Vehicle armrest control method and device, vehicle and storage medium

Abstract

The invention provides a vehicle armrest control method and device, a vehicle and a storage medium, and relates to the technical field of vehicles. The method comprises the steps that driving information of the vehicle in the current time period is obtained, and the driving information comprises road condition information, position information and driving state information; the driving information is input into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period of time, the driving road condition of the vehicle in the next period of time is output, the driving road condition comprises an abnormal driving road condition and a normal driving road condition, and according to the driving road condition of the vehicle in the next period of time and a driving road condition corresponding control strategy, the driving information of the vehicle in the next period of time is obtained. And opening and closing of the vehicle armrest are controlled. By acquiring the driving information of the vehicle in the current time period in real time, predicting the driving road condition on the front expected route and controlling the vehicle armrest to open and close in advance, the vehicle armrest can be timely and effectively adjusted, so that a user can easily hold the armrest when needed, and the riding safety, comfort and user experience are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a vehicle armrest control method, device, vehicle and storage medium. Background Art

[0002] With the continuous advancement of smart car technology, smart cars have not only made significant progress in driving assistance and autonomous driving, but the interaction between the in-car cabin components and users has increased significantly. Against this background, the traditional in-car component design has gradually exposed its limitations, especially the key component of the vehicle armrest.

[0003] The original intention of designing traditional vehicle armrests is to assist passengers in maintaining their body balance while the vehicle is moving. Since the vehicle armrests are fixed to the ceiling structure, passengers need to actively reach out and grasp them when encountering complex driving conditions such as bumps or sharp turns. This passive response mechanism requires passengers to remain highly alert and respond quickly during driving. In actual driving, passengers often find it difficult to quickly grasp the vehicle armrests due to unstable center of gravity or untimely response, which reduces the safety of riding and further affects the user's riding experience. Summary of the invention

[0004] In view of this, the present invention aims to propose a vehicle armrest control method, device, vehicle and storage medium to solve the problem that traditional vehicle armrests require users to actively reach out and grasp them when passengers encounter complex driving conditions, resulting in the vehicle armrests being difficult to respond to user needs in a timely manner, affecting the user's riding experience.

[0005] According to a first aspect of the present invention, a vehicle armrest control method is provided, the method comprising:

[0006] Acquire the driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information;

[0007] Input the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period, and output the driving road condition of the vehicle in the next period; wherein the driving road condition includes abnormal driving road condition and normal driving road condition;

[0008] The opening and closing of the vehicle armrest is controlled according to the driving condition of the vehicle in the next period of time and the control strategy corresponding to the driving condition.

[0009] Optionally, the obtaining of driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information, includes:

[0010] In response to receiving the enable signal, collecting road condition information, location information and driving status information of the vehicle in the current period;

[0011] Cleaning the road condition information, location information and driving status information of the vehicle to obtain cleaned multi-source information;

[0012] The cleaned multi-source information is synchronized in time series and fused to obtain the vehicle's driving information for the current period.

[0013] Optionally, the step of inputting the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period of time, and outputting the driving road condition of the vehicle in the next period of time; wherein the driving road condition includes abnormal driving road condition and normal driving road condition, including:

[0014] Calling a pre-trained road condition prediction model; wherein the road condition prediction model is obtained by training through historical driving road conditions;

[0015] The driving information is input into the road condition prediction model, and the driving condition of the vehicle in the next time period is output; the road condition prediction model is used to identify the driving condition of the current time period based on the driving information, and predict the driving condition of the next time period based on the driving condition of the current time period.

[0016] Optionally, before calling the pre-trained traffic condition prediction model, the method further includes:

[0017] Obtain the vehicle's driving information during historical periods;

[0018] Use deep learning neural network to identify driving information in historical periods and obtain historical driving conditions;

[0019] Iterative training of road condition prediction is performed based on historical driving conditions to obtain a trained road condition prediction model.

[0020] Optionally, controlling the opening and closing of the vehicle armrest according to the driving condition of the vehicle in the next period of time and the control strategy corresponding to the driving condition includes:

[0021] Pre-generate the control strategy for driving conditions and vehicle armrest opening and closing;

[0022] In response to determining that the driving condition of the vehicle in the next period is an abnormal driving condition, controlling the vehicle armrest to open according to the control strategy;

[0023] In response to determining that the driving condition of the vehicle in the next period is a normal driving condition, the vehicle armrest is controlled to close according to the control strategy.

[0024] Optionally, in response to determining that the driving condition of the vehicle in the next period is an abnormal driving condition, controlling the vehicle armrest to open according to the control strategy includes:

[0025] In response to determining that the driving road condition of the vehicle in the next period is an abnormal driving road condition, determining at least one target armrest of the vehicle to be opened;

[0026] At least one target armrest of the vehicle is controlled to open according to the control strategy.

[0027] According to a second aspect of the present invention, a vehicle armrest control device is provided, the device comprising:

[0028] An information acquisition module is used to acquire the driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information;

[0029] A road condition prediction module, used to input the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period of time, and output the driving road condition of the vehicle in the next period of time; wherein the driving road condition includes abnormal driving road condition and normal driving road condition;

[0030] The armrest control module is used to control the opening and closing of the vehicle armrest according to the driving conditions of the vehicle in the next period of time and the control strategy corresponding to the driving conditions.

[0031] Optionally, the information acquisition module includes:

[0032] A collection submodule, for collecting road condition information, location information and driving status information of the vehicle in the current period in response to receiving an enable signal;

[0033] A cleaning submodule is used to clean the road condition information, location information and driving status information of the vehicle to obtain cleaned multi-source information;

[0034] The fusion submodule is used to perform time synchronization and data fusion on the cleaned multi-source information to obtain the vehicle's driving information for the current period.

[0035] Optionally, the road condition prediction module includes:

[0036] A calling submodule, used to call a pre-trained road condition prediction model; wherein the road condition prediction model is obtained by training through historical driving road conditions;

[0037] The prediction submodule is used to input the driving information into the road condition prediction model and output the driving condition of the vehicle in the next time period; the road condition prediction model is used to identify the driving condition of the current time period based on the driving information, and predict the driving condition of the next time period based on the driving condition of the current time period.

[0038] Optionally, the road condition prediction module further includes:

[0039] The acquisition submodule is used to obtain the driving information of the vehicle in the historical period;

[0040] The recognition submodule is used to recognize the driving information of the historical period using a deep learning neural network to obtain the historical driving road conditions;

[0041] The training submodule is used to perform iterative training of road condition prediction based on historical driving conditions to obtain a trained road condition prediction model.

[0042] Optionally, the control armrest module includes:

[0043] A generation submodule, used to pre-generate a control strategy for driving conditions and the opening and closing of vehicle armrests;

[0044] A first control submodule, configured to control the vehicle armrest to open according to the control strategy in response to determining that the driving road condition of the vehicle in the next period is an abnormal driving road condition;

[0045] The second control submodule is used to control the closing of the vehicle armrest according to the control strategy in response to determining that the driving condition of the vehicle in the next period of time is a normal driving condition.

[0046] Optionally, the first control submodule includes:

[0047] a determination unit, configured to determine at least one target armrest of the vehicle to be opened in response to determining that the driving road condition of the vehicle in the next period is an abnormal driving road condition;

[0048] A control unit is used to control the opening of at least one target armrest of the vehicle according to the control strategy.

[0049] According to another aspect of the present invention, there is also provided a vehicle, the vehicle comprising:

[0050] processor;

[0051] a memory for storing instructions executable by the processor;

[0052] Wherein, the processor is configured to execute the instructions to implement the vehicle armrest control method as described above.

[0053] According to another aspect of the present invention, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the vehicle armrest control method as described above are implemented.

[0054] The vehicle armrest control method provided by the embodiment of the present invention obtains the driving information of the vehicle in the current period, the driving information includes road condition information, location information and driving state information, inputs the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period, outputs the driving road condition of the vehicle in the next period, the driving road condition includes abnormal driving road condition and normal driving road condition, and controls the opening and closing of the vehicle armrest according to the driving road condition of the vehicle in the next period and the corresponding control strategy of the driving road condition. The embodiment of the present invention obtains the driving information of the vehicle in the current period in real time, and uses the pre-trained road condition prediction model to predict the driving road condition on the expected route ahead, controls the vehicle armrest to open and close in advance, and can timely and effectively adjust the state of the vehicle armrest according to the road condition ahead, so that the user can easily hold the armrest when needed, which greatly improves the safety, comfort and user experience of riding.

[0055] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0057] Figure 1 is a flow chart of steps of a vehicle armrest control method provided by an embodiment of the present invention;

[0058] Figure 2 yes Figure 1 A flowchart of step 101 in the vehicle armrest control method provided by an embodiment of the present invention;

[0059] Figure 3 yes Figure 1 A flowchart of step 102 in the vehicle armrest control method provided by an embodiment of the present invention;

[0060] Figure 4 yes Figure 1 A flowchart of step 103 in the vehicle armrest control method provided by an embodiment of the present invention;

[0061] Figure 5 is a schematic diagram of a scenario of a vehicle armrest control method provided by an embodiment of the present invention;

[0062] Figure 6It is a structural schematic diagram of a vehicle armrest control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] To make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can be implemented. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined and referenced with each other without contradiction.

[0064] Reference Figure 1 , shows a flow chart of the steps of a vehicle armrest control method provided by an embodiment of the present invention, the method may include:

[0065] Step 101, obtaining the driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information.

[0066] In an embodiment of the present invention, in order to solve the problem that the traditional vehicle armrest requires the user to actively reach out and grasp it when the passenger encounters complex driving conditions, which makes it difficult for the vehicle armrest to respond to the user's needs in time and affects the user's riding experience, in this embodiment, the vehicle armrest control system communicates with the vehicle system, the perception system and the positioning system of the intelligent networked vehicle to obtain the road ahead information and obstacle information identified by the perception system, the vehicle positioning information determined by the positioning system and the vehicle driving information determined by the vehicle system. The vehicle armrest control system predicts the driving road conditions on the expected driving route ahead based on the perception positioning information and the vehicle driving state, and sends control instructions to the actuator of the vehicle armrest in advance, so that the actuator responds to the control instruction to make the vehicle armrest perform actions such as opening, maintaining and closing in advance, and can timely and effectively adjust the state of the vehicle armrest according to the road conditions ahead, so that the user can easily hold the armrest when needed.

[0067] Specifically, the vehicle armrest control system obtains the driving information of the vehicle in the current time period, wherein the driving information includes road condition information, position information and driving status information. The road condition information, position information and driving status information of the vehicle in the current time period are specifically collected in real time through the perception system on the road ahead, the road condition information includes road information and obstacle information, the vehicle driving status information is identified by the vehicle computer, the driving status information includes real-time data of the vehicle, such as speed, direction, acceleration, etc., and the map information and positioning information are identified by the positioning system. In this embodiment, the length of the current time period of the vehicle is set according to the specific prediction needs, which can be 3 minutes, 5 minutes, etc. In order to further improve the road condition prediction accuracy and response timeliness, the duration granularity of the current time period and the next time period can be minimized to the greatest extent, which is not specifically limited here.

[0068] Step 102, input the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period of time, and output the driving road condition of the vehicle in the next period of time; wherein the driving road condition includes abnormal driving road condition and normal driving road condition.

[0069] In an embodiment of the present invention, the vehicle armrest control system inputs the processed driving information of the vehicle in the current time period into a pre-trained road condition prediction model, where the driving information includes road condition information, location information and driving status information. The road condition prediction model performs real-time reasoning based on the input driving information, identifies the driving road condition in the current time period, and predicts the driving road condition in the next time period. The driving road condition includes abnormal driving road condition and normal driving road condition. The abnormal driving road condition may be the driving road condition on a moving surface, such as bumps, steep slopes, and sharp turns. The normal driving road condition may be the driving road condition on a comfortable surface, such as a flat road, smooth driving, etc.

[0070] Step 103, controlling the opening and closing of the vehicle armrest according to the driving condition of the vehicle in the next period of time and the control strategy corresponding to the driving condition.

[0071] In an embodiment of the present invention, the driving condition of the vehicle in the next time period, that is, the predicted driving condition on the expected driving route ahead, and the control strategy corresponding to the driving condition are output according to the pre-trained road condition prediction model to control the opening and closing of the vehicle armrest. Specifically, the vehicle armrest control system sends a control instruction to the vehicle armrest actuator in advance, so that the actuator responds to the control instruction and allows the vehicle armrest to perform actions such as opening, maintaining and closing in advance. The state of the vehicle armrest can be timely and effectively adjusted according to the road condition ahead, so that the user can easily hold the armrest when needed.

[0072] The vehicle armrest control method provided by the embodiment of the present invention obtains the driving information of the vehicle in the current period, the driving information includes road condition information, location information and driving state information, inputs the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period, outputs the driving road condition of the vehicle in the next period, the driving road condition includes abnormal driving road condition and normal driving road condition, and controls the opening and closing of the vehicle armrest according to the driving road condition of the vehicle in the next period and the corresponding control strategy of the driving road condition. The embodiment of the present invention obtains the driving information of the vehicle in the current period in real time, and uses the pre-trained road condition prediction model to predict the driving road condition on the expected route ahead, controls the vehicle armrest to open and close in advance, and can timely and effectively adjust the state of the vehicle armrest according to the road condition ahead, so that the user can easily hold the armrest when needed, which greatly improves the safety, comfort and user experience of riding.

[0073] In order to facilitate those skilled in the art to fully understand the above vehicle armrest control method, refer to Figure 5 , Figure 5 It is a scenario schematic diagram of a vehicle armrest control method provided by an embodiment of the present invention, which shows an application scenario of the vehicle armrest control method provided by an embodiment of the present invention, specifically including: the vehicle armrest control system communicates with the vehicle system, perception system and positioning system of the intelligent networked vehicle, and the specific implementation process is as follows: the front road information, obstacle information, map positioning information, and vehicle positioning information recognized by the perception system are provided to the vehicle armrest control system. The vehicle armrest control system predicts the driving road conditions on the expected driving route ahead based on the perception positioning information and the driving status of the vehicle, and sends control instructions to the actuator of the vehicle armrest in advance. The actuator can respond to the control instructions to make the vehicle armrest perform actions such as opening, maintaining and closing.

[0074] In the embodiment of the present invention, the perception system identifies the road ahead and obstacle information, and the perception system includes at least one camera sensor; the positioning system can provide map information and vehicle positioning information, and the positioning system includes at least a GNSS module or a GPS module; the vehicle system can detect the vehicle driving status, including but not limited to the heading angle, speed, acceleration, etc. The vehicle armrest control system and the vehicle system, perception system and positioning system of the intelligent networked car transmit and communicate information, and the communication means include but are not limited to wired transmission and wireless transmission, wherein wired transmission includes but is not limited to CAN, LIN, Ethernet, etc., and wireless transmission includes but is not limited to WiFi, Bluetooth, Lora, 4G, 5G, etc. The vehicle armrest control system can control the roof armrest actuator through instructions, and the roof armrest actuator can realize the opening, maintaining and closing states of the roof armrest by controlling the roof armrest actuator. The roof armrest actuator is connected to the roof armrest through a mechanical mechanism, and the roof armrest actuator can drive the roof armrest to move. The actuator includes but is not limited to a stepper motor, a permanent magnet motor, etc.

[0075] Further, see Figure 2 , showing Figure 1 A flowchart of step 101 in a vehicle armrest control method is provided. The method is substantially the same as the vehicle armrest control method provided in the first embodiment of the present invention. Step 101 may include:

[0076] Step 1011, in response to receiving the enable signal, collecting the road condition information, location information and driving status information of the vehicle in the current time period.

[0077] Step 1012, cleaning the road condition information, location information and driving status information of the vehicle to obtain cleaned multi-source information.

[0078] Step 1013, performing time-series synchronization and data fusion on the cleaned multi-source information to obtain the vehicle's driving information for the current period.

[0079] It should be noted that in the embodiment of the present invention, when the vehicle armrest control system is started or receives an opening instruction (such as the driver manually opening the intelligent armrest function), it triggers an enabling signal to collect the road condition information, location information and driving status information of the vehicle in the current period. Specifically, the road condition information of the road ahead is collected in real time through a perception system, such as a vehicle-mounted camera, radar, laser radar and other sensors. The road condition information includes road information and obstacle information. The specific road information includes but is not limited to obtaining the geometric features of the road (such as curves, slopes), road conditions, lane lines, traffic signs, traffic light information, etc., and the obstacle information includes but is not limited to other vehicles, pedestrians, road engineering, etc. The vehicle driving status information is identified by the vehicle computer, and the driving status information includes but is not limited to the real-time data of the vehicle, such as speed, direction, acceleration, etc.; the map information and positioning information are identified by the positioning system, and the map information is not limited to the use of high-precision maps, including lane lines, traffic signs, traffic light information, etc., and the positioning information includes but is not limited to the precise location information of the vehicle. The precise location information can be obtained through a GNSS module or a GPS module to determine the real-time location information of the vehicle, and specifically may include latitude and longitude, altitude, etc., which is not specifically limited in the embodiment of the present invention.

[0080] In this embodiment, the system cleans the road condition information, position information and driving status information of the vehicle to obtain cleaned multi-source information, and can remove noise and outliers in the collected data. For example, through filtering algorithms (such as Kalman filtering and mean filtering), missing data is interpolated or filled to ensure the integrity of the data. The data collected by different functional modules are standardized to obtain cleaned multi-source information. The multi-source information includes the cleaned road condition information, position information and driving status information. The collected and processed multi-source data are aligned according to timestamps to ensure the consistency of the data in the time dimension. Specifically, a time synchronization algorithm (such as NTP and PTP) can be used to ensure the consistency of the data timestamps. The cleaned multi-source information is fused using a data fusion algorithm (such as Kalman filtering and particle filtering) to obtain comprehensive driving information. The driving information includes road condition information, position information and driving status information.

[0081] The embodiment of the present invention removes noise and outliers through data cleaning, ensures the consistency of each data in the time dimension through alignment and time synchronization, avoids errors caused by time asynchrony, and fuses multi-source information through a data fusion algorithm to obtain comprehensive driving information, thereby improving the accuracy and reliability of vehicle driving data collected in real time, and further improving the prediction accuracy of the road condition prediction model.

[0082] Further, see Figure 3 , showing Figure 1 A flowchart of step 102 in a vehicle armrest control method is provided. The method is substantially the same as the vehicle armrest control method provided in the first embodiment of the present invention. Step 102 may include:

[0083] Step 1021, calling a pre-trained road condition prediction model; wherein the road condition prediction model is obtained by training based on historical driving road conditions.

[0084] Step 1022, input the driving information into the road condition prediction model, and output the driving road condition of the vehicle in the next time period; the road condition prediction model is used to identify the driving road condition of the current time period based on the driving information, and predict the driving road condition of the next time period based on the driving road condition of the current time period.

[0085] In an embodiment of the present invention, a trained road condition prediction model is deployed in a vehicle armrest control system of an intelligent connected vehicle. When the armrest control is started, the control module calls the model by calling an API or directly loading a model file, and inputs the processed driving information of the vehicle in the current period into the road condition prediction model. The driving information includes road condition information, location information, and driving status information. The road condition prediction model performs real-time reasoning based on the input driving information, identifies the driving road condition in the current period, and predicts the driving road condition in the next period. The road condition prediction model outputs the driving road condition in the next period. The driving road condition includes abnormal driving road conditions (such as bumps, steep slopes, sharp turns, etc.) and normal driving road conditions (such as flat roads, smooth driving, etc.), wherein the abnormal driving road condition may be a driving road condition on a moving surface, such as bumps, steep slopes, sharp turns, and the normal driving road condition may be a driving road condition on a comfortable surface, such as flat roads, smooth driving, etc.

[0086] It should be noted that the pre-trained road condition prediction model is obtained through training on historical driving conditions. The road condition prediction model can identify a variety of driving conditions, and can also include urban roads, highways, rural roads, etc., and provide personalized handrail control strategies. By using historical driving data to train the road condition prediction model, the model can learn the characteristics and rules of different driving conditions, thereby improving the prediction accuracy. Specifically, a deep learning model suitable for time series prediction can be selected, which can capture the time series dependencies of driving information and further improve the prediction accuracy. I will not go into details here.

[0087] The road condition prediction model in the embodiment of the present invention can perform real-time reasoning based on the driving information of the current time period, and quickly output the driving road conditions of the next time period, ensuring that the system can respond in time and adjust the handrail status. By calling the pre-trained road condition prediction model, the system can automatically predict the road condition, and the system can dynamically adjust the handrail status according to the actual driving conditions, thereby improving the flexibility and adaptability of the system.

[0088] Specifically, before calling the pre-trained traffic condition prediction model in step 1021, the following steps are also included:

[0089] First, obtain the driving information of the vehicle in the historical period;

[0090] Secondly, a deep learning neural network is used to identify the driving information of historical periods to obtain historical driving conditions;

[0091] Secondly, iterative training of road condition prediction is performed based on historical driving conditions to obtain a trained road condition prediction model.

[0092] It should be noted that, in an embodiment of the present invention, a historical driving data set is used for model training, and the data set includes historical road condition information, location information and driving status information. Specifically, in this embodiment, a deep learning model suitable for time series prediction can be selected, and the historical driving data can be input into the model through a supervised learning method. The model is trained to recognize the driving conditions of the current time period and predict the driving conditions of the next time period.

[0093] Specifically, the vehicle's driving information in historical time periods is collected through on-board sensors (such as cameras, radars, lidars, etc.) and positioning systems (such as GNSS, GPS), and the vehicle's driving information in historical time periods is obtained. The historical driving information includes historical road condition information, location information and driving status information. The driving information in historical time periods is identified using a deep learning neural network. The collected historical driving information can be cleaned, standardized and time-series synchronized in advance to ensure data quality and consistency. A deep learning model suitable for time series prediction is selected to identify the driving information in historical time periods and obtain historical driving road conditions. Specifically, the driving road conditions in historical time periods are identified based on the historical driving information. The historical driving road conditions include abnormal driving road conditions (such as bumps, steep slopes, sharp turns, etc.) and normal driving road conditions (such as flat roads, smooth driving, etc.). The identified historical driving road conditions are used as a training data set. Based on the training data set of the historical driving road conditions, a supervised learning method is used to input the historical driving road conditions into the model, and multiple iterative training is performed to optimize the prediction ability of the model. In some embodiments, the prediction performance of the model can be evaluated through cross-validation, loss function evaluation and other methods to ensure the accuracy and robustness of the model under different driving conditions. After multiple iterations of training and evaluation, a trained road condition prediction model is obtained and deployed in intelligent connected vehicles.

[0094] The embodiments of the present invention automatically obtain historical driving information, identify historical driving road conditions and perform iterative training. The system can automatically generate a road condition prediction model and continuously optimize its prediction capability, thereby improving the robustness and stability of the model under different driving conditions. The road condition prediction model is trained and makes decisions based on historical driving information, so that the system can adjust the handrail state according to data-driven results, thereby improving the accuracy of decision-making.

[0095] Further, see Figure 4 , showing Figure 1 A flowchart of step 103 in a vehicle armrest control method is provided. The method is substantially the same as the vehicle armrest control method provided in the first embodiment of the present invention. Step 103 may include:

[0096] Step 1031, pre-generate a control strategy for driving conditions and opening and closing of vehicle armrests.

[0097] Step 1032, in response to determining that the driving condition of the vehicle in the next period is an abnormal driving condition, controlling the vehicle armrest to open according to the control strategy.

[0098] Step 1033, in response to determining that the driving condition of the vehicle in the next period is a normal driving condition, controlling the vehicle armrest to close according to the control strategy.

[0099] In an embodiment of the present invention, a control strategy for driving conditions and opening and closing of a vehicle armrest is generated in advance. According to different driving conditions on comfortable surfaces and sports surfaces, such as bumps, steep slopes, sharp turns, flat roads, etc., a corresponding vehicle armrest opening and closing strategy is formulated. The generated control strategy is stored in the vehicle computer system or the vehicle armrest control system of the intelligent connected vehicle so that it can be quickly called when needed.

[0100] Specifically, the control strategies may include the following two types: abnormal driving conditions: when abnormal driving conditions such as bumps, steep slopes, and sharp turns are predicted, the control strategy instructs the vehicle armrests to open so that passengers can easily hold them when needed, thereby improving the stability and safety of the ride; normal driving conditions: when normal driving conditions such as flat roads and smooth driving are predicted, the control strategy instructs the vehicle armrests to close or maintain the current state to reduce unnecessary interference and improve riding comfort.

[0101] In this embodiment, the vehicle armrest control system controls the vehicle armrest to open according to the control strategy in response to determining that the vehicle's driving road condition in the next period is an abnormal driving road condition, and controls the vehicle armrest to close according to the control strategy in response to determining that the vehicle's driving road condition in the next period is a normal driving road condition. According to the driving road condition in the next period output by the road condition prediction model, it is determined whether it is an abnormal driving road condition. According to the identified abnormal driving road condition, a pre-generated control strategy is called to instruct the vehicle armrest to open, and the opening action of the vehicle armrest is achieved by controlling the vehicle armrest actuator (such as a stepper motor, a permanent magnet motor, etc.), and the control instruction can be sent to the actuator of the vehicle armrest through wired transmission (such as CAN, LIN, Ethernet, etc.) or wireless transmission (such as WiFi, Bluetooth, Lora, 4G, 5G, etc.). According to the driving road condition in the next period output by the road condition prediction model, it is determined whether it is a normal driving road condition. According to the identified normal driving road condition, a pre-generated control strategy is called to instruct the vehicle armrest to close or maintain the current state.

[0102] Specifically, in response to determining that the driving road condition of the vehicle in the next period of time is an abnormal driving road condition, controlling the vehicle armrest to open according to the control strategy includes: in response to determining that the driving road condition of the vehicle in the next period of time is an abnormal driving road condition, determining at least one target armrest of the vehicle to be opened, and controlling at least one target armrest of the vehicle to open according to the control strategy. Specifically, the function opening and closing of each smart armrest on the roof can be accurately controlled to achieve accurate delivery of the function, thereby accurately and effectively improving the user experience.

[0103] The embodiment of the present invention pre-generates a control strategy for driving conditions and the opening and closing of vehicle armrests. The system can automatically adjust the state of the armrests, reducing manual intervention. The vehicle armrests are automatically opened in abnormal driving conditions, and passengers can hold them easily, reducing body imbalance caused by vehicle shaking or sharp turns and improving riding safety. The vehicle armrests are automatically closed or maintained in normal driving conditions, reducing unnecessary interference and improving riding comfort. The system can respond to changes in road conditions in real time and quickly adjust the state of the armrests to ensure that the best riding experience is provided under different driving conditions.

[0104] Reference Figure 6 , shows a schematic structural diagram of a vehicle armrest control device provided by an embodiment of the present invention, the device comprising:

[0105] The information acquisition module 201 is used to acquire the driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information;

[0106] The road condition prediction module 202 is used to input the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period of time, and output the driving road condition of the vehicle in the next period of time; wherein the driving road condition includes abnormal driving road condition and normal driving road condition;

[0107] The armrest control module 203 is used to control the opening and closing of the vehicle armrest according to the driving conditions of the vehicle in the next period of time and the control strategy corresponding to the driving conditions.

[0108] Furthermore, the information acquisition module 201 includes:

[0109] A collection submodule, for collecting road condition information, location information and driving status information of the vehicle in the current period in response to receiving an enable signal;

[0110] A cleaning submodule is used to clean the road condition information, location information and driving status information of the vehicle to obtain cleaned multi-source information;

[0111] The fusion submodule is used to perform time synchronization and data fusion on the cleaned multi-source information to obtain the vehicle's driving information for the current period.

[0112] Furthermore, the road condition prediction module 202 includes:

[0113] A calling submodule, used to call a pre-trained road condition prediction model; wherein the road condition prediction model is obtained by training through historical driving road conditions;

[0114] The prediction submodule is used to input the driving information into the road condition prediction model and output the driving condition of the vehicle in the next time period; the road condition prediction model is used to identify the driving condition of the current time period based on the driving information, and predict the driving condition of the next time period based on the driving condition of the current time period.

[0115] Furthermore, the road condition prediction module 202 further includes:

[0116] The acquisition submodule is used to obtain the driving information of the vehicle in the historical period;

[0117] The recognition submodule is used to recognize the driving information of the historical period using a deep learning neural network to obtain the historical driving road conditions;

[0118] The training submodule is used to perform iterative training of road condition prediction based on historical driving conditions to obtain a trained road condition prediction model.

[0119] Furthermore, the control armrest module 203 includes:

[0120] A generation submodule, used to pre-generate a control strategy for driving conditions and the opening and closing of vehicle armrests;

[0121] A first control submodule, configured to control the vehicle armrest to open according to the control strategy in response to determining that the driving road condition of the vehicle in the next period is an abnormal driving road condition;

[0122] The second control submodule is used to control the closing of the vehicle armrest according to the control strategy in response to determining that the driving condition of the vehicle in the next period of time is a normal driving condition.

[0123] Furthermore, the first control submodule includes:

[0124] a determination unit, configured to determine at least one target armrest of the vehicle to be opened in response to determining that the driving road condition of the vehicle in the next period is an abnormal driving road condition;

[0125] A control unit is used to control the opening of at least one target armrest of the vehicle according to the control strategy.

[0126] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0127] The vehicle armrest control device provided by the embodiment of the present invention obtains the driving information of the vehicle in the current period, the driving information includes road condition information, location information and driving state information, inputs the driving information into the pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period, outputs the driving road condition of the vehicle in the next period, the driving road condition includes abnormal driving road condition and normal driving road condition, and controls the opening and closing of the vehicle armrest according to the driving road condition of the vehicle in the next period and the control strategy corresponding to the driving road condition. The embodiment of the present invention obtains the driving information of the vehicle in the current period in real time, and uses the pre-trained road condition prediction model to predict the driving road condition on the expected route ahead, controls the vehicle armrest to open and close in advance, and can timely and effectively adjust the state of the vehicle armrest according to the road condition ahead, so that the user can easily hold the armrest when needed, which greatly improves the safety, comfort and user experience of riding.

[0128] An embodiment of the present invention further provides a vehicle, comprising a processor;

[0129] a memory for storing processor-executable instructions;

[0130] Wherein, the processor is configured to execute the instructions to implement the vehicle armrest control method as described above.

[0131] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0132] The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0133] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the vehicle armrest control method as described above is implemented.

[0134] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0135] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0136] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A vehicle armrest control method, characterized in that: The method comprises: Acquire the driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information; Input the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period, and output the driving road condition of the vehicle in the next period; wherein the driving road condition includes abnormal driving road condition and normal driving road condition; The opening and closing of the vehicle armrest is controlled according to the driving condition of the vehicle in the next period of time and the control strategy corresponding to the driving condition.

2. The method according to claim 1, characterized in that The obtaining of the driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information, includes: In response to receiving the enable signal, collecting road condition information, location information and driving status information of the vehicle in the current period; Cleaning the road condition information, location information and driving status information of the vehicle to obtain cleaned multi-source information; The cleaned multi-source information is synchronized in time series and fused to obtain the vehicle's driving information for the current period.

3. The method according to claim 1, characterized in that The driving information is input into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period of time, and the driving road condition of the vehicle in the next period of time is output; wherein the driving road condition includes abnormal driving road condition and normal driving road condition, including: Calling a pre-trained road condition prediction model; wherein the road condition prediction model is obtained by training through historical driving road conditions; The driving information is input into the road condition prediction model, and the driving condition of the vehicle in the next time period is output; the road condition prediction model is used to identify the driving condition of the current time period based on the driving information, and predict the driving condition of the next time period based on the driving condition of the current time period.

4. The method according to claim 3, characterized in that Before calling the pre-trained traffic condition prediction model, the method further includes: Obtain the vehicle's driving information during historical periods; Use deep learning neural network to identify driving information in historical periods and obtain historical driving conditions; Iterative training of road condition prediction is performed based on historical driving conditions to obtain a trained road condition prediction model.

5. The method according to claim 1, characterized in that The method of controlling the opening and closing of the vehicle armrest according to the driving condition of the vehicle in the next period of time and the control strategy corresponding to the driving condition includes: Pre-generate the control strategy for driving conditions and vehicle armrest opening and closing; In response to determining that the driving condition of the vehicle in the next period is an abnormal driving condition, controlling the vehicle armrest to open according to the control strategy; In response to determining that the driving condition of the vehicle in the next period is a normal driving condition, the vehicle armrest is controlled to close according to the control strategy.

6. The method according to claim 5, characterized in that In response to determining that the driving condition of the vehicle in the next period is an abnormal driving condition, controlling the vehicle armrest to open according to the control strategy includes: In response to determining that the driving road condition of the vehicle in the next period is an abnormal driving road condition, determining at least one target armrest of the vehicle to be opened; At least one target armrest of the vehicle is controlled to open according to the control strategy.

7. A vehicle armrest control device, characterized in that: The device comprises: An information acquisition module is used to acquire the driving information of the vehicle in the current period, wherein the driving information includes road condition information, location information and driving status information; A road condition prediction module, used to input the driving information into a pre-trained road condition prediction model to predict the driving road condition of the vehicle in the next period of time, and output the driving road condition of the vehicle in the next period of time; wherein the driving road condition includes abnormal driving road condition and normal driving road condition; The armrest control module is used to control the opening and closing of the vehicle armrest according to the driving conditions of the vehicle in the next period of time and the control strategy corresponding to the driving conditions.

8. The device according to claim 7, characterized in that The information acquisition module includes: A collection submodule, for collecting road condition information, location information and driving status information of the vehicle in the current period in response to receiving an enable signal; A cleaning submodule is used to clean the road condition information, location information and driving status information of the vehicle to obtain cleaned multi-source information; The fusion submodule is used to perform time synchronization and data fusion on the cleaned multi-source information to obtain the vehicle's driving information for the current period.

9. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the instructions to implement the vehicle armrest control method as claimed in any one of claims 1 to 6.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle armrest control method according to any one of claims 1 to 6 is implemented.

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