Vehicle data processing method

Abstract

A vehicle data processing method comprises: acquiring vehicle travel information of a vehicle; and on the basis of the vehicle travel information, determining a data sampling frequency corresponding to operation state information of the vehicle, and collecting the operation state information according to the data sampling frequency. According to the vehicle data processing method, on the basis of the vehicle travel information of a vehicle, a data sampling frequency corresponding to operation state information of the vehicle is determined, and the operation state information of the vehicle is sampled according to different data sampling frequencies.

Description

Vehicle data processing methods

[0001] Cross-reference to related applications

[0002] This application is based on and claims priority to Chinese Patent Application No. 2024112109526, filed on August 29, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of vehicle technology, and more specifically, to a vehicle data processing method, a seat belt control method, and a vehicle. Background Technology

[0004] With the continuous development of electric vehicle technology, vehicle systems integrate a variety of sensors, controllers and actuators to achieve intelligent control of functions such as vehicle acceleration, braking, steering, suspension and seat belt control, so as to improve vehicle handling, safety and comfort.

[0005] For example, an intelligent seatbelt system is a vehicle-wide seatbelt usage system that collects numerous signals from sensors to determine the vehicle's status and determine the appropriate actions the seatbelts should take. For instance, if radar or external cameras detect a potential collision risk from a person or vehicle nearby, the vehicle's control system, such as the Electronic Control Unit (ECU), assesses the risk level and determines whether to trigger the seatbelt locking mechanism. If the assessment is high-risk, the locking mechanism is triggered to restrain the webbing movement.

[0006] Vehicle systems not only need to sense and analyze numerous signals, but also need to output corresponding control states to external devices and coordinate the actions of various vehicle systems. However, the system's processing capacity is limited, making it unable to process data in a timely and effective manner, which affects the real-time performance and response speed of the vehicle system. Summary of the Invention

[0007] This disclosure aims to address at least one of the technical problems existing in the prior art. To this end, this disclosure proposes a vehicle data processing method, a seat belt control method, and a vehicle, which can process data in a timely and effective manner, ensuring the real-time performance and response speed of the vehicle system.

[0008] This disclosure provides a vehicle data processing method, the method comprising:

[0009] Obtain vehicle driving information;

[0010] Based on the vehicle driving information, the data sampling frequency of the vehicle's operating status information is determined, and the operating status information is collected according to the data sampling frequency.

[0011] According to one embodiment of this disclosure, the operating status information includes at least one of driving speed information, vehicle gear information, vehicle posture information, and driving environment information.

[0012] According to one embodiment of this disclosure, the vehicle driving information includes driving mode information, the operating status information includes driving speed information, and the step of determining the data sampling frequency of the vehicle's operating status information based on the vehicle driving information includes:

[0013] Based on the driving mode information, the maximum driving speed of the vehicle is determined;

[0014] The data sampling frequency of the driving speed information is determined based on the vehicle's maximum driving speed.

[0015] According to one embodiment of this disclosure, determining the data sampling frequency of the driving speed information based on the driving speed limit includes:

[0016] If it is determined that the upper limit of the vehicle's driving speed is less than a first speed threshold, the data sampling frequency corresponding to the driving speed information is determined to be the first sampling frequency;

[0017] Alternatively, if it is determined that the upper limit of the vehicle's driving speed is greater than or equal to the first speed threshold, the data sampling frequency corresponding to the driving speed information is determined to be the second sampling frequency, where the first sampling frequency is less than the second sampling frequency.

[0018] According to one embodiment of this disclosure, the vehicle driving information includes road condition information, and the operating status information includes at least one of driving speed information, vehicle posture information, and driving environment information. The step of determining the data sampling frequency of the vehicle's operating status information based on the vehicle driving information includes:

[0019] Based on the driving road condition information, the road scenario in which the vehicle is traveling is determined;

[0020] Based on the road scenario in which the vehicle is traveling, the data sampling frequency of at least one of the driving speed information, the vehicle posture information, and the driving environment information is determined.

[0021] According to one embodiment of this disclosure, determining the data sampling frequency of at least one of the driving speed information, the vehicle posture information, and the driving environment information based on the road scene in which the vehicle is driving includes:

[0022] When it is determined that the vehicle is traveling in a first road scenario, the data sampling frequency corresponding to the driving speed information is determined to be the third sampling frequency, and the data sampling frequency corresponding to the vehicle posture information is determined to be the fifth sampling frequency;

[0023] Alternatively, if it is determined that the vehicle is traveling in a second road scenario, the data sampling frequency corresponding to the driving speed information is determined to be the fourth sampling frequency, and the data sampling frequency corresponding to the vehicle posture information is determined to be the sixth sampling frequency.

[0024] The third sampling frequency is greater than the fourth sampling frequency, the fifth sampling frequency is less than the sixth sampling frequency, and the average slope of the first road scene is less than the average slope of the second road scene.

[0025] According to one embodiment of this disclosure, determining the data sampling frequency of at least one of the driving speed information, the vehicle posture information, and the driving environment information based on the road scene in which the vehicle is driving includes:

[0026] When it is determined that the vehicle is traveling in a third road scenario, the data sampling frequency corresponding to the first state information of the vehicle is determined to be the seventh sampling frequency, and the first state information includes at least one of the vehicle posture information and the driving environment information.

[0027] Alternatively, if it is determined that the vehicle is traveling in a fourth road scenario, the data sampling frequency corresponding to the first state information is determined to be the eighth sampling frequency;

[0028] The eighth sampling frequency is greater than the seventh sampling frequency, and the road complexity of the fourth road scenario is greater than that of the third road scenario.

[0029] According to one embodiment of this disclosure, road complexity is determined based on obstacle information and slope information in the driving road condition information.

[0030] This disclosure also provides a method for controlling a seat belt, including:

[0031] Obtain vehicle driving information;

[0032] Based on the vehicle driving information, the data sampling frequency of the vehicle's operating status information is determined, and the operating status information is collected according to the data sampling frequency;

[0033] Based on the operating status information, the seat belts of the vehicle are controlled to operate.

[0034] According to one embodiment of this disclosure, after obtaining the vehicle's driving information, the method further includes:

[0035] Based on the vehicle driving information, the control response speed of the vehicle is determined;

[0036] The step of controlling the seatbelt operation of the vehicle based on the operating status information includes:

[0037] Based on the operating status information, the seat belt is controlled to move according to the control response speed.

[0038] According to one embodiment of this disclosure, controlling the seatbelt operation of the vehicle based on the operating status information includes:

[0039] If the operating status information meets the first action triggering condition, the seat belt is controlled to perform the first action, and the operating status information is acquired.

[0040] If the operating status information meets the triggering condition for the second action, the seat belt is controlled to switch from performing the first action to performing the second action, whereby the priority of the second action is greater than that of the first action.

[0041] According to one embodiment of this disclosure, the seat belt includes a mechanical vehicle sensing mechanism and an electronic vehicle sensing mechanism, and controlling the seat belt operation of the vehicle based on the operating status information includes:

[0042] When the operating status information meets the locking trigger condition, the electronic vehicle sensing mechanism of the seat belt is controlled to perform a locking action;

[0043] Alternatively, if the operating status information does not meet the locking trigger condition, the mechanical sensing mechanism of the seat belt may be inhibited from performing its action.

[0044] This disclosure provides a vehicle data processing apparatus, the apparatus comprising:

[0045] The first acquisition module is used to acquire vehicle driving information;

[0046] The second processing module is used to determine the data sampling frequency of the vehicle's operating status information based on the vehicle's driving information, and to collect the operating status information according to the data sampling frequency.

[0047] This disclosure provides a seat belt control device, the device comprising:

[0048] The second acquisition module is used to acquire vehicle driving information;

[0049] The second processing module is used to determine the data sampling frequency of the vehicle's operating status information based on the vehicle driving information, and to collect the operating status information according to the data sampling frequency;

[0050] The control module is used to control the seat belt operation of the vehicle based on the operating status information.

[0051] This disclosure provides a vehicle, including:

[0052] A sensing system, wherein the sensing system is used to collect the operating status information of the vehicle;

[0053] As described above, the vehicle data processing device is connected to the sensing system.

[0054] This disclosure also provides a vehicle, including:

[0055] A sensing system and seat belts, wherein the sensing system is used to collect the vehicle's operating status information;

[0056] As described above, the control device for the seat belt is connected to the sensing system and the seat belt.

[0057] According to one embodiment of this disclosure, the seat belt includes a mechanical vehicle sensing mechanism and an electronic vehicle sensing mechanism.

[0058] This disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle data processing method or the seat belt control method described above.

[0059] This disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle data processing method or seat belt control method described above.

[0060] This disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle data processing method or seat belt control method described above.

[0061] The above-mentioned one or more technical solutions in this disclosure have at least the following technical effects: by determining the data sampling frequency corresponding to the vehicle's operating status information through vehicle driving information, and sampling the vehicle's operating status information according to different data sampling frequencies, data can be processed in a timely and effective manner, the overall vehicle status can be accurately judged, and the real-time performance and response speed of the vehicle system can be guaranteed.

[0062] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0063] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0064] Figure 1 is a schematic flowchart of the vehicle data processing method provided in an embodiment of this disclosure;

[0065] Figure 2 is a schematic flowchart of one of the seat belt control methods provided in this embodiment of the present disclosure;

[0066] Figure 3 is a system schematic diagram of the vehicle provided in an embodiment of this disclosure;

[0067] Figure 4 is a second schematic flowchart of the seat belt control method provided in this embodiment of the present disclosure;

[0068] Figure 5 is a third schematic flowchart of the seat belt control method provided in this embodiment of the present disclosure;

[0069] Figure 6 is a fourth flowchart illustrating the seat belt control method provided in this embodiment of the present disclosure;

[0070] Figure 7 is one of the structural schematic diagrams of the seat belt provided in the embodiment of this disclosure;

[0071] Figure 8 is a second structural schematic diagram of the seat belt provided in an embodiment of this disclosure;

[0072] Figure 9 is a third structural schematic diagram of the seat belt provided in the embodiment of this disclosure;

[0073] Figure 10 is a schematic diagram of the structure of the vehicle data processing device provided in an embodiment of this disclosure;

[0074] Figure 11 is a schematic diagram of the structure of the seat belt control device provided in an embodiment of this disclosure;

[0075] Figure 12 is a schematic diagram of the structure of the electronic device provided in an embodiment of this disclosure;

[0076] Figure 13 is one of the structural schematic diagrams of the vehicle provided in the embodiments of this disclosure;

[0077] Figure 14 is a second structural schematic diagram of the vehicle provided in an embodiment of this disclosure;

[0078] Figure 15 is a structural schematic diagram of the seat belt provided in an embodiment of this disclosure.

[0079] Reference numerals: Ratchet tooth 604, rocker arm 605, valve core 606, first relay 607, steel ball 609, vehicle-sensing follow-up pawl 610, second relay 611, locking lever 612, vehicle data processing device 1000, control device 1100, vehicle 1300, sensing system 1310, seat belt 1320, mechanical vehicle sensing mechanism 1321, electronic vehicle sensing mechanism 1322. Detailed Implementation

[0080] The technical solutions of the embodiments of this disclosure will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure are within the scope of protection of this disclosure.

[0081] The terms "first," "second," etc., used in this disclosure and in the claims are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this disclosure can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0082] The following description, in conjunction with the accompanying drawings, details the vehicle 1300 data processing method, seat belt 1320 control method, vehicle data processing device 1000, seat belt 1320 control device 1100, vehicle 1300, electronic device, and readable storage medium provided in this disclosure through specific embodiments and application scenarios.

[0083] The data processing method for vehicle 1300 can be applied to vehicle 1300, and can be executed by hardware or software in vehicle 1300.

[0084] As shown in Figure 3, the system of vehicle 1300 can be divided into a perception layer, a control layer and an execution layer. The perception layer can collect numerous signals from the vehicle's domain controller, Electronic Stability Program (ESP), Advanced Driver Assistance System (ADAS), radar, vehicle attitude and other sensing devices.

[0085] The Electronic Control Unit (ECU) in the control layer can analyze numerous signals from the sensing layer to determine the overall vehicle status and provide corresponding actions to be executed in the execution layer.

[0086] In actual implementation, the control layer may include a power module, an information parsing module, a self-protection module, a status monitoring module, a drive module, an event recording module, etc. The control layer can not only monitor and analyze various signals, but also output corresponding statuses to devices such as the seat belt 1320, coordinate with the actions of various systems on the vehicle 1300, and make corresponding records.

[0087] As shown in Figure 1, the data processing method for vehicle 1300 includes steps 110 and 120.

[0088] Step 110: Obtain vehicle driving information for vehicle 1300.

[0089] Among them, vehicle driving information is information that represents the current driving status of vehicle 1300, which can be obtained through the perception layer of vehicle 1300.

[0090] In this embodiment, the vehicle driving information includes at least one of driving mode information and road condition information. The driving mode information of vehicle 1300 can be the driving mode currently being driven by vehicle 1300, and the road condition information of vehicle 1300 can be the type of road condition currently being driven by vehicle 1300.

[0091] In actual use, the driving modes of the vehicle 1300 include, but are not limited to, Sport mode, Eco mode, Standard mode, Off-road mode, Low-speed Off-road mode, and Snow mode. Each driving mode has unique characteristics and applicable scenarios, and the driver of the vehicle 1300 can choose different driving modes according to actual needs and road conditions.

[0092] For example, the Sport mode is suitable for vehicles with low RPMs and wide, flat roads. When Sport mode is activated, the electronic control unit will increase the engine speed. The torque at 1300 RPM is sufficient to enable quick start and stop.

[0093] For example, the economy mode, also known as the energy-saving mode, is suitable for low-to-medium speed driving conditions. By reducing the response speed of power output, it reduces unnecessary fuel consumption and makes the vehicle speed more stable.

[0094] For example, the off-road mode is suitable for rough roads and slippery surfaces. When the off-road mode is activated, the drive system will brake the wheels that are slipping or spinning, improving the vehicle's off-road performance.

[0095] The road conditions that vehicle 1300 travels on can be classified according to specific road types, including but not limited to highways, mountain roads, water-crossing roads, bridges and tunnels, or according to road quality. The road conditions that vehicle 1300 travels on are diverse and have a significant impact on its driving.

[0096] For example, highways have smooth surfaces, high traffic volume, and vehicles travel at relatively high speeds.

[0097] For example, mountain roads are winding and steep, which can limit the driver's visibility when the vehicle is traveling at 1300 km / h. There may be risks of natural disasters such as falling rocks and landslides. The vehicle travels at a relatively slow speed and needs to maintain stability.

[0098] For example, bridges are prone to icing due to rain and snow, and tunnels are dark and slippery, requiring vehicles to maintain a speed of 1300 km / h and avoid emergency braking and lane changes.

[0099] It is understandable that different vehicles 1300 have different driving mode selections, and the vehicle 1300 can switch to different driving modes when driving on different road surfaces.

[0100] In this step, you can obtain the driving mode information of vehicle 1300, the road condition information of vehicle 1300, and both the driving mode information and the road condition information of vehicle 1300.

[0101] In actual operation, vehicle driving information such as driving mode information and road condition information of vehicle 1300 can be obtained from multiple terminals in the perception layer of vehicle 1300.

[0102] Step 120: Based on the vehicle driving information, determine the data sampling frequency of the vehicle 1300's operating status information, and collect the operating status information according to the data sampling frequency.

[0103] Among them, the operating status information of vehicle 1300 can be information that can characterize the overall vehicle status during the operation of vehicle 1300, such as vehicle speed, vehicle posture, vehicle gear, surrounding environment, etc. The operating status information of vehicle 1300 can be collected through the perception layer of vehicle 1300.

[0104] In this step, based on the vehicle driving information of vehicle 1300, the data sampling frequency corresponding to the operating status information of vehicle 1300 is analyzed, and then the corresponding operating status information is collected according to the data sampling frequency to analyze and obtain the current vehicle status.

[0105] In actual implementation, the data sampling frequency corresponding to the operating status information of vehicle 1300 can be determined by analyzing the driving mode information of vehicle 1300, or by analyzing the road condition information of vehicle 1300, or by combining the driving mode information and road condition information of vehicle 1300.

[0106] It is understandable that there may be multiple types of operating status information for vehicle 1300. When determining the data sampling frequency based on the vehicle driving information of vehicle 1300, one type of operating status information data sampling frequency can be determined, or multiple types of operating status information can be determined, and operating status information can be collected according to their respective data sampling frequencies.

[0107] For example, the operating status information of vehicle 1300 includes signal A, signal B and signal C. When the driving mode of vehicle 1300 is switched to off-road mode, the data sampling frequency of signal A can be increased, while the data sampling frequency of signal B and signal C can be decreased. This reduces the amount of data in the operating status information while increasing the proportion of important data in the operating status information. The operating status information obtained by sampling according to the data sampling frequency can more accurately reflect the status of the whole vehicle.

[0108] It should be noted that the data sampling frequency of the operating status information is determined based on the vehicle driving information of vehicle 1300. When the vehicle driving information changes, the data sampling frequency of the operating status information can be adjusted accordingly.

[0109] In related technologies, the control system not only needs to sense and analyze numerous signals, but also needs to output corresponding control states to external devices, and also needs to coordinate the actions of various systems in the vehicle. The system's processing capacity is limited, and it cannot process data in a timely and effective manner, affecting the real-time performance and response speed of the vehicle system.

[0110] In this embodiment of the disclosure, the data sampling frequency corresponding to the operating status information of vehicle 1300 is obtained by analyzing the vehicle driving information of vehicle 1300. Under different vehicle driving information, the operating status information of vehicle 1300 is sampled and analyzed according to different data sampling frequencies. This can reduce the amount of data in the operating status information, while also increasing the proportion of important data in the operating status information, accurately judging the overall vehicle status, and processing data in a timely and effective manner to ensure the real-time performance and response speed of the vehicle system.

[0111] According to the vehicle 1300 data processing method provided in this disclosure embodiment, the data sampling frequency corresponding to the vehicle 1300's operating status information is determined through vehicle driving information. By sampling the vehicle 1300's operating status information according to different data sampling frequencies, data can be processed in a timely and effective manner, the overall vehicle status can be accurately judged, and the real-time performance and response speed of the vehicle system can be guaranteed.

[0112] In some embodiments, the operating status information includes at least one of driving speed information, vehicle gear information, vehicle posture information, and driving environment information.

[0113] The driving speed information includes the vehicle's current speed, average speed, and current acceleration.

[0114] The vehicle gear information is the vehicle's current gear, such as reverse, neutral, parking, and speed gear (different speed gears correspond to different vehicle speeds and power requirements).

[0115] Vehicle attitude information is used to characterize the current position and posture of the vehicle. Vehicle attitude information can include information such as tilt angle. Vehicle attitude information can be obtained by sampling through inertial sensors, ranging sensors and other sensors on the vehicle 1300.

[0116] Driving environment information is used to characterize the environment in which vehicle 1300 is located. Driving environment information may include obstacle information, slope information, etc. in the environment in which vehicle 1300 is located.

[0117] In this embodiment, the operating status information of vehicle 1300 may include a combination of one or more of driving speed information, vehicle gear information, vehicle body posture information and driving environment information. When determining the data sampling frequency of operating status information based on the vehicle driving information of vehicle 1300, the data sampling frequency corresponding to one or more of driving speed information, vehicle gear information, vehicle body posture information and driving environment information can be determined, and status information can be collected according to the corresponding data sampling frequency.

[0118] It should be noted that, based on the vehicle driving information of vehicle 1300, the types of operating status information to be collected can be adjusted. When the vehicle driving information changes, some types of operating status information will no longer be collected, thus reducing the amount of data to be processed.

[0119] For example, at the first moment, the operating status information of vehicle 1300, including signal A, signal B and signal C, is collected according to the driving mode of vehicle 1300. At the second moment, the driving mode of vehicle 1300 is switched, and the operating status information of vehicle 1300, including signal A, signal B and signal C, is no longer collected. That is, the data sampling frequency of signal B and signal C is adjusted to 0.

[0120] In some embodiments, vehicle driving information includes driving mode information, and operating status information includes driving speed information. Step 120, determining the data sampling frequency of the operating status information of vehicle 1300 based on the vehicle driving information, may include:

[0121] Based on driving mode information, the maximum driving speed of the vehicle is determined to be 1300.

[0122] Based on the vehicle's maximum speed of 1300, the data sampling frequency for speed information is determined.

[0123] In this embodiment, the driving mode information and the upper limit of the driving speed of vehicle 1300 are related. The upper limit of the driving speed of vehicle 1300 can be the maximum speed that vehicle 1300 can reach in the current driving mode.

[0124] It is understandable that, based on the maximum speed that vehicle 1300 can reach in the current driving mode, it can be determined whether vehicle 1300 is operating at high speed or low speed.

[0125] When the vehicle is running at high speed, the extreme value of the 1300 speed is large and the range of speed variation is also large. The driving speed information has a significant impact on the control of the 1300 speed. Therefore, the data sampling frequency of the driving speed information can be increased. When the vehicle is running at low speed, the extreme value of the 1300 speed is small and the range of speed variation is also small. The driving speed information has a smaller impact on the control of the 1300 speed. Therefore, the data sampling frequency of the driving speed information can be reduced.

[0126] In this embodiment, the upper limit of driving speed is determined based on the driving mode information of vehicle 1300, the driving speed of vehicle 1300 is judged, and different data sampling frequencies are set for the driving speed information of vehicle 1300 according to different driving speed conditions of vehicle 1300, so as to ensure the real-time performance and response speed of the vehicle system.

[0127] In some embodiments, determining the data sampling frequency for driving speed information based on the upper limit of the driving speed of vehicle 1300 may include:

[0128] If it is determined that the upper limit of the driving speed of vehicle 1300 is less than the first speed threshold, the data sampling frequency corresponding to the driving speed information of vehicle 1300 is determined as the first sampling frequency.

[0129] Alternatively, if the upper limit of the vehicle's speed is determined to be greater than or equal to the first speed threshold, the data sampling frequency corresponding to the speed information is determined to be the second sampling frequency, and the first sampling frequency is less than the second sampling frequency.

[0130] The first speed threshold is a pre-set critical value for driving speed, and different vehicles can correspond to different first speed thresholds.

[0131] When the driving speed limit corresponding to the driving mode information is less than the first speed threshold, the data sampling frequency corresponding to the driving speed information of vehicle 1300 is the first sampling frequency. When the driving speed limit corresponding to the driving mode information is greater than or equal to the first speed threshold, the data sampling frequency corresponding to the driving speed information of vehicle 1300 is the second sampling frequency. The first sampling frequency is less than the second sampling frequency.

[0132] In this embodiment, when it is determined from the driving mode information that the upper limit of the driving speed of vehicle 1300 is less than the first speed threshold, it indicates that the current driving mode of vehicle 1300 has a low speed. The data sampling frequency corresponding to the driving speed information in the operating status information of vehicle 1300 can be reduced. The data sampling frequency of other types of information in the operating status information of vehicle 1300 can remain unchanged or be appropriately adjusted to reduce the amount of data processed by vehicle 1300, which helps to reduce the judgment time and achieve rapid control.

[0133] The following is a specific example.

[0134] According to the driving mode information, vehicle 1300 is in low-speed off-road mode. At this time, the vehicle speed is limited to a first speed threshold of 40km / h to provide stronger off-road performance and traction. The data sampling frequency of driving speed information is the first sampling frequency.

[0135] Understandably, given the maximum driving speed, there are fewer situations where speed-related dangers occur. When sensing signals, the sampling frequency of driving speed information such as speed and acceleration can be reduced, which helps to reduce the amount of data processed by the ECU, shorten the ECU's decision-making time, and quickly and accurately control the seat belt 1320 action.

[0136] According to the driving mode information, when the vehicle switches to off-road mode at 1300, the vehicle speed is limited to the first speed threshold of 40km / h, and the data sampling frequency of the driving speed information is increased to the second sampling frequency.

[0137] In some embodiments, vehicle driving information includes road condition information, and operating status information includes at least one of driving speed information, vehicle posture information, and driving environment information. Based on the vehicle driving information, determining the data sampling frequency of the operating status information of vehicle 1300 includes:

[0138] Based on the driving road condition information, the road scenario in which vehicle 1300 is traveling is determined;

[0139] Based on the road scenario of vehicle 1300 driving, determine the data sampling frequency of at least one of the driving speed information, vehicle posture information and driving environment information.

[0140] In this embodiment, the road scenario in which the vehicle 1300 is traveling can be determined based on the road condition information of the vehicle 1300, and the data sampling frequency of the corresponding operating status information can be determined for different road scenarios.

[0141] It is understandable that when the vehicle 1300 is driving in different road scenarios, the driving speed information, vehicle posture information and driving environment information have different degrees of impact on the control of the vehicle 1300. According to different road scenarios, the data acquisition frequency corresponding to the driving speed information, vehicle posture information and driving environment information is set to ensure the real-time performance and response speed of the vehicle system.

[0142] In some embodiments, determining the data sampling frequency of at least one of driving speed information, vehicle posture information, and driving environment information based on the road scenario in which the vehicle 1300 is driving may include:

[0143] Based on the scenario where vehicle 1300 is traveling on the first road, the data sampling frequency corresponding to the driving speed information of vehicle 1300 is determined to be the third sampling frequency, and the data sampling frequency corresponding to the body posture information of vehicle 1300 is determined to be the fifth sampling frequency.

[0144] Alternatively, based on the premise that vehicle 1300 is traveling on the second road scenario, the data sampling frequency corresponding to the driving speed information is determined to be the fourth sampling frequency, the data sampling frequency corresponding to the vehicle posture information is determined to be the sixth sampling frequency, the third sampling frequency is greater than the fourth sampling frequency, the fifth sampling frequency is less than the sixth sampling frequency, and the average slope of the first road scenario is less than the average slope of the second road scenario.

[0145] In this embodiment, the first road scenario and the second road scenario can be distinguished based on the average road surface slope in the driving road condition information. The average slope of the first road scenario is less than that of the second road scenario. The first road scenario is a gently changing road surface with very few steep slopes. The second road scenario has more steep road surfaces compared to the first road scenario.

[0146] In actual implementation, a slope threshold corresponding to the average slope can be preset. When the average slope of the road surface in the driving road condition information is less than the slope threshold, it indicates that vehicle 1300 is driving in the first road scenario. When the average slope of the road surface in the driving road condition information is greater than or equal to the slope threshold, it indicates that vehicle 1300 is driving in the second road scenario.

[0147] Based on the average road slope in the driving condition information, when it is determined that vehicle 1300 is driving in the first road scenario, the data sampling frequency corresponding to the driving speed information is set to the third sampling frequency, and the data sampling frequency corresponding to the vehicle posture information is set to the fifth sampling frequency.

[0148] Based on the average road slope in the driving road condition information, when vehicle 1300 switches from the first road scenario to the second road scenario, the data sampling frequency corresponding to the driving speed information is reduced, and the data sampling frequency corresponding to the vehicle body posture information is increased. The data sampling frequency corresponding to the driving speed information is set to the fourth sampling frequency, and the data sampling frequency corresponding to the vehicle body posture information is set to the sixth sampling frequency to cope with the increased road conditions with sloping surfaces.

[0149] In this embodiment, when it is determined from the road condition information that the vehicle 1300 is traveling on the first road scenario, it rarely travels on steep slopes. This can reduce the data sampling frequency corresponding to vehicle tilt angle and other vehicle posture information. At the same time, the vehicle speed may be relatively high, so the data sampling frequency corresponding to driving speed information can be increased, reducing the amount of data processed by the vehicle 1300. Meanwhile, the proportion of important perception signals can be increased, which can quickly and accurately control the action of the seat belt 1320.

[0150] The following is a specific example.

[0151] Based on the road condition information, it is determined that vehicle 1300 is traveling on a paved highway. There are few inclined surfaces with a slope exceeding 27 degrees on paved highways, so the data sampling frequency corresponding to vehicle tilt angle and other body posture information can be reduced. At the same time, since there are many vehicles 1300 on paved highways with high speeds, the data sampling frequency corresponding to speed information such as speed and acceleration can be appropriately increased. When a danger is detected by analyzing the operating status information of vehicle 1300 (for example, the speed exceeds a set threshold), the seat belt 1320 locking signal is triggered, and the seat belt 1320 is locked.

[0152] In some embodiments, determining the data sampling frequency of at least one of driving speed information, vehicle posture information, and driving environment information based on the road scenario in which the vehicle 1300 is driving may include:

[0153] When it is determined that vehicle 1300 is driving in a third road scenario, the data sampling frequency corresponding to the first state information of vehicle 1300 is determined to be the seventh sampling frequency. The first state information includes at least one of vehicle body posture information and driving environment information of vehicle 1300.

[0154] Alternatively, if it is determined that vehicle 1300 is traveling in the fourth road scenario, the data sampling frequency corresponding to the first state information is determined to be the eighth sampling frequency. The eighth sampling frequency is greater than the seventh sampling frequency, and the road complexity of the fourth road scenario is greater than that of the third road scenario.

[0155] The first state information includes vehicle attitude information such as the tilt angle of vehicle 1300, as well as driving environment information such as obstacles in the environment in which vehicle 1300 is located.

[0156] In practice, the data collection frequency for vehicle posture information, driving environment information, or both can be adjusted depending on the road conditions.

[0157] In this embodiment, the third road scenario and the fourth road scenario can be distinguished based on the road complexity in the driving road condition information. The road complexity of the fourth road scenario is greater than that of the third road scenario. The fourth road scenario is a complex road surface with many obstacles and large slope changes.

[0158] In some embodiments, road complexity is determined based on obstacle information and slope information in the driving condition information.

[0159] In actual implementation, thresholds for parameters such as the number of obstacles and the rate of change of slope can be preset. When the obstacle information and slope information in the driving road condition information exceed the corresponding thresholds, it can be determined that vehicle 1300 is driving in the fourth road scenario.

[0160] Based on the road condition information, when it is determined that vehicle 1300 is traveling in the third road scenario, the data sampling frequency corresponding to the first state information of vehicle 1300 is set to the seventh sampling frequency.

[0161] Based on the driving road condition information, it is determined that when vehicle 1300 switches from the third road scenario to the fourth road scenario, there are more obstacles and the slope changes more significantly. Therefore, the data sampling frequency of vehicle 1300's body posture information and driving environment information can be increased. The data sampling frequency corresponding to the first state information of vehicle 1300 is set as the eighth sampling frequency, increasing the data proportion of important perception signals to cope with complex road changes, accurately control the action of seat belt 1320, and improve the safety of vehicle 1300.

[0162] The following is a specific example.

[0163] For the fourth road scenario with many bumps and slopes, the collection frequency of sensors such as radar and tilt angle on vehicle 1300 can be increased. That is, the data sampling frequency of vehicle 1300's body posture information and driving environment information can be increased to judge the road bumps and slopes. When a danger is found by analyzing the vehicle 1300's operating status information, the seat belt 1320 locking signal is triggered to control the seat belt 1320 to lock, thereby improving the safety of vehicle 1300.

[0164] It should be noted that the road scenario in which vehicle 1300 is driving can also be determined based on the driving mode information of vehicle 1300. For example, when the driving mode of vehicle 1300 is off-road mode, it can also be determined that vehicle 1300 is driving in a complex road scenario, which can increase the data sampling frequency of vehicle posture information and driving environment information.

[0165] This disclosure also provides a method for controlling a seatbelt 1320.

[0166] The control method for the seat belt 1320 can be applied to the vehicle 1300, and can be executed by the hardware or software in the vehicle 1300.

[0167] It should be noted that the control method of the seat belt 1320 uses the operating status information of the vehicle 1300 to control the action of the seat belt 1320. The operating status information of the vehicle 1300 can be obtained through the above-mentioned vehicle 1300 data processing method.

[0168] As shown in Figure 2, the control method of the seat belt 1320 includes steps 210, 220 and 230.

[0169] Step 210: Obtain vehicle driving information for vehicle 1300.

[0170] Among them, vehicle driving information is information that represents the current driving status of vehicle 1300, which can be obtained through the perception layer of vehicle 1300.

[0171] In this embodiment, the vehicle driving information includes at least one of driving mode information and road condition information. The driving mode information of vehicle 1300 can be the driving mode currently being driven by vehicle 1300, and the road condition information of vehicle 1300 can be the type of road condition currently being driven by vehicle 1300.

[0172] In actual use, the driving modes of the vehicle 1300 include, but are not limited to, Sport mode, Eco mode, Standard mode, Off-road mode, Low-speed Off-road mode, and Snow mode. Each driving mode has unique characteristics and applicable scenarios, and the driver of the vehicle 1300 can choose different driving modes according to actual needs and road conditions.

[0173] The road conditions that vehicle 1300 travels on can be classified according to specific road types, including but not limited to highways, mountain roads, water-crossing roads, bridges and tunnels, or according to road quality. The road conditions that vehicle 1300 travels on are diverse and have a significant impact on its driving.

[0174] It is understandable that different vehicles 1300 have different driving mode selections, and the vehicle 1300 can switch to different driving modes when driving on different road surfaces.

[0175] In this step, you can obtain the driving mode information of vehicle 1300, the road condition information of vehicle 1300, and both the driving mode information and the road condition information of vehicle 1300.

[0176] In actual operation, vehicle driving information such as driving mode information and road condition information of vehicle 1300 can be obtained from multiple terminals in the perception layer of vehicle 1300.

[0177] Step 220: Based on the vehicle driving information, determine the data sampling frequency of the vehicle 1300's operating status information, and collect the operating status information according to the data sampling frequency.

[0178] Among them, the operating status information of vehicle 1300 can be information that can characterize the overall vehicle status during the operation of vehicle 1300, such as vehicle speed, vehicle posture, vehicle gear, surrounding environment, etc. The operating status information of vehicle 1300 can be collected through the perception layer of vehicle 1300.

[0179] In this step, based on the vehicle driving information of vehicle 1300, the data sampling frequency corresponding to the operating status information of vehicle 1300 is analyzed, and then the corresponding operating status information is collected according to the data sampling frequency to analyze and obtain the current vehicle status.

[0180] In actual implementation, the data sampling frequency corresponding to the operating status information of vehicle 1300 can be determined by analyzing the driving mode information of vehicle 1300, or by analyzing the road condition information of vehicle 1300, or by combining the driving mode information and road condition information of vehicle 1300.

[0181] It is understandable that there may be multiple types of operating status information for vehicle 1300. When determining the data sampling frequency based on the vehicle driving information of vehicle 1300, one type of operating status information data sampling frequency can be determined, or multiple types of operating status information can be determined, and operating status information can be collected according to their respective data sampling frequencies.

[0182] For example, the operating status information of vehicle 1300 includes signal A, signal B and signal C. When the driving mode of vehicle 1300 is switched to off-road mode, the data sampling frequency of signal A can be increased, while the data sampling frequency of signal B and signal C can be decreased. This reduces the amount of data in the operating status information while increasing the proportion of important data in the operating status information. The operating status information obtained by sampling according to the data sampling frequency can more accurately reflect the status of the whole vehicle.

[0183] It should be noted that the data sampling frequency of the operating status information is determined based on the vehicle driving information of vehicle 1300. When the vehicle driving information changes, the data sampling frequency of the operating status information can be adjusted accordingly.

[0184] Step 230: Based on the operating status information, control the seat belt 1320 of vehicle 1300 to move.

[0185] In this step, the operating status information of vehicle 1300 is obtained by sampling according to the data sampling frequency, the operating status information of vehicle 1300 is analyzed to determine the overall vehicle status, and the seat belt 1320 of vehicle 1300 is controlled to perform the corresponding action.

[0186] For example, vehicle 1300 samples the operating status information of vehicle 1300 through the perception layer according to the data sampling frequency. When the radar, external camera and other sensors detect people or vehicles around vehicle 1300 and there is a risk of collision, the control layer ECU can assess the risk level as high risk and trigger the locking action to control the seat belt 1320 of vehicle 1300 to lock, so as to ensure the safety of the people in vehicle 1300. At the same time, the judgment result is sent as a message and recorded.

[0187] In related technologies, the control system not only needs to sense and analyze numerous signals, but also needs to output corresponding control states to external devices, and also needs to coordinate the actions of various systems in the vehicle 1300. The control system has limited processing power and cannot quickly and accurately control the action of the seat belt 1320, resulting in low safety and affecting the seat belt 1320 wearing experience of the occupants.

[0188] In this embodiment, the data sampling frequency corresponding to the operating status information of vehicle 1300 is obtained by analyzing the vehicle driving information of vehicle 1300. Under different driving modes and road conditions, the operating status information of vehicle 1300 is sampled and analyzed according to different data sampling frequencies. This can reduce the amount of data in the operating status information, while also increasing the proportion of important data in the operating status information. This allows for accurate judgment of the overall vehicle status, rapid and accurate control of the seat belt 1320, improved safety, and enhanced seat belt wearing experience for occupants.

[0189] According to the control method of seat belt 1320 provided in this disclosure embodiment, the data sampling frequency corresponding to the operating status information of vehicle 1300 is determined by the vehicle driving information of vehicle 1300. The operating status information of vehicle 1300 is sampled according to different data sampling frequencies, the data is processed in a timely and effective manner, the overall vehicle status is accurately judged, and the action of seat belt 1320 can be controlled quickly and accurately, thereby improving safety and enhancing the wearing experience.

[0190] In some embodiments, after obtaining the vehicle driving information of vehicle 1300, the method for controlling seat belt 1320 may further include:

[0191] Based on vehicle driving information, determine the control response speed of vehicle 1300;

[0192] Based on the operating status information, control the action of the seat belt 1320 in vehicle 1300, including:

[0193] Based on the operating status information, the seat belt 1320 is controlled to move according to the control response speed.

[0194] In this embodiment, based on the vehicle driving information of vehicle 1300, the data sampling frequency corresponding to the operating status information of vehicle 1300 can be determined, and the control response speed corresponding to the control action can also be determined. Subsequently, the seat belt 1320 is controlled according to the control response speed, which helps to control the seat belt 1320 quickly and accurately.

[0195] Among them, control response speed refers to how quickly the vehicle's 1300 system, such as the seat belt 1320, responds to user operations or external commands issued by the control layer.

[0196] In actual execution, the response speed can be defined as the time interval between the issuance of the action command and the actual execution of the corresponding action by the vehicle system. The control layer of vehicle 1300 can adjust the specific value of the response speed.

[0197] It should be noted that the data sampling frequency of the operating status information and the control response speed of the vehicle 1300 are determined based on the vehicle driving information of the vehicle 1300. When the vehicle driving information changes, the data sampling frequency of the operating status information and the control response speed of the vehicle 1300 can be adjusted accordingly.

[0198] For example, based on road condition information, it can be determined that vehicle 1300 is traveling on a paved highway. Since there are fewer inclined road surfaces with a slope exceeding 27 degrees on paved highways, the sampling frequency of data corresponding to vehicle tilt angle and other body posture information can be reduced, while the sampling frequency of data corresponding to speed and acceleration information can be increased. At the same time, the control response speed of the seat belt 1320 system can be improved. When a danger is detected by analyzing the operating status information of vehicle 1300, the seat belt 1320 locking signal can be triggered to quickly control the seat belt 1320 to lock.

[0199] For example, in the case of the fourth road scenario with many bumps and slopes, the acquisition frequency of sensors such as radar and tilt angle on vehicle 1300 can be increased to improve the data sampling frequency of vehicle 1300's body posture information and driving environment information, so as to judge the road bumps and slopes. At the same time, the control response speed of seat belt 1320 system can be improved. When a danger is detected by analyzing the operating status information of vehicle 1300, the seat belt 1320 locking signal is triggered to quickly control the seat belt 1320 to lock.

[0200] Understandably, the seat belt 1320 can be controlled to perform different actions. It can be controlled to lock the seat belt 1320, stop the webbing from retracting and extending, and restrict the movement of personnel. It can also be controlled to not lock the seat belt 1320, so that the webbing can retract and extend and the movement of personnel can be unrestricted. It can also be controlled to suppress passive locking components such as the mechanical vehicle sensing mechanism 1321 in the seat belt 1320 system to reduce noise.

[0201] In some embodiments, controlling the movement of the seatbelt 1320 of vehicle 1300 based on operating status information includes:

[0202] If the operating status information meets the triggering condition for the first action, control the seat belt 1320 to execute the first action and obtain the operating status information;

[0203] If the operating status information meets the triggering conditions for the second action, control the seat belt 1320 to switch from executing the first action to executing the second action.

[0204] In this embodiment, the operating status information of the vehicle 1300 is acquired. When the operating status information meets the first action triggering condition, the seat belt 1320 is controlled to perform the first action. During this process, when the operating status information meets the second action triggering condition, the seat belt 1320 is controlled to perform the second action. Based on the real-time operating status information, the seat belt 1320 is controlled to perform different actions.

[0205] The second action has a higher priority than the first action.

[0206] In actual implementation, the priority of the actions performed by the seat belt 1320 can be divided according to safety performance requirements. The locking action of the seat belt 1320 can protect the safety of the personnel, and the priority of the locking action of the seat belt 1320 can be greater than the priority of the free retraction action of the seat belt 1320.

[0207] For example, the second action is a locking action, and the first action is a free release action.

[0208] The following is a specific example.

[0209] As shown in Figure 4, after the vehicle system is powered on, the control layer can start the initialization program. The initialization program is the matching and calibration process of the seat belt 1320 system. By setting the calibration theoretical origin, information is compared to confirm the deviation between the current state and the calibration theoretical origin, deviation compensation is performed, internal parameters are self-adjusted, and after the initial definition calibration is completed, the corresponding status record is output, and the seat belt 1320 system enters the standby state.

[0210] After the seat belt 1320 system is activated by the trigger signal, it monitors the demand signal in real time, compares the threshold of the operating status information with the control layer, calculates and judges, compares the real-time status of the seat belt 1320, and controls the seat belt 1320 to perform the corresponding action.

[0211] As shown in Figure 5, the ECU is first activated and performs initialization definition. After initialization is completed, the seat belt 1320 status confirmation is immediately performed, and the status of each sensing terminal on the vehicle 1300 is confirmed. If the status does not meet the set status (some sensors are malfunctioning and cannot sense signals), the status information will be uploaded to the background. At the same time, the system operation record is compared with the most recent one, and the corresponding information code is generated and incorporated into the system's data stream processing.

[0212] When the set conditions are met, the vehicle completes the standby confirmation and enters the standby state. In the standby state, the ECU will receive the corresponding operating status information in real time and perform trigger condition judgment. If the trigger condition is not met, it will continuously capture and compare. If the trigger condition is met, the corresponding function will be executed, the execution status will be output, and the trigger trend of high priority functions will be detected.

[0213] When the corresponding function A is executed, the seat belt 1320 is controlled to execute the action corresponding to function A, output the execution status, and detect the triggering trend of high-priority functions. When the corresponding high-priority function B is executed, the seat belt 1320 is controlled to execute the action corresponding to high-priority function B, upload the execution action record, output the status parameters, monitor the function status in real time, maintain the current status, or trigger a new round of information capture and comparison by the ECU.

[0214] In some embodiments, the seat belt 1320 includes a mechanical vehicle sensing mechanism 1321 and an electronic vehicle sensing mechanism 1322, which control the movement of the seat belt 1320 of the vehicle 1300 based on operating status information, including:

[0215] When the operating status information meets the locking trigger conditions, the electronic vehicle sensing mechanism 1322 that controls the seat belt 1320 executes the locking action;

[0216] Alternatively, if the operating status information does not meet the locking trigger conditions, the mechanical sensing mechanism 1321 of the seat belt 1320 may be inhibited from performing its action.

[0217] Among them, the mechanical vehicle sensing mechanism 1321 is a passive vehicle sensing mechanism. When the speed of the vehicle 1300 exceeds a certain threshold or when emergency braking occurs, the mechanical structure in the mechanical vehicle sensing mechanism 1321 will automatically lock the seat belt 1320 without the need for control of the vehicle 1300 control layer. The electronic vehicle sensing mechanism 1322 is an active vehicle sensing mechanism. It is controlled by the vehicle 1300 control layer and performs corresponding actions.

[0218] In this embodiment, when the operating status information meets the locking trigger condition, the electronic vehicle sensing mechanism 1322 of the seat belt 1320 is controlled to perform a locking action, actively locking the seat belt 1320. This prevents the mechanical vehicle sensing mechanism 1321 from failing to lock properly, effectively improving the safety performance of the vehicle 1300.

[0219] In related technologies, mechanical vehicle sensors mostly consist of mechanical structures such as vehicle sensor steel balls. When a vehicle is driving normally at 1300 km / h, the vibration of these mechanical structures can cause abnormal noises, affecting the comfort of passengers.

[0220] In this embodiment, if the operating status information does not meet the locking trigger condition, the mechanical vehicle sensing mechanism 1321 of the seat belt 1320 is suppressed from performing its action, which can reduce noise, ensure safety, and improve the comfort of the passengers.

[0221] In this embodiment, when the mechanical vehicle sensing mechanism 1321 that inhibits the seat belt 1320 performs its action, the electronic vehicle sensing mechanism 1322 can be in a state of performing a locking action or in a state of not locking.

[0222] The following is a specific example.

[0223] As shown in Figure 6, the seat belt 1320 wearing information is detected. After the seat belt 1320 is worn, the vehicle 1300 driving mode and road condition information are detected in real time.

[0224] According to the driving mode information, the vehicle 1300 is in low-speed off-road mode, where the speed is limited to no more than 40km / h to provide stronger off-road performance and traction.

[0225] Under this driving speed limit, there are fewer situations where danger is caused by speed. When sensing signals, the frequency of speed-related signals can be reduced and the frequency of position-related signals (vehicle posture information and driving environment information) can be increased. It can be determined whether the driving status information exceeds the set threshold, and the electronic vehicle sensing mechanism 1322 can be controlled to perform a locking action, or the mechanical vehicle sensing mechanism 1321 of the seat belt 1320 can be inhibited from performing an action.

[0226] Based on the road condition information, it is determined that vehicle 1300 is traveling on a paved highway. Since there are few inclined road surfaces with a slope of more than 27 degrees on paved highways, the frequency of speed signal acquisition is increased and the frequency of position signal acquisition is decreased. It is determined whether the driving status information exceeds the set threshold, and the electronic vehicle sensing mechanism 1322 is controlled to perform a locking action, or the mechanical vehicle sensing mechanism 1321 of the seat belt 1320 is inhibited from performing an action.

[0227] For example, as shown in Figure 7, when the first relay 607 is energized, the valve core 606 is attracted, and the valve core 606 retracts downward, causing the rocker arm 605 to rotate counterclockwise around the fulcrum. This causes the motion-following claw 610 of the mechanical vehicle sensing mechanism 1321 to rotate downward, thereby suppressing the mechanical vehicle sensing mechanism 1321 from performing its action. The steel ball 609 no longer rotates with the movement of the vehicle 1300, which can reduce noise.

[0228] As shown in Figure 8, the mechanical vehicle sensing mechanism 1321 is suppressed from performing its action. At the same time, the second relay 611 is energized, the locking lever 612 pops out and engages with the ratchet teeth 604, and the electronic vehicle sensing mechanism 1322 performs the locking action, thereby restraining the movement of the people on the vehicle.

[0229] In actual implementation, when the mechanical sensing mechanism 1321 is not inhibited from performing its actions, the mechanical sensing mechanism 1321 is in a decoupled state.

[0230] As shown in Figure 9, when the first relay 607 is de-energized, the valve core 606 extends upward, causing it to rotate clockwise around the fulcrum and lift up. The mechanical vehicle sensing mechanism 1321 is activated, and by sensing the change in acceleration through the steel ball 609, it pushes the vehicle sensing follow-up pawl 610 to achieve locking.

[0231] When the seat belt 1320 system is powered off or malfunctions, the ECU controls the first relay 607 and the second relay 611 to be powered off simultaneously, and the mechanical vehicle sensing mechanism 1321 is activated. At this time, the seat belt 1320 can still be locked.

[0232] In this embodiment, by suppressing the mechanical vehicle sensing mechanism 1321 from performing its actions, the seat belt 1320 can be freely extended and retracted, which can meet the needs of zero-gravity seat operation and large-angle seat adjustment, while suppressing vehicle noise and improving user comfort. The electronic vehicle sensing mechanism 1322 can achieve active locking to ensure the safety of the people in the vehicle.

[0233] The vehicle 1300 data processing method provided in this embodiment can be executed by a vehicle data processing device 1000. This embodiment uses the vehicle data processing device 1000 executing the vehicle 1300 data processing method as an example to illustrate the vehicle data processing device 1000 provided in this embodiment.

[0234] This disclosure also provides a vehicle data processing device 1000.

[0235] As shown in Figure 10, the vehicle data processing device 1000 includes:

[0236] The first acquisition module 1010 is used to acquire the vehicle driving information of vehicle 1300;

[0237] The first processing module 1020 is used to determine the data sampling frequency of the vehicle 1300's operating status information based on the vehicle's driving information, and to collect the operating status information according to the data sampling frequency.

[0238] According to the vehicle data processing device 1000 provided in this embodiment, the data sampling frequency corresponding to the operating status information of the vehicle 1300 is determined by the vehicle driving information of the vehicle 1300. By sampling the operating status information of the vehicle 1300 at different data sampling frequencies, data can be processed in a timely and effective manner, the overall vehicle status can be accurately judged, and the real-time performance and response speed of the vehicle system can be guaranteed. In some embodiments, the operating status information includes at least one of driving speed information, vehicle gear information, vehicle posture information, and driving environment information.

[0239] In some embodiments, vehicle driving information includes driving mode information, and operating status information includes driving speed information. The first processing module 1020 is configured to determine the data sampling frequency of the operating status information of the vehicle 1300 based on the vehicle driving information, including:

[0240] Based on driving mode information, the maximum driving speed of the vehicle is determined to be 1300.

[0241] Based on the vehicle's maximum speed of 1300, the data sampling frequency for speed information is determined.

[0242] In some embodiments, the first processing module 1020 is configured to determine the data sampling frequency of driving speed information based on the upper limit of the driving speed of the vehicle 1300, including:

[0243] If it is determined that the upper limit of the driving speed of vehicle 1300 is less than the first speed threshold, the data sampling frequency corresponding to the driving speed information of vehicle 1300 is determined as the first sampling frequency.

[0244] Alternatively, if the upper limit of the vehicle's speed is determined to be greater than or equal to the first speed threshold, the data sampling frequency corresponding to the speed information is determined to be the second sampling frequency, and the first sampling frequency is less than the second sampling frequency.

[0245] In some embodiments, vehicle driving information includes road condition information, and operating status information includes at least one of driving speed information, vehicle posture information, and driving environment information. The first processing module 1020 is configured to determine the data sampling frequency of the operating status information of the vehicle 1300 based on the vehicle driving information, including:

[0246] Based on the driving road condition information, the road scenario in which vehicle 1300 is traveling is determined;

[0247] Based on the road scenario of vehicle 1300 driving, determine the data sampling frequency of at least one of the driving speed information, vehicle posture information and driving environment information.

[0248] In some embodiments, the first processing module 1020 is configured to determine the data sampling frequency of at least one of driving speed information, vehicle posture information, and driving environment information based on the road scene in which the vehicle 1300 is driving, including:

[0249] Given that vehicle 1300 is traveling on the first road scenario, the data sampling frequency corresponding to the vehicle 1300's driving speed information is determined to be the third sampling frequency, and the data sampling frequency corresponding to the vehicle 1300's body posture information is determined to be the fifth sampling frequency.

[0250] Alternatively, if it is determined that vehicle 1300 is traveling on the second road scenario, the data sampling frequency corresponding to the driving speed information is determined to be the fourth sampling frequency, and the data sampling frequency corresponding to the vehicle posture information is determined to be the sixth sampling frequency.

[0251] Among them, the third sampling frequency is greater than the fourth sampling frequency, the fifth sampling frequency is less than the sixth sampling frequency, and the average slope of the first road scene is less than the average slope of the second road scene.

[0252] In some embodiments, the first processing module 1020 is configured to determine the data sampling frequency of at least one of driving speed information, vehicle posture information, and driving environment information based on the road scene in which the vehicle 1300 is driving, including:

[0253] When it is determined that vehicle 1300 is driving in a third road scenario, the data sampling frequency corresponding to the first state information of vehicle 1300 is determined to be the seventh sampling frequency. The first state information includes at least one of vehicle body posture information and driving environment information of vehicle 1300.

[0254] Alternatively, if it is determined that vehicle 1300 is traveling in the fourth road scenario, the data sampling frequency corresponding to the first state information is determined to be the eighth sampling frequency.

[0255] Among them, the eighth sampling frequency is greater than the seventh sampling frequency, and the road complexity of the fourth road scenario is greater than that of the third road scenario.

[0256] In some embodiments, road complexity is determined based on obstacle information and slope information in the driving condition information.

[0257] The vehicle data processing device 1000 in this embodiment of the present disclosure may be an electronic device or a component in an electronic device, such as an integrated circuit or a chip.

[0258] The vehicle data processing device 1000 provided in this disclosure can implement the various processes implemented in the vehicle 1300 data processing method embodiment. To avoid repetition, these processes will not be described again here.

[0259] The control method for the seat belt 1320 provided in this embodiment can be executed by the control device 1100 of the seat belt 1320. This embodiment uses the control device 1100 of the seat belt 1320 executing the control method as an example to illustrate the control device 1100 of the seat belt 1320 provided in this embodiment.

[0260] This disclosure also provides a control device 1100 for a seat belt 1320.

[0261] As shown in Figure 11, the control device 1100 of the seat belt 1320 includes:

[0262] The second acquisition module 1110 is used to acquire the vehicle driving information of vehicle 1300;

[0263] The second processing module 1120 is used to determine the data sampling frequency of the vehicle 1300's operating status information based on the vehicle driving information, and to collect the operating status information according to the data sampling frequency.

[0264] The control module 1130 is used to control the operation of the seat belt 1320 of the vehicle 1300 based on the operating status information.

[0265] According to the control device 1100 of the seat belt 1320 provided in this embodiment, the data sampling frequency corresponding to the operating status information of the vehicle 1300 is determined by the vehicle driving information of the vehicle 1300. The operating status information of the vehicle 1300 is sampled according to different data sampling frequencies, the data is processed in a timely and effective manner, the overall vehicle status is accurately judged, and the action of the seat belt 1320 can be controlled quickly and accurately to improve safety and enhance the wearing experience.

[0266] In some embodiments, after acquiring the vehicle driving information of vehicle 1300, the second processing module 1120 is further configured to determine the control response speed of vehicle 1300 based on the vehicle driving information.

[0267] Control module 1130 is used to control the action of seat belt 1320 of vehicle 1300 based on operating status information, including:

[0268] Based on the operating status information, the seat belt 1320 is controlled to move according to the control response speed.

[0269] In some embodiments, the control module 1130 is configured to control the movement of the seatbelt 1320 of the vehicle 1300 based on operating status information, including:

[0270] If the operating status information meets the triggering condition for the first action, control the seat belt 1320 to execute the first action and obtain the operating status information;

[0271] If the operating status information meets the triggering conditions for the second action, the control seat belt 1320 switches from executing the first action to executing the second action, and the priority of the second action is greater than the priority of the first action.

[0272] In some embodiments, the seat belt 1320 includes a mechanical vehicle sensing mechanism 1321 and an electronic vehicle sensing mechanism 1322. A control module 1130 is used to control the movement of the seat belt 1320 of the vehicle 1300 based on operating status information, including:

[0273] When the operating status information meets the locking trigger conditions, the electronic vehicle sensing mechanism 1322 that controls the seat belt 1320 executes the locking action;

[0274] Alternatively, if the operating status information does not meet the locking trigger conditions, the mechanical sensing mechanism 1321 of the seat belt 1320 may be inhibited from performing its action.

[0275] The control device 1100 of the seat belt 1320 in this embodiment of the present disclosure may be an electronic device or a component in an electronic device, such as an integrated circuit or a chip.

[0276] The control device 1100 for the seat belt 1320 provided in this embodiment can realize the various processes implemented in the control method embodiment for the seat belt 1320. To avoid repetition, it will not be described again here.

[0277] This disclosure also provides a vehicle 1300.

[0278] As shown in Figure 13, the vehicle 1300 includes: a sensing system 1310 and a vehicle data processing device 1000 as described above. The sensing system 1310 is used to collect the operating status information of the vehicle 1300; the vehicle data processing device 1000 is connected to the sensing system 1310.

[0279] The vehicle data processing device 1000 can determine the data sampling frequency of the vehicle 1300's operating status information based on the vehicle's driving information, and control the sensing system 1310 to collect the operating status information according to the data sampling frequency.

[0280] In actual implementation, the sensing system 1310 can be the perception layer of the vehicle 1300, collecting numerous signals from the vehicle's domain controller, ESP, ADAS radar, vehicle attitude, and other sensing terminals. The control unit can be the ECU, which analyzes the numerous signals from the perception layer to determine the overall vehicle status and provide the corresponding actions to be executed in the execution layer.

[0281] According to the vehicle 1300 provided in this embodiment, the data sampling frequency corresponding to the operating status information of the vehicle 1300 is determined by the vehicle driving information of the vehicle 1300. By sampling the operating status information of the vehicle 1300 at different data sampling frequencies, data can be processed in a timely and effective manner, the overall vehicle status can be accurately judged, and the real-time performance and response speed of the vehicle system can be guaranteed. This embodiment also provides a vehicle 1300.

[0282] As shown in Figure 14, the vehicle 1300 includes: a sensing system 1310, a seat belt 1320, and a control device 1100 for the seat belt 1320 as described above. The sensing system 1310 is used to collect the operating status information of the vehicle 1300. The control device 1100 is connected to the sensing system 1310 and the seat belt 1320.

[0283] The control device 1100 can acquire the vehicle driving information of the vehicle 1300, determine the data sampling frequency of the vehicle 1300's operating status information based on the vehicle driving information, control the sensor system 1310 to collect the operating status information according to the data sampling frequency, and control the seat belt 1320 of the vehicle 1300 to move based on the operating status information.

[0284] In actual implementation, the sensing system 1310 can be the perception layer of the vehicle 1300, collecting numerous signals from the vehicle's domain controller, ESP, ADAS radar, vehicle attitude and other sensing terminals. The control device 1100 can be the ECU, which analyzes the numerous signals from the perception layer to determine the overall vehicle status and gives the corresponding actions that the seat belt 1320 in the execution layer needs to perform.

[0285] According to the vehicle 1300 provided in this embodiment, the data sampling frequency corresponding to the operating status information of the vehicle 1300 is determined by the vehicle driving information of the vehicle 1300, and the operating status information of the vehicle 1300 is sampled according to different data sampling frequencies.

[0286] It can process data in a timely and effective manner, accurately judge the status of the vehicle, and quickly and accurately control the seat belt movement, thereby improving safety and enhancing the wearing experience.

[0287] In some embodiments, as shown in FIG15, the seat belt 1320 includes a mechanical vehicle sensing mechanism 1321 and an electronic vehicle sensing mechanism 1322.

[0288] Among them, the mechanical vehicle sensing mechanism 1321 is a passive vehicle sensing mechanism. When the speed of the vehicle 1300 exceeds a certain threshold or when emergency braking occurs, the mechanical structure in the mechanical vehicle sensing mechanism 1321 will automatically lock the seat belt 1320 without the need for control of the vehicle 1300 control layer. The electronic vehicle sensing mechanism 1322 is an active vehicle sensing mechanism. It is controlled by the vehicle 1300 control layer and performs corresponding actions.

[0289] In this embodiment, when the operating status information meets the locking trigger condition, the control device 1100 can control the electronic vehicle sensing mechanism 1322 of the seat belt 1320 to perform a locking action, actively locking the seat belt 1320, preventing the mechanical vehicle sensing mechanism 1321 from failing to lock properly, and effectively improving the safety performance of the vehicle 1300.

[0290] In this embodiment, if the operating status information does not meet the locking trigger condition, the control device 1100 can suppress the mechanical vehicle sensing mechanism 1321 of the seat belt 1320 from performing its actions, which can reduce noise, ensure safety, and improve the comfort of the passengers.

[0291] In this embodiment, when the mechanical vehicle sensing mechanism 1321 that inhibits the seat belt 1320 performs its action, the electronic vehicle sensing mechanism 1322 can be in a state of performing a locking action or in a state of not locking.

[0292] The following is a specific example.

[0293] For example, as shown in Figure 7, when the first relay 607 is energized, the valve core 606 is attracted, and the valve core 606 retracts downward, causing the rocker arm 605 to rotate counterclockwise around the fulcrum. This causes the motion-following claw 610 of the mechanical vehicle sensing mechanism 1321 to rotate downward, thereby suppressing the mechanical vehicle sensing mechanism 1321 from performing its action. The steel ball 609 no longer rotates with the movement of the vehicle 1300, which can reduce noise.

[0294] As shown in Figure 8, the mechanical vehicle sensing mechanism 1321 is suppressed from performing its action. At the same time, the second relay 611 is energized, the locking lever 612 pops out and engages with the ratchet teeth 604, and the electronic vehicle sensing mechanism 1322 performs the locking action, thereby restraining the movement of the people on the vehicle.

[0295] In actual implementation, when the mechanical sensing mechanism 1321 is not inhibited from performing its actions, the mechanical sensing mechanism 1321 is in a decoupled state.

[0296] As shown in Figure 9, when the first relay 607 is de-energized, the valve core 606 extends upward, causing it to rotate clockwise around the fulcrum and lift up. The mechanical vehicle sensing mechanism 1321 is activated, and by sensing the change in acceleration through the steel ball 609, it pushes the vehicle sensing follow-up pawl 610 to achieve locking.

[0297] When the seat belt 1320 system is powered off or malfunctions, the ECU controls the first relay 607 and the second relay 611 to be powered off simultaneously, and the mechanical vehicle sensing mechanism 1321 is activated. At this time, the seat belt 1320 can still be locked.

[0298] In this embodiment, by suppressing the mechanical vehicle sensing mechanism 1321 from performing its actions, the seat belt 1320 can be freely extended and retracted, which can meet the needs of zero-gravity seat operation and large-angle seat adjustment, while suppressing vehicle noise and improving user comfort. The electronic vehicle sensing mechanism 1322 can achieve active locking to ensure the safety of the people in the vehicle.

[0299] In some embodiments, as shown in FIG12, this disclosure also provides an electronic device 1200, including a processor 1201, a memory 1202, and a computer program stored in the memory 1202 and executable on the processor 1201. When the program is executed by the processor 1201, it implements the various processes of the above-described vehicle 1300 data processing method or seat belt 1320 control method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0300] It should be noted that the electronic devices in this disclosure include the mobile electronic devices and non-mobile electronic devices described above.

[0301] This disclosure also provides a non-transitory computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described vehicle 1300 data processing method or seat belt 1320 control method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0302] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0303] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described vehicle 1300 data processing method or seat belt 1320 control method.

[0304] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0305] This disclosure also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described vehicle 1300 data processing method or seat belt 1320 control method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0306] It should be understood that the chip mentioned in the embodiments of this disclosure may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0307] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this disclosure is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0308] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0309] The embodiments of this disclosure have been described above with reference to the accompanying drawings. However, this disclosure is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this disclosure without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this disclosure.

[0310] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0311] Although embodiments of this disclosure have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this disclosure, the scope of which is defined by the claims and their equivalents.

Claims

1. A vehicle data processing method, wherein, include: Obtain vehicle driving information; Based on the vehicle driving information, the data sampling frequency of the vehicle's operating status information is determined, and the operating status information is collected according to the data sampling frequency.

2. The vehicle data processing method according to claim 1, wherein, The operating status information includes at least one of the following: driving speed information, vehicle gear information, vehicle posture information, and driving environment information.

3. The vehicle data processing method according to claim 1 or 2, wherein, The vehicle driving information includes driving mode information, and the operating status information includes driving speed information. The step of determining the data sampling frequency of the vehicle's operating status information based on the vehicle driving information includes: Based on the driving mode information, the maximum driving speed of the vehicle is determined; The data sampling frequency of the driving speed information is determined based on the vehicle's maximum driving speed.

4. The vehicle data processing method according to claim 3, wherein, The step of determining the data sampling frequency of the driving speed information based on the driving speed limit includes: If it is determined that the upper limit of the vehicle's driving speed is less than a first speed threshold, the data sampling frequency corresponding to the driving speed information is determined to be the first sampling frequency; Alternatively, if it is determined that the upper limit of the vehicle's driving speed is greater than or equal to the first speed threshold, the data sampling frequency corresponding to the driving speed information is determined to be the second sampling frequency, where the first sampling frequency is less than the second sampling frequency.

5. The vehicle data processing method according to any one of claims 1-4, wherein, The vehicle driving information includes road condition information, and the operating status information includes at least one of driving speed information, vehicle posture information, and driving environment information. The step of determining the data sampling frequency of the vehicle's operating status information based on the vehicle driving information includes: Based on the driving road condition information, the road scenario in which the vehicle is traveling is determined; Based on the road scenario in which the vehicle is traveling, the data sampling frequency of at least one of the driving speed information, the vehicle posture information, and the driving environment information is determined.

6. The vehicle data processing method according to claim 5, wherein, The step of determining the data sampling frequency of at least one of the driving speed information, the vehicle posture information, and the driving environment information based on the road scene in which the vehicle is driving includes: When it is determined that the vehicle is traveling in a first road scenario, the data sampling frequency corresponding to the driving speed information is determined to be the third sampling frequency, and the data sampling frequency corresponding to the vehicle posture information is determined to be the fifth sampling frequency; Alternatively, if it is determined that the vehicle is traveling in a second road scenario, the data sampling frequency corresponding to the driving speed information is determined to be the fourth sampling frequency, and the data sampling frequency corresponding to the vehicle posture information is determined to be the sixth sampling frequency. The third sampling frequency is greater than the fourth sampling frequency, the fifth sampling frequency is less than the sixth sampling frequency, and the average slope of the first road scene is less than the average slope of the second road scene.

7. The vehicle data processing method according to claim 5, wherein, The step of determining the data sampling frequency of at least one of the driving speed information, the vehicle posture information, and the driving environment information based on the road scene in which the vehicle is driving includes: When it is determined that the vehicle is traveling in a third road scenario, the data sampling frequency corresponding to the first state information of the vehicle is determined to be the seventh sampling frequency, and the first state information includes at least one of the vehicle posture information and the driving environment information. Alternatively, if it is determined that the vehicle is traveling in a fourth road scenario, the data sampling frequency corresponding to the first state information is determined to be the eighth sampling frequency; The eighth sampling frequency is greater than the seventh sampling frequency, and the road complexity of the fourth road scenario is greater than that of the third road scenario.

8. The vehicle data processing method according to claim 7, wherein, The road complexity is determined based on the obstacle information and slope information in the driving road condition information.

9. A method for controlling a seatbelt, wherein, include: Obtain vehicle driving information; Based on the vehicle driving information, the data sampling frequency of the vehicle's operating status information is determined, and the operating status information is collected according to the data sampling frequency; Based on the operating status information, the seat belts of the vehicle are controlled to operate.

10. The method for controlling a seatbelt according to claim 9, wherein, After obtaining the vehicle's driving information, the method further includes: Based on the vehicle driving information, the control response speed of the vehicle is determined; The step of controlling the seatbelt operation of the vehicle based on the operating status information includes: Based on the operating status information, the seat belt is controlled to move according to the control response speed.

11. The method for controlling a seatbelt according to claim 9 or 10, wherein, The step of controlling the seatbelt operation of the vehicle based on the operating status information includes: If the operating status information meets the first action triggering condition, the seat belt is controlled to perform the first action, and the operating status information is acquired. If the operating status information meets the triggering condition for the second action, the seat belt is controlled to switch from performing the first action to performing the second action, whereby the priority of the second action is greater than that of the first action.

12. The method for controlling a seatbelt according to any one of claims 9-11, wherein, The seat belt includes a mechanical vehicle sensing mechanism and an electronic vehicle sensing mechanism. Controlling the seat belt movement based on the operating status information includes: When the operating status information meets the locking trigger condition, the electronic vehicle sensing mechanism of the seat belt is controlled to perform a locking action; Alternatively, if the operating status information does not meet the locking trigger condition, the mechanical sensing mechanism of the seat belt may be inhibited from performing its action.

13. A vehicle data processing device, wherein, include: The first acquisition module is used to acquire vehicle driving information; The first processing module is used to determine the data sampling frequency of the vehicle's operating status information based on the vehicle's driving information, and to collect the operating status information according to the data sampling frequency.

14. A seat belt control device, wherein, include: The second acquisition module is used to acquire vehicle driving information; The second processing module is used to determine the data sampling frequency of the vehicle's operating status information based on the vehicle driving information, and to collect the operating status information according to the data sampling frequency; The control module is used to control the seat belt operation of the vehicle based on the operating status information.

15. A vehicle, wherein, include: The sensing system is used to collect the vehicle's operating status information. ； The vehicle data processing device as described in claim 13, wherein the vehicle data processing device is connected to the sensing system.

16. A vehicle, wherein, include: The sensing system and seat belts, wherein the sensing system is used to collect the vehicle's operating status information. ； The control device for the seat belt as claimed in claim 14, wherein the control device is connected to the sensing system and the seat belt.

17. The vehicle according to claim 16, wherein, The seat belt includes a mechanical vehicle sensing mechanism and an electronic vehicle sensing mechanism.

18. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the program, it implements the vehicle data processing method as described in any one of claims 1-8 or the seat belt control method as described in any one of claims 9-12.

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