data, wiper gear status, and environmental images captured by a camera. Next, an image recognition algorithm extracts rain and fog interference features from the environmental images and combines this with the rainfall data to match the trigger coverage rate of the vehicle's braking system failure. Then, it determines whether the corrected trigger coverage rate reaches or exceeds a preset 90% threshold. If the result is yes, the vehicle's braking system is determined to be in a failed state, and a signal indicating that the vehicle's braking system is unavailable is output to alert the driver to take over. If the result is no, the vehicle's braking system is determined not to be in a failed state, and a normal signal is output to the braking control module to maintain the automatic braking function. Then, the intervention time of the vehicle's braking system is dynamically corrected based on the vehicle's real-time speed. Specifically, the intervention time is advanced by 10% at high speeds and delayed by 10% at low speeds, thereby optimizing the braking response characteristics across different speed ranges.

[0232] According to an embodiment of this application, an embodiment of a speed switching method for a vehicle braking system control method is also provided. Figure 6 This is a speed switching flowchart of an optional vehicle braking system control method according to an embodiment of this application, such as... Figure 6 This method detects vehicle speed in real time and determines whether the current speed has decreased to a preset safe speed threshold (set to 30 km / h in this embodiment). If the result is higher than the preset safe speed threshold, the vehicle braking control system maintains the established braking strategy for the current scenario. If the result is lower than or equal to the preset safe speed threshold, the vehicle braking control system reads the braking deceleration demand signal from the previous moment. If the deceleration demand from the previous moment is 0, it indicates that the vehicle was not previously braking, and the vehicle braking control system switches to the default braking parameters for the low-speed scenario for subsequent control. If the deceleration demand signal from the previous moment is not 0, it indicates that the vehicle was previously braking, and the vehicle braking control system continuously outputs a deceleration demand signal of 0.2g to 0.3g to maintain the continuity of braking force and avoid impact caused by braking interruption. Finally, the vehicle braking control system continuously monitors the collision hazard ahead until it is confirmed that the hazard has been completely eliminated or the vehicle has been stationary for more than 2 seconds. At this time, the vehicle braking system exits braking control and enters standby mode, thereby ensuring braking smoothness and driving safety during the transition from high speed to low speed.

[0233] Optionally, this embodiment also provides a vehicle braking control system and control method. The vehicle braking control system architecture includes a perception module, a scene recognition module, a failure determination module, a braking control module, a human-machine interaction module, and a cloud server. Each module achieves data interaction and collaborative control through an onboard CAN-FD bus. The perception module includes a millimeter-wave radar, a forward-facing high-definition camera, a rain sensor, a vehicle speed sensor, and a wiper gear detector, which are used to collect information on the position and speed of obstacles ahead, environmental images, actual rainfall, real-time vehicle speed, and wiper gear status, respectively. The millimeter-wave radar has a detection range of 0.5-200m, the rain sensor has a measurement range of 0-50mm / h, and the vehicle speed sensor has an accuracy of ±1km / h. The scene recognition module receives multi-dimensional information collected by the perception module and obtains a neural network model trained from historical braking data of the same vehicle model from the cloud server. It inputs vehicle motion information, traffic participant information, and road information into the model to accurately identify the current driving scenario, specifically including: high-speed cruising scenarios (vehicle speed ≥ 60 km / h and no continuous congestion obstacles ahead), urban congestion scenarios (vehicle speed < 30 km / h and continuous moving obstacles ahead with a distance < 5 m), and rain / snow scenarios (rainfall ≥ 10 mm / h or windshield wipers in medium or high speed mode). The failure determination module, based on actual rainfall, windshield wiper mode, and environmental images, extracts rain / snow interference features through image recognition and matches the AEB blinding failure trigger coverage rate. When the corrected trigger coverage rate is ≥ 90%, the vehicle braking system is determined to have blinding failure; otherwise, the vehicle braking system is determined to be normal. The braking control module dynamically adjusts the braking strategy based on scene recognition results: In high-speed cruising scenarios, an early trigger mode is adopted, setting ETTC≤2.5s, Asafe≥4m / s², and the initial braking intensity to 0.6g, with an early warning when the obstacle distance is ≥100m. In urban congestion scenarios, a late trigger mode is adopted, setting ETTC≤1.5s, Asafe≥3m / s², and the initial braking intensity to 0.4g to reduce the probability of false braking. In rain and snow scenarios, the threshold is corrected to ETTC≤2.0s, Asafe≥3.5m / s², and the braking intensity is adjusted to 0.3-0.5g to prevent sideslip. In high-low speed switching scenarios, when the vehicle speed drops to the critical value of 30km / h, if the deceleration demand at the previous moment was not 0, a deceleration signal of 0.2-0.3g is continuously output to maintain braking continuity until the danger is eliminated or the vehicle stops for more than 2 seconds and enters standby mode. The human-machine interface module provides drivers with warnings of brake failure or malfunction via a buzzer, dashboard warning lights, and a touchscreen display, allowing drivers to adjust AEB sensitivity or view the vehicle's braking system status. A cloud server stores braking data for multiple vehicle models, updates the neural network model quarterly and distributes it to the vehicles, while simultaneously receiving braking data uploaded by the vehicles to continuously optimize the model. Control methods include: a perception module that collects vehicle status, environmental, and road information in real-time at a 10Hz frequency and simultaneously transmits it to all modules.The scene recognition module outputs scene recognition results using a cloud-based model. The failure detection module determines whether the vehicle's braking system has failed; if so, it alerts the driver to take over; otherwise, it adjusts the braking parameters. The braking control module dynamically adjusts the trigger threshold and intensity based on the scene, and automatically executes emergency braking when the trigger conditions are met; if the driver intervenes, it exits control. Finally, the braking data is uploaded to the cloud for model iteration and optimization.

[0234] As can be seen, this application has significant advantages compared to traditional solutions. First, by combining a scene recognition module with a cloud-based neural network model, this application achieves accurate identification of various driving scenarios, including high-speed cruising, urban congestion, and rain / snow. It can dynamically adjust the braking trigger timing and intensity based on scenario characteristics, effectively solving the problems of poor adaptability, false triggering, and missed triggering caused by fixed thresholds in traditional vehicle braking systems, thus comprehensively improving braking accuracy and reliability. Second, this application presets high and low speed thresholds. When the vehicle speed crosses these thresholds, it logically judges the braking deceleration demand signal from the previous moment. If it is not zero, it continuously sends a non-zero deceleration signal to maintain braking continuity, thereby avoiding intensity fluctuations caused by braking interruption and significantly improving driving smoothness and safety during high-low speed transitions. Furthermore, this application adopts an integrated design, combining scene recognition, failure determination, braking control, and cloud-based model iterative optimization functions. Compared to existing single-technology-point optimization solutions, it can more comprehensively address the overall defects of vehicle braking systems. Moreover, through continuous cloud-based model optimization, it possesses strong adaptability and scalability to different vehicle models and road conditions. Finally, this application takes into account the driver's autonomy, allowing the driver to adjust the sensitivity of the vehicle's braking system according to their habits through the human-machine interaction module, and automatically disengaging control when the driver actively intervenes in braking. This balances driving autonomy while ensuring active safety, further enhancing the user's driving experience.

[0235] According to an embodiment of this application, a device embodiment for a vehicle braking system control method is also provided. This vehicle braking system control device is configured to implement the above-described method embodiment and various optional implementations of the method embodiment. The technical content already described above will not be repeated in the device embodiment. It should be noted that in the following related descriptions of the device embodiment, a "module" can be defined as software, hardware, or a combination of software and hardware that implements a specified function.

[0236] Figure 7 This is a structural block diagram of a control device for a vehicle braking system according to an embodiment of this application, such as... Figure 7As shown, the control device 700 of the vehicle braking system includes: an acquisition module 701 for acquiring vehicle status information and environmental perception information; an identification module 702 for identifying the scene in which the vehicle is located based on the status information and obtaining a scene identification result; a first determination module 703 for determining the working state of the vehicle braking system based on the status information and environmental perception information, wherein the working state indicates whether the vehicle braking system is working normally; a second determination module 704 for determining braking trigger parameters based on the scene identification result in response to the working state indicating that the vehicle braking system is working normally; and a control module 705 for controlling the vehicle braking system based on the scene identification result and the braking trigger parameters.

[0237] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0238] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any of the aforementioned vehicle braking system control methods.

[0239] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps: acquiring vehicle status information and environmental perception information; identifying the vehicle's location based on the status information to obtain a scene recognition result; determining the operating state of the vehicle braking system based on the status information and environmental perception information, wherein the operating state indicates whether the vehicle braking system is operating normally; in response to the operating state indicating that the vehicle braking system is operating normally, determining braking trigger parameters based on the scene recognition result; and controlling the vehicle braking system based on the scene recognition result and the braking trigger parameters.

[0240] Optionally, the aforementioned computer storage media may include, but are not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0241] According to another aspect of the embodiments of this application, a vehicle is also provided, including an on-board memory and an on-board processor, wherein the on-board memory stores a computer program, and the on-board processor is configured to run the computer program to execute the control method of the vehicle braking system of any of the foregoing.

[0242] Optionally, in this embodiment, the on-board processor can be configured to perform the following steps via a computer program: acquiring vehicle status information and environmental perception information; identifying the vehicle's location based on the status information to obtain a scene recognition result; determining the operating state of the vehicle braking system based on the status information and environmental perception information, wherein the operating state indicates whether the vehicle braking system is operating normally; in response to the operating state indicating that the vehicle braking system is operating normally, determining braking trigger parameters based on the scene recognition result; and controlling the vehicle braking system based on the scene recognition result and the braking trigger parameters.

[0243] According to an embodiment of this application, a computer program product is also provided. Optionally, in this embodiment, the above-mentioned computer program product can provide control services for a vehicle braking system based on the vehicle braking system control methods provided in the foregoing embodiments.

[0244] Optionally, in this embodiment, the computer program product described above may be a set of instructions and codes pre-written according to the control method of the vehicle braking system described above. This computer program product can run on various computer platforms, including personal computers, servers, mobile devices, etc.

[0245] Optionally, in this embodiment, the instructions and code corresponding to the computer program product are used to implement the following method steps: acquiring vehicle status information and environmental perception information; identifying the scene in which the vehicle is located based on the status information, and obtaining scene recognition results; determining the working state of the vehicle braking system based on the status information and environmental perception information, wherein the working state is used to indicate whether the vehicle braking system is working normally; in response to the working state indicating that the vehicle braking system is working normally, determining braking trigger parameters based on the scene recognition results; and controlling the vehicle braking system based on the scene recognition results and braking trigger parameters.

[0246] The aforementioned computer program product provides vehicle braking system control services in application scenarios involving the generation of virtual machine models and outlining effects in the field of computer technology. It acquires vehicle status information and environmental perception information; identifies the vehicle's current scenario based on the status information, obtaining a scenario recognition result; determines the operating state of the vehicle braking system based on the status and environmental perception information, where the operating state indicates whether the vehicle braking system is working normally; responds to the operating state indicating normal operation of the vehicle braking system, determines braking trigger parameters based on the scenario recognition result; and controls the vehicle braking system based on the scenario recognition result and braking trigger parameters. This achieves the goal of dynamically matching and optimizing braking strategies for different driving scenarios and evaluating system availability in real time. This results in improved driving smoothness while ensuring timely braking response, and effectively reduces false and missed braking triggers. Furthermore, it solves the technical problem of existing braking systems having a single and fixed braking mode, making it difficult to balance the effectiveness of braking response and driving smoothness under different driving scenarios, thus leading to lower vehicle safety performance.

[0247] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and their optional implementations, and will not be repeated here.

[0248] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0249] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection between units or modules can be electrical or other forms.

[0250] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0251] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0252] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, ROM, RAM, portable hard drives, magnetic disks, or optical disks.

[0253] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A control method for a vehicle braking system, characterized in that, include: Acquire vehicle status information and environmental perception information; The scene in which the vehicle is located is identified based on the state information, and a scene identification result is obtained; The operating state of the vehicle braking system is determined based on the state information and the environmental perception information, wherein the operating state is used to indicate whether the vehicle braking system is working normally; In response to the operating state indicating that the vehicle braking system is working normally, brake trigger parameters are determined based on the scene recognition result; The vehicle braking system is controlled based on the scene recognition results and the braking trigger parameters.

2. The method according to claim 1, characterized in that, The status information includes the vehicle's speed, acceleration, and braking status. The step of identifying the vehicle's location based on the status information and obtaining the scene identification result includes: The driving state of the vehicle is determined based on the vehicle speed, the acceleration, and the braking state; The scene recognition result is determined based on the driving status.

3. The method according to claim 2, characterized in that, The environmental perception information includes meteorological environmental information and obstacle status. The meteorological environmental information includes weather conditions. Determining the scene recognition result based on the driving status includes: In response to the driving state being a first state, the scene recognition result is determined to be a speed switching scene, wherein the first state is used to indicate that the vehicle speed decreases from a first speed to a second speed and the vehicle is in a braking state, the first speed is higher than a preset safe speed, and the second speed is lower than the preset safe speed; In response to the driving state being different from the first state and the weather state being preset rainy / snowy weather, the scene recognition result is determined to be a rainy / snowy scene; In response to the driving state being the second state and the weather state not being the preset rain or snow weather, the scene recognition result is determined to be a high-speed cruise scene; In response to the driving state being the third state, the obstacle state being the obstacle cluster state, and the weather state not being the preset rain or snow weather, the scene recognition result is determined to be an urban traffic congestion scene, wherein the vehicle speed corresponding to the second state is greater than the vehicle speed corresponding to the third state, and the obstacle cluster state is used to indicate that there are multiple continuously moving obstacles within the target range of the vehicle.

4. The method according to claim 3, characterized in that, The meteorological environment information also includes rainfall and snowfall amounts and meteorological environment images. The status information also includes windshield wiper settings. Determining the operating status of the vehicle braking system based on the status information and the environmental perception information includes: The initial coverage rate is determined based on the amount of rain and snow. The initial coverage rate is corrected based on the wiper setting and the meteorological environment image to obtain the target coverage rate, wherein the target coverage rate is used to represent the failure probability of the vehicle braking system; The operating status of the vehicle braking system is determined based on the target coverage rate.

5. The method according to claim 4, characterized in that, Determining the initial coverage rate based on the rainfall and snowfall includes: In response to the fact that the amount of rain and snow is less than a first rain and snow threshold, the initial coverage rate is determined to be a first coverage rate; In response to the fact that the amount of rain and snow is greater than or equal to the first rain and snow threshold and less than the second rain and snow threshold, the initial coverage rate is determined to be the second coverage rate; In response to the rainfall and snowfall being greater than or equal to the second rainfall and snowfall threshold, the initial coverage rate is determined to be the third coverage rate, wherein the first coverage rate is less than the second coverage rate, and the third coverage rate is greater than the second coverage rate.

6. The method according to claim 3, characterized in that, Determining the braking trigger parameters based on the scene recognition result includes: In response to the scene recognition result being the high-speed cruise scene, a first braking trigger parameter is determined, wherein the first braking trigger parameter includes a first collision time threshold, a first deceleration threshold, and a first braking intensity; In response to the scene recognition result being the urban congestion scene, a second braking trigger parameter is determined, wherein the second braking trigger parameter includes a second collision time threshold, a second deceleration threshold, and a second braking intensity; In response to the scene recognition result being the rain and snow scene, a third braking trigger parameter is determined, wherein the third braking trigger parameter includes a third collision time threshold, a third deceleration threshold, and a third braking intensity; In response to the scene recognition result being the speed switching scene, a fourth braking trigger parameter is determined, wherein the fourth braking trigger parameter includes a fourth braking intensity.

7. The method according to claim 6, characterized in that, The control of the vehicle braking system based on the scene recognition result and the braking trigger parameters includes: In response to the scenario recognition result being the high-speed cruise scenario, the vehicle's current collision time being less than or equal to the first collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to the first deceleration threshold, braking control of the vehicle's braking system is performed based on the first braking intensity. In response to the scenario recognition result being the urban congestion scenario, the vehicle's current collision time being less than or equal to the second collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to the second deceleration threshold, braking control of the vehicle's braking system is performed based on the second braking intensity; In response to the scene recognition result being the rain and snow scene, the vehicle's current collision time being less than or equal to the third collision time threshold, and the vehicle's current collision avoidance deceleration being greater than or equal to the third deceleration threshold, the vehicle braking system is braked based on the third braking intensity. In response to the scene recognition result being the speed switching scene, the vehicle braking system is continuously braked based on the fourth braking intensity until the collision hazard is eliminated or the vehicle comes to a stop.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Generate target reminder information based on the operating status of the vehicle braking system; The target reminder information is played through an acoustic reminder device, and / or the target reminder information is displayed through an optical display device.

9. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 8.

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