Two-wheeled vehicle braking system, method and device

Abstract

The embodiment of the invention provides a two-wheeled vehicle braking system, method and device, the system comprises a sensing module, a braking force control module, a motion detection module and a processor, and the processor is configured to judge the current state of a two-wheeled vehicle based on first data; the first data comprises motion information of the two-wheeled vehicle; in response to the fact that the two-wheeled vehicle is in the moving state, the collision risk of the two-wheeled vehicle is determined; the collision risk is determined based on the first data and the second data; the second data comprises road sensing data; and when the collision risk meets a preset condition, a braking instruction is generated based on the collision risk, and the braking instruction is sent to the braking force control module.

Description

Technical Field

[0001] This specification relates to the field of Internet technologies, and particularly to a braking system, method, and device for two-wheeled vehicles. Background Art

[0002] Traditional two-wheeled vehicles (including bicycles and electric two-wheeled vehicles) rely on manual braking, that is, the user realizes braking through a handbrake or a footbrake. However, in some emergency situations, the user may not brake in time due to untimely reaction or failure to detect risks in time, or may also fall or lose control of the vehicle due to excessive braking.

[0003] Although some two-wheeled vehicles are equipped with an Antilock Brake System (ABS), its main purpose is to prevent tire skidding and cannot provide active braking for the user in emergency situations.

[0004] Therefore, a braking system, method, and device for two-wheeled vehicles are needed to solve the above problems. Summary of the Invention

[0005] One or more embodiments of this specification provide a braking system for two-wheeled vehicles. The system includes a sensing module, a braking force control module, a motion detection module, and a processor. The processor is configured to: based on first data, determine the current state of the two-wheeled vehicle; the first data includes the motion information of the two-wheeled vehicle; in response to the two-wheeled vehicle being in a motion state, determine the collision risk of the two-wheeled vehicle; the collision risk is determined based on the first data and second data; the second data includes road sensing data; when the collision risk meets a preset condition, generate a braking instruction based on the collision risk and send the braking instruction to the braking force control module.

[0006] In some embodiments, the motion detection module is configured to obtain the first data; the sensing module is configured to obtain the second data; the processor is further configured to: determine the collision risk based on the road sensing data and the motion information of the two-wheeled vehicle.

[0007] In some embodiments, the processor is further configured to: determine obstacles within a preset range and characteristic parameters of the obstacles based on the road sensing data; the characteristic parameters include at least one of the type of the obstacle, the moving speed of the obstacle, and the distance between the obstacle and the current position of the two-wheeled vehicle; determine the collision risk based on the characteristic parameters and the motion information of the two-wheeled vehicle.

[0008] In some embodiments, the characteristic parameter further includes the relative path of the obstacle and the two-wheeled vehicle; the relative path includes the relative positional relationship and relative motion trajectory between the obstacle and the two-wheeled vehicle.

[0009] In some embodiments, the processor is further configured to: determine the collision risk based on the characteristic parameter and the motion information of the two-wheeled vehicle through a collision risk prediction model, where the collision risk prediction model is a machine learning model.

[0010] In some embodiments, the processor is further configured to: determine the braking force and braking time based on the motion information of the two-wheeled vehicle, the collision risk, and the response time of the braking force control module; generate the braking instruction based on the braking force and the braking time.

[0011] In some embodiments, the system further includes a feedback module configured to obtain braking preference information; the processor is further configured to: determine the braking force and the braking time based on the motion information of the two-wheeled vehicle, the collision risk, the response time of the braking force control module, and the braking preference information.

[0012] In some embodiments, the preset condition is related to braking preference information.

[0013] One embodiment of this specification provides a two-wheeled vehicle braking method, which is executed based on the two-wheeled vehicle braking system described in any of the above embodiments. The method includes: judging the current state of the two-wheeled vehicle based on first data; the first data includes the motion information of the two-wheeled vehicle; in response to the two-wheeled vehicle being in a moving state, determining the collision risk of the two-wheeled vehicle; the collision risk is determined based on the first data and second data; the second data includes road sensing data; when the collision risk meets a preset condition, generating a braking instruction based on the collision risk and sending the braking instruction to the braking force control module.

[0014] One or more embodiments of this specification provide a two-wheeled vehicle braking device, which includes at least one storage medium and at least one processor; the at least one storage medium is used to store computer instructions; the at least one processor is used to execute the computer instructions to implement the two-wheeled vehicle braking method. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] This specification will be further described in the form of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0016] Figure 1It is a schematic diagram of the application scenario of a two-wheeler braking system shown in some embodiments of this specification;

[0017] Figure 2 It is an exemplary block diagram of a two-wheeler braking system shown in some embodiments of this specification;

[0018] Figure 3 It is an exemplary flowchart of a two-wheeler braking method shown in some embodiments of this specification. Detailed implementation manners

[0019] To more clearly illustrate the technical solutions of the embodiments of this specification, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0020] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0021] As shown in this specification and the claims, unless the context clearly indicates an exceptional situation, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0022] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in sequence. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0023] Figure 1 It is a schematic diagram of the application scenario of a two-wheeler braking system shown in some embodiments of this specification.

[0024] In some embodiments, as Figure 1 shown, the two-wheeler braking system 100 includes a two-wheeler 110, a network 120, a terminal 130, a processor 140, and a storage device 150.

[0025] The two-wheeled vehicle 110 may include a bicycle, an electric bicycle, a motorcycle, an electric motorcycle, etc.

[0026] The network 120 may connect the components in the two-wheeled vehicle braking system 100 and / or connect other components outside the two-wheeled vehicle braking system 100. In some embodiments, one or more components of the two-wheeled vehicle braking system 100 (e.g., the two-wheeled vehicle 110, the terminal 130, the processor 140, and the storage device 150, etc.) may be connected to and / or communicate with each other through the network 120. For example, the two-wheeled vehicle 110 may send the first data and / or the second data to the processor 140, etc. through the network 120.

[0027] The terminal 130 can provide functional components related to user interaction and can implement user interaction functions (such as providing or presenting information and data for the user). The user may refer to the two-wheeled vehicle operation and maintenance personnel or the rider of the two-wheeled vehicle, etc. Only by way of example, the terminal 130 may be a mobile device, a tablet computer, a laptop computer, a desktop computer, etc. or any one or any combination of other devices with input and / or output functions.

[0028] The processor 140 can process information and / or data related to the two-wheeled vehicle braking system 100 to execute one or more functions described in this specification. In some embodiments, the processor 140 may be configured to determine whether the two-wheeled vehicle is in a moving state based on the first data; in response to the two-wheeled vehicle being in a moving state, determine the collision risk of the two-wheeled vehicle; the collision risk is determined based on the first data and the second data; when the collision risk meets a preset condition, generate a braking instruction based on the collision risk and send the braking instruction to the braking force control module. For a detailed description of the relevant content, please refer to the following text (such as Figure 3 ) related description.

[0029] In some embodiments, the processor 140 may include a central processing unit (CPU), a digital signal processor (DSP), a microcontroller unit (MCU), a computer, a user console, etc. or any combination thereof. In some embodiments, the processor 140 may include a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 140 may be local or remote. In some embodiments, the processor 140 may be implemented on a cloud platform. Only by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc. or any combination thereof.

[0030] The storage device 150 is capable of storing data, instructions, and / or any other information. In some embodiments, the storage device 150 may store data obtained from the two-wheeled vehicle 110 and / or the processor 140, such as first data, second data, etc. In some embodiments, the storage device 150 may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 may be implemented on a cloud platform. In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more other components of the two-wheeled vehicle braking system 100 (e.g., the two-wheeled vehicle 110, the processor 140, etc.). In some embodiments, the storage device 150 may be a part of the processor 140.

[0031] It should be noted that the two-wheeled vehicle braking system 100 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those of ordinary skill in the art can make various changes and modifications according to the description of this specification. For example, the two-wheeled vehicle braking system 100 may further include a database, an information source, etc. Again, for example, the two-wheeled vehicle braking system 100 may be implemented on other devices to achieve similar or different functions. However, these changes and modifications will not depart from the scope of this specification.

[0032] Figure 2 is an exemplary module diagram of a two-wheeled vehicle braking system shown in some embodiments of this specification. As Figure 2 shown, the two-wheeled vehicle braking system 100 includes a sensing module 210, a braking force control module 220, a motion detection module 230, and a processor 140.

[0033] The sensing module 210 is configured to obtain second data. In some embodiments, the second data includes road sensing data.

[0034] The braking force control module 220 is configured to control the braking of the two-wheeled vehicle based on a braking instruction. For example, the braking force control module 220 may control the braking of the two-wheeled vehicle based on the braking force and the braking time.

[0035] The motion detection module 230 is configured to obtain first data. In some embodiments, the first data includes the motion information of the two-wheeled vehicle.

[0036] The processor 140 is configured to determine whether the two-wheeled vehicle is in a motion state based on the first data; in response to the two-wheeled vehicle being in a motion state, determine the collision risk of the two-wheeled vehicle; the collision risk is determined based on the first data and the second data; when the collision risk meets a preset condition, generate a braking instruction based on the collision risk and send the braking instruction to the braking force control module 220.

[0037] In some embodiments, the processor 140 is further configured to determine the collision risk based on the road sensing data and the motion information of the two-wheeled vehicle.

[0038] In some embodiments, the processor 140 is further configured to determine obstacles within a preset range and characteristic parameters of the obstacles based on the road sensing data; the characteristic parameters include at least one of the obstacle type, the moving speed of the obstacle, and the distance between the obstacle and the current position of the two-wheeled vehicle; and determine the collision risk based on the characteristic parameters and the motion information of the two-wheeled vehicle.

[0039] In some embodiments, the characteristic parameters further include the relative path of the obstacle with respect to the two-wheeled vehicle; the relative path includes the relative position relationship and the relative motion trajectory between the obstacle and the two-wheeled vehicle.

[0040] In some embodiments, the processor 140 is further configured to determine the collision risk through a collision risk prediction model based on the characteristic parameters and the motion information of the two-wheeled vehicle, and the collision risk prediction model is a machine learning model.

[0041] In some embodiments, the processor 140 is further configured to determine the braking force and the braking time based on the motion information of the two-wheeled vehicle, the collision risk, and the response time of the braking force control module; and generate the braking instruction based on the braking force and the braking time.

[0042] In some embodiments, the two-wheeled vehicle braking system 100 further includes a feedback module 240.

[0043] The feedback module 240 is configured to obtain braking preference information.

[0044] In some embodiments, the processor 140 is further configured to determine the braking force and the braking time based on the motion information of the two-wheeled vehicle, the response time of the braking force control module 220, and the braking preference information.

[0045] In some embodiments, the preset condition is related to the braking preference information.

[0046] In some embodiments, the two-wheeled vehicle braking system 100 further includes a communication module 250.

[0047] The communication module 250 is configured to obtain auxiliary motion information, and the processor 140 is further configured to determine the braking force and the braking time based on the motion information of the two-wheeled vehicle, the response time of the braking force control module 220, and the auxiliary motion information.

[0048] For a specific description of the functions involved in each module of the two-wheeled vehicle braking system 100 shown above, reference may be made to the relevant parts of the following text of this specification. For example, Figure 3 and its related descriptions.

[0049] It should be noted that the above descriptions of the two-wheeled vehicle braking system 100 and its modules are only for convenience of description and do not limit this specification to the scope of the exemplified embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, various modules may be arbitrarily combined, or a subsystem may be formed and connected to other modules without departing from this principle. In some embodiments, Figure 2 the sensing module 210, the braking force control module 220, the motion detection module 230, the processor 140, the feedback module 240, and the communication module 250 disclosed in Figure 2 may be different modules in a system, or a single module may implement the functions of two or more of the above modules. For example, each module may share a storage module, or each module may have its own storage module separately. Such variations are all within the protection scope of this specification.

[0050] Figure 3 is an exemplary flowchart of a two-wheeled vehicle braking method according to some embodiments of this specification. In some embodiments, process 300 may be executed by the processor 140 or the two-wheeled vehicle braking system 100. As Figure 3 shown, process 300 includes the following steps.

[0051] Step 310, start.

[0052] When the processor receives a start instruction, it starts to execute the two-wheeled vehicle braking method. In some embodiments, the start instruction may be obtained through user input. For example, the user may input a start instruction through the terminal 130. In some embodiments, the start instruction may also be automatically generated. Taking the two-wheeled vehicle as a shared vehicle (such as a shared bicycle) as an example, when the user scans the code to unlock the vehicle, the processor may automatically generate a start instruction and start to execute the two-wheeled vehicle braking method.

[0053] Step 320, based on the first data, determine whether the two-wheeled vehicle is in a moving state.

[0054] The first data refers to the data obtained from the motion detection module 230. In some embodiments, the first data includes the motion information of the two-wheeled vehicle.

[0055] The motion information of the two-wheeled vehicle refers to the information related to the motion state of the two-wheeled vehicle. For example, the motion information of the two-wheeled vehicle may include the speed, acceleration, inclination angle, etc. of the two-wheeled vehicle.

[0056] The motion detection module 230 includes various sensors that can detect the motion state of the two-wheeled vehicle. Exemplarily, the motion detection module 230 may include a speed sensor, an acceleration sensor, an angle sensor, etc.

[0057] In some embodiments, the processor may determine whether the two-wheeled vehicle is in a motion state based on the first data. For example, when the speed (or acceleration) of the two-wheeled vehicle in the first data is greater than a preset value, the processor may determine that the two-wheeled vehicle is in a motion state.

[0058] In response to the two-wheeled vehicle being in a motion state, the processor executes step 330; in response to the two-wheeled vehicle not being in a motion state, the processor executes step 360, that is, ends the two-wheeled vehicle braking process.

[0059] Step 330, determine the collision risk of the two-wheeled vehicle.

[0060] The collision risk refers to the probability of a collision accident occurring to the two-wheeled vehicle. In some embodiments, the collision risk may refer to the probability of a collision accident occurring to the two-wheeled vehicle within a future period of time (such as the next 5 seconds, 10 seconds, etc.). In some embodiments, the collision risk may refer to the probability of a collision accident occurring at each of multiple future time points (such as the 5th second, 10th second, etc.).

[0061] In some embodiments, the collision risk may be determined based on the first data and the second data. Exemplarily, the processor may determine the collision risk based on the first data and the second data through methods such as preset look-up tables and database matching. For example, the correspondence between the first data, the second data, and the collision risk may be preset in the storage device 150, and the processor determines the collision risk through data retrieval and other means after determining the first data and the second data.

[0062] The second data refers to the data obtained from the perception module 210. In some embodiments, the second data includes road sensing data.

[0063] The road sensing data refers to the sensing data related to the road on which the two-wheeled vehicle travels. Exemplarily, the road sensing data may include road images, obstacles on the road, the position and speed of the obstacles, etc.

[0064] The perception module 210 includes various sensors that can detect road sensing data. For example, the perception module 210 may include a radar, a laser rangefinder, an image acquisition device, an inertial measurement unit, etc.

[0065] In some embodiments, the processor may determine the collision risk based on the road sensing data and the motion information of the two-wheeled vehicle. For example, the correspondence between the road sensing data, the motion information of the two-wheeled vehicle, and the collision risk may be preset in the storage device 150, and the processor determines the collision risk through data retrieval and other means after determining the road sensing data and the motion information of the two-wheeled vehicle.

[0066] In some embodiments, the processor may determine obstacles within a preset range and the characteristic parameters of the obstacles based on road sensing data.

[0067] The preset range refers to a preset area in the driving direction of the two-wheeled vehicle. For example, the preset range may be a sector area centered on the two-wheeled vehicle with a preset length (such as 10 meters, 50 meters, etc.) as the radius in the driving direction of the two-wheeled vehicle.

[0068] An obstacle refers to an object that may impede the driving of the two-wheeled vehicle within the preset range. Obstacles may include stationary obstacles (such as stones, trees, buildings, etc.) and moving obstacles (such as pedestrians, animals, other vehicles, etc.).

[0069] The characteristic parameters of the obstacle refer to the parameters related to the characteristics of the obstacle. In some embodiments, the characteristic parameters of the obstacle may include at least one of the obstacle type (such as pedestrian, parked vehicle, moving vehicle, tree, etc.), the moving speed of the obstacle, and the distance between the obstacle and the current position of the two-wheeled vehicle.

[0070] In some embodiments, the processor may determine the obstacle and the characteristic parameters of the obstacle based on the road sensing data through multiple algorithms. For example, the processor may determine the obstacle and the characteristic parameters of the obstacle by methods such as image recognition algorithms and image recognition models.

[0071] In some embodiments, the processor may determine the collision risk based on the characteristic parameters of the obstacle and the movement information of the two-wheeled vehicle. For example, the correspondence between the characteristic parameters of the obstacle, the movement information of the two-wheeled vehicle, and the collision risk may be preset in the storage device 150, and the processor determines the collision risk by means of data retrieval and the like after determining the characteristic parameters of the obstacle and the movement information of the two-wheeled vehicle.

[0072] In some embodiments, the characteristic parameters of the obstacle further include the relative path between the obstacle and the two-wheeled vehicle.

[0073] The relative path includes the relative position relationship and the relative movement trajectory between the obstacle and the two-wheeled vehicle.

[0074] The relative position relationship refers to the spatial relative position relationship between the obstacle and the two-wheeled vehicle. For example, the relative position relationship includes the straight-line distance between the obstacle and the two-wheeled vehicle, and the relative position of the obstacle and the two-wheeled vehicle (such as the obstacle is directly in front of, on the left side, or on the right side of the two-wheeled vehicle).

[0075] The relative movement trajectory refers to the trajectory when the obstacle moves relative to the two-wheeled vehicle.

[0076] It can be understood that when the obstacle is an object stationary by the roadside or an object moving in front of (or on the left or right side of) the two-wheeled vehicle, the relative motion trajectories with respect to the two-wheeled vehicle may be different, and the risks of collision with the two-wheeled vehicle are also different. When the characteristic parameters of the obstacle include the relative path between the obstacle and the two-wheeled vehicle, the accuracy of the subsequently determined collision risk can be improved.

[0077] In some embodiments, the processor can determine the road environment characteristics, the position of the obstacle, and its motion characteristics based on the road sensing data; and, the processor can determine the relative path between the obstacle and the two-wheeled vehicle based on the road environment characteristics, the position of the obstacle, and its motion characteristics.

[0078] The road environment characteristics refer to the characteristics related to the environment of the road on which the two-wheeled vehicle travels. The road environment characteristics can include the area of the carriageway, the area of the intersection, the sidewalk area, etc. For example, the processor can obtain the road environment characteristics based on sensors such as radar, lidar, and imaging devices.

[0079] The motion characteristics of the obstacle refer to the characteristics of the relative motion between the obstacle and the two-wheeled vehicle. The motion characteristics of the obstacle include the type of the obstacle, the motion direction and speed of the obstacle relative to the two-wheeled vehicle, etc.

[0080] In some embodiments, the processor can determine the road environment characteristics, the position of the obstacle, and its motion characteristics based on multiple algorithms (such as image recognition algorithms, image recognition models, etc.), and further determine the relative motion trajectory between the obstacle and the two-wheeled vehicle.

[0081] Exemplarily, the processor can obtain the characteristic parameters of the obstacle based on sensors (such as radar, lidar, imaging devices, etc.). For example, the processor can obtain the speed information of the obstacle and the relative distance from the two-wheeled vehicle based on radar and lidar, and obtain computer vision recognition of the obstacle based on the imaging device. Further, the processor can determine the motion direction and speed of the obstacle relative to the two-wheeled vehicle based on computer vision algorithms (such as object tracking technology based on deep learning), and combine the data obtained by the sensors to determine the motion characteristics of the obstacle using filtering algorithms (such as Kalman filters).

[0082] In some embodiments, the processor can fuse the data such as the distance, speed, and angle of the obstacle obtained by the sensors to determine the position of the obstacle relative to the two-wheeled vehicle; and, the processor can determine the relative motion trajectory between the obstacle and the two-wheeled vehicle based on the path prediction model. The path prediction model is a machine learning model, the input of which includes the road environment characteristics, the position of the obstacle relative to the two-wheeled vehicle, and the motion characteristics of the obstacle, and the output is the relative motion trajectory between the obstacle and the two-wheeled vehicle.

[0083] It should be noted that the structure of the two-wheeler is relatively light, the response time of the braking system is short, and it is sensitive to the braking force. Excessive or uneven braking force may cause the vehicle to lose control and result in safety accidents. In addition, the two-wheeler may operate in more complex and changeable environments (such as mountains, urban roads, rural paths, etc.), which requires higher environmental adaptability and perception ability of the braking system. Through the relative motion trajectory of the obstacle and the two-wheeler determined by the above embodiments, the motion trajectory of the obstacle can be determined more accurately, and various obstacles (such as small obstacles, etc.) can also be identified more precisely, further providing a more accurate and precise braking force for the two-wheeler, obtaining a better braking effect, reducing safety risks, and enhancing the user's riding experience.

[0084] In some embodiments, the processor may determine the collision risk through a collision risk prediction model based on the characteristic parameters of the obstacle and the motion information of the two-wheeler.

[0085] The collision risk prediction model is a machine learning model. In some embodiments, the collision risk model may be a deep learning model. For example, the collision risk model may be a convolutional neural network model, a recurrent neural network model, a reinforcement learning model, etc. or a combination thereof.

[0086] In some embodiments, the input of the collision risk prediction model is the characteristic parameters of the obstacle and the motion information of the two-wheeler, and the output is the collision risk.

[0087] In some embodiments, the collision risk prediction model may be obtained by training based on labeled samples. The samples include the characteristic parameters of the sample obstacle and the motion information of the sample two-wheeler, and the labels include the characteristic parameters of the sample obstacle and the collision risk corresponding to the motion information of the sample two-wheeler. Among them, the training samples and labels can be collected and labeled based on historical data (or simulation).

[0088] Step 340, determine whether the collision risk meets the preset conditions.

[0089] In some embodiments, the preset conditions may include that the collision risk is greater than the first preset threshold.

[0090] In some embodiments, the preset conditions are also related to the braking preference information. For more descriptions of the braking preference information and determining the preset conditions based on the braking preference information, refer to step 350.

[0091] In some embodiments, when the processor determines that the collision risk meets the preset conditions, execute step 350; when the collision risk does not meet the preset conditions, return to step 320, that is, continuously detect the collision risk of the two-wheeler until the two-wheeler is not in a moving state.

[0092] Step 350: Generate a braking instruction based on the collision risk and send the braking instruction to the braking force control module.

[0093] In some embodiments, the processor may determine the braking force and the braking time based on the movement information of the two-wheeled vehicle, the collision risk, and the response time of the braking force control module; and, the processor may generate a braking instruction based on the braking force and the braking time.

[0094] The response time of the braking force control module refers to the time from when the processor issues a braking instruction to when the braking force control module responds to the braking instruction and causes the two-wheeled vehicle to brake to a stop.

[0095] The braking force refers to the magnitude of the resistance that causes the two-wheeled vehicle to brake.

[0096] The braking time refers to the time from when the two-wheeled vehicle starts braking to when it completely stops.

[0097] In some embodiments, the braking force and the braking time of the two-wheeled vehicle may be related to the movement speed of the two-wheeled vehicle. For example, when the movement speed of the two-wheeled vehicle is greater, the braking force is smaller, and the braking time is longer, etc.

[0098] In some embodiments, when the movement speed of the two-wheeled vehicle is greater than a preset value (such as 5 m / s), the processor may adopt a progressive braking method, that is, the faster the driving speed of the two-wheeled vehicle, the smaller the braking force, and the more gentle the increase in the braking force over time.

[0099] In some embodiments of this specification, the braking force and the braking time of the two-wheeled vehicle being related to the movement speed of the two-wheeled vehicle can increase the stability of the two-wheeled vehicle, reduce the risk of vehicle out-of-control caused by excessive braking, and improve safety.

[0100] In some embodiments, the sensing module 210 may further include a pose sensor, and the pose sensor may collect the riding pose of the user and the tilting pose of the two-wheeled vehicle.

[0101] In some embodiments, the braking force and the braking time of the two-wheeled vehicle may also be related to the riding pose of the user and the tilting pose of the two-wheeled vehicle. For example, when the stability of the riding pose of the user and the tilting pose of the two-wheeled vehicle is lower, the braking force is smaller, and the braking time is longer, etc.

[0102] Among them, the stability of the riding pose of the user may be related to the center of gravity of the user. The lower the center of gravity of the user, the higher the stability of his riding pose; the stability of the tilting pose of the two-wheeled vehicle may be related to the tilting angle of the two-wheeled vehicle. The smaller the tilting angle, the higher the stability of the tilting pose of the two-wheeled vehicle.

[0103] In some embodiments of the present specification, the braking force and braking time of the two-wheeler are related to the riding posture of the user and the tilting posture of the two-wheeler, which can improve the stability of the two-wheeler and reduce the risk of vehicle out of control.

[0104] In some embodiments, the braking force and braking time of the two-wheeler can also be related to the change in the distance between the two-wheeler and the obstacle. For example, when the approaching speed of the two-wheeler and the obstacle is faster, it indicates that the possibility of collision between the two-wheeler and the obstacle is greater, the braking force of the two-wheeler is greater, and the braking time is shorter.

[0105] In some embodiments, the braking force and braking time of the two-wheeler can also be related to the speed change of the two-wheeler. For example, when the speed of the two-wheeler decreases, it indicates that the user may have taken the initiative to brake. Auxiliary braking can be provided for the user to shorten the braking time and improve the braking effect. Among them, the auxiliary braking can be that when it is detected that the speed of the two-wheeler decreases, the processor provides braking force for the two-wheeler. The faster the speed of the two-wheeler decreases, the greater the braking force of the auxiliary braking.

[0106] In some embodiments, the auxiliary braking can also be related to the road characteristics. For example, when the road is uphill, the decrease in the speed of the two-wheeler does not mean that braking is required, and the processor can determine that no auxiliary braking is needed.

[0107] In some embodiments of the present specification, providing auxiliary braking for the user based on the road characteristics and the speed change of the two-wheeler can obtain a better auxiliary braking effect and enhance the riding experience of the user.

[0108] In some embodiments, the braking force and braking time of the two-wheeler can also be related to the collision risk of the two-wheeler. For example, when the collision risk is greater, the braking force of the two-wheeler is greater, and the braking time is shorter.

[0109] In some embodiments, the braking force and braking time of the two-wheeler can also be related to the response time of the braking force control module of the two-wheeler. For example, when the response time is longer, it indicates that the braking performance of the two-wheeler may be worse, and the corresponding braking force can be greater, and the braking time is shorter.

[0110] In some embodiments, the braking force and braking time of the two-wheeler can also be related to the braking stability and braking smoothness of the braking force control module of the two-wheeler.

[0111] The braking stability refers to the ability of the two-wheeler to maintain a stable state during braking.

[0112] The braking smoothness refers to the speed and smoothness of the increase in braking torque, as well as the speed and thoroughness of the release of the braking torque.

[0113] Exemplarily, when the braking stability and braking smoothness of the braking control module of the two-wheeler are greater, it indicates that the two-wheeler is more stable during braking, and accordingly, the braking force can be greater and the braking time can be shorter.

[0114] In some embodiments, the two-wheeler braking system 100 may further include a communication module 250.

[0115] The communication module 250 is configured to obtain auxiliary motion information.

[0116] The auxiliary motion information refers to the motion information of the two-wheeler obtained based on other means (such as the terminal 130).

[0117] In some embodiments, the processor may determine the braking force and braking time based on the motion information of the two-wheeler, the collision risk, the response time, and the auxiliary motion information. For example, the processor may correct the motion information of the two-wheeler based on the motion information and the auxiliary motion information of the two-wheeler; and determine the braking force and braking time based on the corrected motion information of the two-wheeler, the collision risk, and the response time.

[0118] In some embodiments of the present specification, by correcting the motion information of the two-wheeler through the auxiliary motion information obtained based on other means, the accuracy of the motion information of the two-wheeler can be improved, and a better braking effect can be obtained.

[0119] In some embodiments, the sensing module 210 further includes a pressure sensor. The pressure sensor may be deployed in the two-wheeler seat cushion and is configured to obtain the weight information of the riding user.

[0120] In some embodiments, the processor determines the braking force and braking time based on the weight information of the riding user. For example, when the weight of the riding user is greater, the corresponding braking force is greater and the braking time is shorter.

[0121] It should be noted that the foregoing embodiments are only for convenience of description. The processor may determine the braking force and braking time of the two-wheeler based on one of the foregoing embodiments or a combination of multiple embodiments. In the present specification, by determining the braking force and braking time of the two-wheeler through the above one embodiment or multiple embodiments, a better braking effect can be obtained while ensuring the riding experience of the user, and the braking safety can be improved.

[0122] In some embodiments, the two-wheeler braking system 100 further includes a feedback module 240.

[0123] The feedback module 240 is configured to obtain the braking preference information of the user.

[0124] The braking preference information refers to the braking preference of the user under different collision risks.

[0125] In some embodiments, the braking preference information can be obtained based on user input. For example, the user can input the braking force and braking time under various collision risks.

[0126] In some embodiments, the braking preference information can also be obtained according to the braking data of the user during historical driving. Exemplarily, the braking preference information can be expressed as ((P1, X1, Y1, Z1), (P2, X2, Y2, Z2),...). Wherein, P1, P2,... respectively represent different collision risks, X1, X2,... respectively represent the user's braking frequencies corresponding to each collision risk, Y1, Y2,... respectively represent the user's average braking forces corresponding to different collision risks, and Z1, Z2,... respectively represent the user's average braking times corresponding to different collision risks.

[0127] In some embodiments, the processor can determine the braking force and braking time based on the motion information of the two-wheeler, the collision risk, the response time of the braking force control module, and the braking preference information. For example, the processor can determine the braking force and braking time based on the motion information of the two-wheeler, the collision risk, and the response time of the braking force control module, and correct the braking force and braking time based on the user's braking preference information.

[0128] In some embodiments of the present specification, by obtaining the user's braking preference information and adjusting the braking force and time, the personalized braking needs of the user can be met, a better braking effect can be obtained, and user satisfaction can be improved.

[0129] In some embodiments, the preset conditions satisfied by the collision risk can also be related to the user's braking preference information.

[0130] For example, the processor can determine the first preset threshold of the preset conditions as the collision risk value with the highest user braking frequency in the user's braking preference information. That is, when the collision risk is greater than the collision risk value with the highest user braking frequency, it is determined that the user needs to brake.

[0131] In some embodiments, the preset conditions can also be related to one or more of the response time of the braking force control module, braking stability, braking smoothness, the user's weight information, the user's riding posture, and the tilt posture of the two-wheeler.

[0132] For example, the longer the response time of the braking force control module, the smaller the first preset threshold of the preset conditions; the smaller the braking stability, the smaller the first preset threshold of the preset conditions; the smaller the braking smoothness, the smaller the first preset threshold of the preset conditions; the greater the user's weight information, the smaller the first preset threshold of the preset conditions; the more stable the user's riding posture and the tilt posture of the two-wheeler, the greater the first preset threshold of the preset conditions, etc.

[0133] In some embodiments, the preset conditions are also related to the road characteristics before collision corresponding to historical collision accidents and the movement characteristics of the two-wheeler before collision.

[0134] The road characteristics before collision refer to the road environmental characteristics before the historical collision accident occurs.

[0135] In some embodiments, the preset conditions may include that the collision risk is greater than a first preset threshold or the similarity between the road characteristics and the two-wheeler movement characteristics at the current moment and the road characteristics before collision and the two-wheeler movement characteristics corresponding to any historical collision accident is greater than a second preset threshold.

[0136] It can be understood that by taking the road characteristics and the two-wheeler movement characteristics corresponding to historical collision accidents as a reference, braking assistance can be provided for users in accident-prone sections, obtaining a better braking effect and ensuring user safety.

[0137] In some embodiments, the preset conditions are also related to the road characteristics of a preset duration before collision and the movement characteristics of the two-wheeler of a preset duration before collision corresponding to historical collision accidents.

[0138] In some embodiments, the preset duration is related to the movement information of the two-wheeler. For example, the faster the movement speed of the two-wheeler, the shorter the preset duration, etc.

[0139] In some embodiments, the processor may generate a braking instruction based on the braking force and the braking time.

[0140] In some embodiments, the processor may also send the braking instruction to the braking force control module 220, and the braking force control module 220 controls the braking of the two-wheeler based on the braking instruction.

[0141] Step 360, end.

[0142] It should be noted that the above description of the process 300 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 300 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0143] The two-wheeler braking system, method and device described in some embodiments of this specification can at least achieve the following effects: (1) Determine the collision risk according to the movement information of the two-wheeler and the road sensing information, provide emergency braking for users, improve the safety of users, and have less impact on the riding comfort of users; (2) By collecting the braking preference information of users, the personalized braking needs of users can be met, a better braking effect can be obtained, and user satisfaction can be improved.

[0144] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0145] Meanwhile, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0146] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this specification are not used to limit the order of the processes and methods of this specification. Although some currently considered useful embodiments have been discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0147] Similarly, it should be noted that, in order to simplify the expression of the disclosure of this specification and thus help the understanding of one or more embodiments, in the previous description of the embodiments of this specification, sometimes multiple features are merged into one embodiment, drawing, or its description. However, this disclosure method does not mean that the features required by the subject of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0148] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used in the description of embodiments are, in some examples, modified by the modifiers "about", "approximately" or "substantially". Unless otherwise stated, "about", "approximately" or "substantially" indicate that the said numbers are allowed to have a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0149] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes the application history files that are inconsistent with or conflict with the content of this specification, as well as the files that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0150] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered to be consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A two-wheeler braking system, characterized in that, The system includes a sensing module, a braking force control module, a motion detection module, and a processor, and the processor is configured to: Based on first data, determine whether the two-wheeled vehicle is in a moving state; the first data includes the motion information of the two-wheeled vehicle; In response to the two-wheeled vehicle being in a moving state, determine the collision risk of the two-wheeled vehicle; the collision risk is determined based on the first data and second data; the second data includes road sensing data; When the collision risk meets a preset condition, generate a braking instruction based on the collision risk and send the braking instruction to the braking force control module.

2. The system according to claim 1, characterized in that, The motion detection module is configured to obtain the first data; the sensing module is configured to obtain the second data; the processor is further configured to: Determine the collision risk based on the road sensing data and the motion information of the two-wheeled vehicle.

3. The system according to claim 2, characterized in that, The processor is further configured to: Based on the road sensing data, determine obstacles within a preset range and characteristic parameters of the obstacles; the characteristic parameters include at least one of the obstacle type, the moving speed of the obstacle, and the distance between the obstacle and the current position of the two-wheeled vehicle; Based on the characteristic parameters and the motion information of the two-wheeled vehicle, determine the collision risk.

4. The system according to claim 3, wherein The characteristic parameters further include the relative path between the obstacle and the two-wheeled vehicle; the relative path includes the relative position relationship and relative motion trajectory between the obstacle and the two-wheeled vehicle.

5. The system according to claim 3, characterized in that, The processor is further configured to: Based on the characteristic parameters and the motion information of the two-wheeled vehicle, determine the collision risk through a collision risk prediction model, and the collision risk prediction model is a machine learning model.

6. The system according to claim 1, wherein, The processor is further configured to: Based on the motion information of the two-wheeled vehicle, the collision risk, and the response time of the braking force control module, determine the braking force and braking time; Generate the braking instruction based on the braking force and the braking time.

7. The system according to claim 6, wherein The system further includes a feedback module, and the feedback module is configured to obtain braking preference information; the processor is further configured to: Based on the motion information of the two-wheeled vehicle, the collision risk, the response time of the braking force control module, and the braking preference information, determine the braking force and the braking time.

8. The system according to claim 1, wherein The preset condition is related to the braking preference information.

9. A braking method for a two-wheeled vehicle, characterized in that, The method is executed based on the two-wheeled vehicle braking system according to any one of claims 1 to 8, and the method includes: Based on first data, determine whether the two-wheeled vehicle is in a moving state; the first data includes the motion information of the two-wheeled vehicle; In response to the two-wheeled vehicle being in a moving state, determine the collision risk of the two-wheeled vehicle; the collision risk is determined based on the first data and second data; the second data includes road sensing data; When the collision risk meets a preset condition, generate a braking instruction based on the collision risk and send the braking instruction to the braking force control module.

10. A braking device for a two-wheeled vehicle, characterized in that, The device includes at least one storage medium and at least one processor; The at least one storage medium is used to store computer instructions; The at least one processor is configured to execute the computer instructions to implement the two-wheeler braking method as claimed in claim 9.

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