A steering wheel control method and device based on SBW system

Abstract

The present application relates to the technical field of computers, and discloses a steering wheel control method based on an SBW system, including: obtaining corresponding real-time angle data through an angle sensor installed on the steering wheel, detecting the driver's corresponding steering intention based on the real-time angle data, and converting the steering intention into a steering electrical signal, and transmitting the steering electrical signal to an electronic control unit through a high-speed communication bus; obtaining the corresponding vehicle speed data and corresponding environmental data under the current state of the vehicle, and the electronic control unit determines the corresponding target steering torque based on the real-time angle data, vehicle speed data and environmental data; generating corresponding control instructions based on the target steering torque, driving the motor installed on the steering mechanism to generate the corresponding steering torque according to the control instructions, and collecting the driver's corresponding real-time road surface feedback. The present application can improve the performance of the steering system.

Description

Technical Field

[0001] The present application relates to the technical field of computers, and in particular to a steering wheel control method and device based on a SBW system. Background Art

[0002] Traditional automobile steering systems mainly rely on mechanical or hydraulic power assistance, directly controlling the steering of the wheels through the physical rotation of the steering wheel. This system has some inherent disadvantages, such as slow response, high failure rate, and high maintenance cost. With the development of automobile technology, especially the advancement of electronic control technology, the SBW system came into being. The SBW system transmits steering instructions through electronic signals, eliminates mechanical connections, and achieves more precise and faster steering control.

[0003] However, although the existing SBW system has improved the steering performance to a certain extent, there are still some problems in practical applications, such as delayed steering response, unrealistic road feedback, insufficient system reliability and safety, etc. As can be seen from the above, how to improve the performance of the steering system still needs to be solved. Summary of the invention

[0004] In order to improve the performance of the steering system, the present application provides a steering wheel control method and device based on the SBW system.

[0005] In a first aspect, the present application provides a steering wheel control method based on an SBW system, which adopts the following technical solution:

[0006] A steering wheel control method based on an SBW system comprises: obtaining corresponding real-time steering angle data through a steering angle sensor installed on the steering wheel, detecting the corresponding steering intention of the driver based on the real-time steering angle data, converting the steering intention into a steering electrical signal, and transmitting the steering electrical signal to an electronic control unit through a high-speed communication bus, wherein the real-time steering angle data includes steering angle, steering angular velocity, steering angular acceleration and steering direction; obtaining corresponding vehicle speed data and corresponding environmental data under the current state of the vehicle, wherein the electronic control unit determines the corresponding target steering torque based on the real-time steering angle data, the vehicle speed data and the environmental data, wherein the environmental data includes road conditions, weather conditions, traffic conditions and navigation information; generating corresponding control torque based on the target steering torque control instructions, according to which the motor installed on the steering mechanism is driven to generate a corresponding steering torque, and the corresponding real-time road feedback of the driver is collected; in the process of determining the corresponding target steering torque, it also includes: the electronic control unit calls the corresponding intelligent prediction unit, and determines the current driving behavior of the driver based on the vehicle speed data and the environmental data, wherein the intelligent prediction unit is a multi-layer neural network model pre-trained and generated according to historical driving data; the current driving behavior and the real-time steering angle data are input into the intelligent prediction unit to generate a corresponding multi-level prediction result, and a plurality of corresponding next steering torques are generated based on the multi-level prediction results, wherein the multi-level prediction results include a plurality of next steering intentions sorted based on a time series, and the target steering torque includes a plurality of next steering torques.

[0007] By adopting the above technical solution, the real-time steering angle data is obtained through the steering angle sensor installed on the steering wheel, combined with the vehicle speed data and environmental data, and the intelligent prediction results are generated using the multi-layer neural network model to determine the target steering torque, and drive the motor to generate the corresponding steering torque, thereby achieving precise steering control. This method not only improves the response speed and accuracy of the steering system, but also optimizes the driving experience through intelligent prediction and real-time feedback, and improves the overall performance and safety of the system. Specifically, through real-time monitoring and intelligent prediction, the system can adjust the steering torque in advance, reduce the driver's operating burden, improve driving smoothness and comfort, and provide better controllability and safety under complex road conditions.

[0008] Optionally, the method also includes: real-time collection of real-time reaction data of the driver to different feedback intensities and different feedback frequencies of the feedback motor; retrieving corresponding historical feedback data, predicting the driver's reaction data to different feedbacks from the feedback motor based on the feedback data and the real-time reaction data, and determining a corresponding feedback mechanism, wherein the historical feedback data is the historical feedback intensities and historical feedback frequencies corresponding to the feedback motor; generating multi-level feedback according to the multi-level prediction results and the feedback mechanism, and dynamically adjusting the intensity and frequency of the feedback motor based on the multi-level feedback, wherein each level of prediction result corresponds to one level of feedback.

[0009] By adopting the above technical solution, the driver's response data to the different feedback strengths and frequencies of the feedback motor are collected in real time, and combined with historical feedback data, the intelligent prediction algorithm is used to determine the best feedback mechanism. Based on the multi-level prediction results, the strength and frequency of the feedback motor are dynamically adjusted to provide more personalized and accurate driving feedback. This not only improves the driver's perception of road conditions, but also optimizes the driving experience, improves the system's response speed and control accuracy, and thus enhances the overall driving safety and comfort.

[0010] Optionally, during the process of the driver driving the vehicle, the method also includes: determining a target driver, and obtaining corresponding real-time operation data based on the target driver, wherein the real-time operation data includes steering angle, steering angular velocity, steering angular acceleration, steering direction, and vehicle speed; retrieving a corresponding driving habit model based on the target driver, wherein the driving habit model is a multi-layer neural network model trained in advance; inputting the real-time operation data into the driving habit model to determine a generation rule for generating a target steering torque, wherein the generation rule includes adjusting the size of the steering assist, the speed of the steering response, and matching the driver's driving preferences.

[0011] By adopting the above technical solution, by determining the target driver and collecting his operation data in real time, combined with the pre-trained driving habit model, the rules for generating the target steering torque are dynamically adjusted. Specifically, this method can adjust the size of the steering assist and the speed of the steering response according to the driver's driving habits, thereby providing a steering experience that is more in line with the driver's preferences; it not only improves driving comfort and controllability, but also improves the intelligence level of the system, and improves the overall driving safety and satisfaction.

[0012] Optionally, during the driving process of the vehicle, the method also includes: retrieving environmental data corresponding to the vehicle and data on the current status of the vehicle, wherein the environmental data also includes the position and distance of obstacles in front of the vehicle; judging whether there is a potential collision risk based on the real-time environmental data and the current status data of the vehicle; if so, determining the corresponding optimal avoidance path and generating a corresponding emergency steering torque instruction; transmitting the information to a motor installed on the steering mechanism via a high-speed communication bus, and generating a corresponding target steering torque based on the motor according to the received emergency steering torque instruction.

[0013] By adopting the above technical solution, the position and distance of the obstacle ahead are monitored in real time by accessing the corresponding environmental data and current status data of the vehicle to determine whether there is a potential collision risk. If there is a risk, the system will determine the optimal avoidance path and generate the corresponding emergency steering torque command, which will be transmitted to the motor of the steering mechanism through the high-speed communication bus. The motor will generate the target steering torque according to the command, thereby achieving fast and accurate avoidance operations, significantly improving driving safety and reliability, and reducing potential collision accidents.

[0014] Optionally, if there is an adjacent vehicle to the vehicle, the method also includes: receiving adjacent vehicle status information of the adjacent vehicle in real time through the V2V communication module, and retrieving corresponding environmental data, the adjacent vehicle status information including position, speed, acceleration, driving direction, and steering intention; analyzing the adjacent vehicle status information and the environmental data to obtain a corresponding comprehensive analysis result; generating a corresponding potential danger situation based on the comprehensive analysis result, and adjusting the target steering torque in advance based on the potential danger situation.

[0015] By adopting the above technical solution and generating potential dangerous situations, the system adjusts the target steering torque in advance to optimize steering control and improve driving safety and predictability; it not only helps to avoid potential collision risks, but also enhances the driver's perception of the surrounding environment and improves the overall driving stability and reliability.

[0016] Optionally, the method also includes: acquiring monitoring data corresponding to the vehicle in real time, wherein the detection data includes the operating status corresponding to the vehicle and the health status of the vehicle steering system; judging whether there is abnormal operation or system failure during the vehicle driving process based on the monitoring data, wherein abnormal operation includes sudden large-angle steering and unreasonable steering speed changes, and system failure includes sensor failure, communication interruption and motor failure; when abnormal operation or system failure is detected, the electronic control unit immediately activates a safety protection mechanism, wherein the safety protection mechanism includes automatically limiting the steering angle and completely locking the steering mechanism.

[0017] By adopting the above technical solution, by judging whether there is abnormal operation or system failure during vehicle driving, once abnormal operation or system failure is detected, the electronic control unit immediately activates the safety protection mechanism, automatically limits the steering angle or completely locks the steering mechanism, which can effectively prevent accidents caused by abnormal operation or system failure, and significantly improve driving safety and system reliability.

[0018] In a second aspect, the present application provides a steering wheel control device based on the SBW system, which adopts the following technical solution:

[0019] A steering wheel control device based on the SBW system, comprising:

[0020] A steering intention detection module, which obtains corresponding real-time steering angle data through a steering angle sensor installed on the steering wheel, detects the driver's corresponding steering intention based on the real-time steering angle data, converts the steering intention into a steering electrical signal, and transmits the steering electrical signal to the electronic control unit through a high-speed communication bus, wherein the real-time steering angle data includes steering angle, steering angular velocity, steering angular acceleration and steering direction;

[0021] a target steering torque determination module, which obtains corresponding vehicle speed data and corresponding environmental data under the current state of the vehicle, and the electronic control unit is used to determine the corresponding target steering torque based on the real-time steering angle data, the vehicle speed data and the environmental data, wherein the environmental data includes road conditions, weather conditions, traffic conditions, and navigation information;

[0022] A control instruction generation module, which is used to generate corresponding control instructions based on the target steering torque, drives a motor installed on the steering mechanism to generate corresponding steering torque according to the control instruction, and collects corresponding real-time road surface feedback from the driver;

[0023] The process of determining the corresponding target steering torque also includes:

[0024] The electronic control unit calls a corresponding intelligent prediction unit to determine the current driving behavior of the driver based on the vehicle speed data and the environmental data, wherein the intelligent prediction unit is a multi-layer neural network model pre-trained and generated according to historical driving data;

[0025] The current driving behavior and the real-time steering angle data are input into the intelligent prediction unit to generate a corresponding multi-level prediction result, and multiple corresponding next steering torques are generated based on the multi-level prediction result, wherein the multi-level prediction result includes multiple next-step steering intentions sorted based on a time series, and the target steering torque includes multiple next steering torques.

[0026] In a third aspect, the present application provides a steering wheel control method based on the SBW system, which adopts the following technical solution:

[0027] A steering wheel control system based on an SBW system comprises a processor, wherein a program of any one of the above-mentioned steering wheel control methods based on an SBW system is running on the processor.

[0028] In a fourth aspect, the present application provides a storage medium, which adopts the following technical solution:

[0029] A storage medium stores a program of any one of the above-mentioned steering wheel control methods based on the SBW system.

[0030] In summary, the present application includes at least one of the following beneficial technical effects:

[0031] Real-time steering angle data is obtained through the steering angle sensor installed on the steering wheel. Combined with vehicle speed and environmental data, a multi-layer neural network model is used to generate intelligent prediction results to determine the target steering torque, thereby achieving precise steering control. This can improve the response speed and accuracy of the steering system, reduce the driver's operating burden, and improve driving smoothness and comfort.

[0032] Real-time environmental data and vehicle current status data are used to determine whether there is a potential collision risk. If so, the optimal avoidance path is determined and the corresponding emergency steering torque command is generated. The motor generates the corresponding target steering torque based on the received emergency steering torque command, which improves driving safety and predictability and effectively avoids potential collision risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of a steering wheel control method based on a SBW system according to an exemplary embodiment.

[0034] Figure 2 It is a structural block diagram of a steering wheel control method device based on a SBW system according to an exemplary embodiment. DETAILED DESCRIPTION

[0035] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0036] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0037] The present application embodiment discloses a steering wheel control method based on the SBW system, referring to Figure 1 ,include:

[0038] S100 obtains corresponding real-time steering angle data through a steering angle sensor installed on the steering wheel, detects the driver's corresponding steering intention based on the real-time steering angle data, converts the steering intention into a steering electrical signal, and transmits the steering electrical signal to the electronic control unit through a high-speed communication bus.

[0039] Among them, a high-precision angle sensor is installed on the steering wheel. The angle sensor monitors the rotation of the steering wheel in real time, including the steering angle, steering angular velocity, steering angular acceleration and steering direction. The angle sensor converts these data into electrical signals.

[0040] Then, the electronic control unit (ECU) receives real-time steering angle data from the steering angle sensor. The ECU analyzes the real-time steering angle data through a built-in algorithm and detects the driver's steering intention. For example, by judging whether the driver is making a sharp turn or a slow turn through changes in the steering angle and steering angular velocity, the ECU can accurately identify the driver's steering intention and ensure that the system can respond according to the driver's actual operation.

[0041] The ECU generates a corresponding steering signal based on the detected steering intention. The steering signal contains the driver's steering intention, such as steering angle, steering speed and other information. At the same time, the steering signal is transmitted to the electronic control unit (ECU) through a high-speed communication bus (such as CAN bus). Among them, CAN bus is an efficient and reliable communication protocol that can transmit a large amount of data in a short time.

[0042] S110, obtaining corresponding vehicle speed data and corresponding environmental data under the current state of the vehicle, and the electronic control unit determines the corresponding target steering torque based on the real-time steering angle data, the vehicle speed data and the environmental data.

[0043] Among them, the speed sensor on the vehicle (such as the wheel speed sensor) monitors the speed of the vehicle in real time. The speed sensor converts the measured speed data into an electrical signal. Similarly, the electrical signal is transmitted to the electronic control unit (ECU) through a high-speed communication bus (such as the CAN bus). As for environmental data, various sensors on the vehicle (such as cameras, radars, GPS, etc.) monitor the environment around the vehicle in real time. All these environmental data are transmitted to the electronic control unit (ECU) through a high-speed communication bus.

[0044] In addition, the data collected by the sensors include: road conditions: detecting the flatness and slipperiness of the road surface through cameras and sensors; weather conditions: detecting temperature, humidity, rainfall, etc. through meteorological sensors; traffic conditions: detecting vehicles, pedestrians, obstacles, etc. in front through radar and cameras; navigation information: obtaining the current location, route planning, turn prompts, etc. through GPS and navigation systems.

[0045] In the process of generating the target steering torque, the ECU receives real-time steering angle data from the steering angle sensor, vehicle speed data from the vehicle speed sensor, and environmental data from various environmental sensors; and the ECU calculates the corresponding target steering torque based on these data and a preset algorithm model.

[0046] The specific calculation process may include:

[0047] Turning angle data: Analyze the steering angle, steering angular velocity, steering angular acceleration and steering direction to determine the driver's steering intention.

[0048] Vehicle speed: Adjust the amount of steering assistance according to the vehicle speed to ensure steering comfort at different speeds.

[0049] Environmental data: 1. Road conditions: If the road is slippery, increase the steering assist appropriately to improve stability. 2. Weather conditions: In rainy and snowy weather, adjust the steering torque to improve grip. 3. Traffic conditions: In congested roads, provide more direct steering feedback to improve handling. 4. Navigation information: According to the navigation prompts, adjust the steering torque in advance and prepare for turning.

[0050] The ECU combines these factors to generate the final target steering torque, thereby ensuring that the generation of the steering torque fully considers the current driving conditions and environmental factors, and improves the accuracy and safety of steering.

[0051] It should be pointed out here that in the process of determining the corresponding target steering torque, the following is also included:

[0052] S111, the electronic control unit calls the corresponding intelligent prediction unit to determine the current driving behavior of the driver based on the vehicle speed data and the environmental data.

[0053] Among them, the electronic control unit (ECU) calls a pre-trained intelligent prediction unit, which is a multi-layer neural network model. The multi-layer neural network model is pre-trained and generated based on historical driving data. The multi-layer neural network model has learned the relationship between different driving behaviors and vehicle status and environmental data.

[0054] The ECU inputs the current vehicle speed data and environmental data (such as road conditions, weather conditions, traffic conditions, navigation information, etc.) into the intelligent prediction unit, which analyzes the driver's current driving behavior based on this data. For example, the model may determine whether the driver is driving slowly on urban roads or driving quickly on highways. This can accurately identify the driver's current driving behavior, provide accurate input data for subsequent multi-level predictions, and help the system adjust the steering torque more accurately.

[0055] S112, inputting the current driving behavior and the real-time turning angle data into an intelligent prediction unit to generate a corresponding multi-level prediction result, and generating a plurality of corresponding next steering torques based on the multi-level prediction result.

[0056] Among them, the ECU inputs the current driving behavior and real-time turning angle data into the intelligent prediction unit, and the intelligent prediction unit generates multi-level prediction results based on these data. The multi-level prediction results include multiple next-step turning intentions based on time series sorting, for example, predicting the driver's turning intention at several points in the future. In this way, the driver's next turning intention can be predicted in advance, providing a basis for generating the target steering torque and optimizing the prediction ability of the steering system.

[0057] Then, the ECU generates multiple corresponding next steering torques based on the multi-level prediction results. Each prediction result corresponds to a future steering intention, so multiple steering torques are generated, which cover the steering needs at multiple time points in the future. Through the multi-level prediction results, the steering torque is adjusted in advance to reduce the impact of sudden steering operations and improve the response speed and accuracy of the steering system.

[0058] S120, generating a corresponding control instruction based on the target steering torque, driving a motor installed on the steering mechanism to generate a corresponding steering torque according to the control instruction, and collecting corresponding real-time road surface feedback from the driver.

[0059] Among them, the electronic control unit (ECU) generates corresponding control instructions according to the calculated target steering torque, and the control instructions contain the specific parameters of the steering torque that the motor needs to generate, such as torque size, direction, duration, etc. These control instructions are transmitted to the motor controller installed on the steering mechanism through a high-speed communication bus (such as CAN bus). By converting the calculated target steering torque into specific motor control instructions, it can ensure that the motor can accurately perform steering operations and ensure the real-time and accuracy of the steering system.

[0060] The motor controller receives the control instructions transmitted by the ECU, parses the control instructions, and controls the motor to generate steering torque according to the specified torque magnitude and direction. The motor transmits the steering torque to the wheels through a mechanical transmission system (such as gears, connecting rods, etc.) to achieve vehicle steering.

[0061] Then, the feedback motor installed on the steering mechanism collects the driver's actual feelings under different road conditions. The feedback motor simulates the steering feeling under different road conditions and feeds back to the driver through torque. The torque change of the feedback motor is monitored in real time by the sensor and the data is transmitted back to the ECU. By providing the driver with real-time road feedback, the driver's perception of road conditions is enhanced, and the steering torque can also be dynamically adjusted through the feedback mechanism.

[0062] It should be noted here that, for the feedback motor, in the solution in the embodiment of the present application, the method further performs the following steps:

[0063] S121, real-time collection of real-time response data of the driver corresponding to different feedback strengths and different feedback frequencies of the feedback motor.

[0064] Among them, the feedback motor installed on the steering wheel is used to adjust the strength and frequency of the feedback motor in real time. The feedback motor provides different feedback strengths and frequencies according to different road conditions and driving conditions. The driver's response to these feedbacks is monitored in real time through sensors, such as the driver's hand movements, changes in steering force, etc., and then these real-time response data are transmitted to the electronic control unit through a high-speed communication bus, so that the driver's actual response to different feedback strengths and frequencies can be understood.

[0065] S122, retrieve corresponding historical feedback data, predict the driver's reaction data to different feedbacks from the feedback motor based on the feedback data and the real-time reaction data, and determine the corresponding feedback mechanism.

[0066] The ECU retrieves the stored historical feedback data, which includes the historical feedback strength and frequency of the feedback motor. The historical feedback data can come from the driving records of the same driver or other drivers. The historical feedback data is usually stored in the vehicle's database or cloud server. Through the historical feedback data, common feedback patterns and driver preferences can be discovered.

[0067] The ECU then inputs the real-time reaction data and historical feedback data into the prediction model, which is a machine learning model that can predict the driver's reaction to different feedback strengths and frequencies based on historical data and real-time data. The prediction model generates prediction results and determines the most appropriate feedback mechanism, which includes the strength and frequency settings of the feedback motor under different road conditions.

[0068] Through the predictive model, we can understand the driver's reaction to different feedbacks in advance and optimize the feedback mechanism, so as to ensure that the feedback mechanism can better adapt to the driver's needs and improve driving comfort and safety.

[0069] S123, generating multi-level feedback according to the multi-level prediction results and the feedback mechanism, and dynamically adjusting the strength and frequency of the feedback motor based on the multi-level feedback.

[0070] Among them, the ECU generates multi-level feedback based on the multi-level prediction results and the determined feedback mechanism. The multi-level feedback is based on multiple next-step feedback intentions sorted in time series. Each level of prediction results corresponds to a level of feedback. For example, the driver's feedback needs at several points in the future are predicted, and the corresponding feedback strength and frequency settings are generated. Through multi-level feedback, the strength and frequency of the feedback motor are adjusted in advance to reduce the impact of sudden feedback, thereby improving the response speed and accuracy of the feedback system.

[0071] In addition, the ECU dynamically adjusts the strength and frequency of the feedback motor based on the generated multi-level feedback. The feedback motor provides corresponding feedback strength and frequency in real time based on the adjusted settings. The feedback effect is monitored in real time through sensors to ensure the effectiveness of the feedback mechanism.

[0072] Based on executing the above steps S121 to S123, the system can understand the driver's reaction to different feedbacks in advance through the prediction model, and can adjust the steering torque more timely to reduce the occurrence of unexpected situations; according to the generated multi-level feedback, by dynamically adjusting the strength and frequency of the feedback motor, the system can provide more intuitive road feedback; through real-time feedback, the driver can better perceive the road conditions and improve the ability to cope with complex road conditions.

[0073] In the embodiment of the present application, during the process of the driver driving the vehicle, the method further includes:

[0074] S131, determining a target driver, and acquiring corresponding real-time operation data based on the target driver.

[0075] Among them, the driver currently driving the vehicle is determined through an identity authentication system (such as fingerprint recognition, facial recognition, etc.), the system records the identity information of the target driver and matches it with the driver profile in the database; the real-time operation data of the target driver is collected in real time through devices such as steering angle sensors and vehicle speed sensors installed on the steering wheel. In an embodiment of the present application, the real-time operation data includes steering angle, steering angular velocity, steering angular acceleration, steering direction and vehicle speed, and these data are transmitted to the electronic control unit via a high-speed communication bus (such as a CAN bus).

[0076] S132, retrieve a corresponding driving habit model based on the target driver.

[0077] Among them, the ECU retrieves the driving habit model of the target driver stored in the database. In the embodiment of the present application, the driving habit model is a multi-layer neural network model, which is pre-trained and generated based on the historical driving data of the target driver. The driving habit model has learned the driving behavior pattern of the target driver, including steering habits, speed control, etc.

[0078] S133, inputting the real-time operation data into the driving habit model to determine a generation rule for generating the target steering torque.

[0079] The ECU inputs real-time operation data (steering angle, steering angular velocity, steering angular acceleration, steering direction and vehicle speed) into the driving habit model, which generates the target steering torque generation rules based on these data. In addition, the generation rules include adjusting the size of the steering assist, the speed of the steering response and matching the driver's driving preferences. For example, if the driver is accustomed to fast steering, the system can appropriately increase the size of the steering assist and the response speed.

[0080] Through the driving habit model, personalized steering torque generation rules are generated to ensure that the system can be adjusted according to the driver's preferences, improve driving comfort and controllability, reduce unnecessary operating burden, and enhance the driving experience.

[0081] Based on executing steps S131 to S133, by identifying the target driver and calling up his driving habit model, the system can provide personalized steering torque generation rules to meet the driving preferences of different drivers; at the same time, it adjusts the steering assist and response speed according to the driver's driving habits to improve driving comfort and controllability and reduce unnecessary operating burden.

[0082] While the vehicle is traveling, the method further includes:

[0083] S141, retrieve the environment data corresponding to the vehicle and the current status data of the vehicle.

[0084] Among them, environmental data, including the position and distance of obstacles in front of the vehicle, are collected in real time through various sensors on the vehicle (such as cameras, radars, laser radars, etc.), and vehicle current status data, including vehicle speed, steering angle, steering angular velocity, etc., are collected in real time through speed sensors, steering sensors and other devices. These data are transmitted to the electronic control unit (ECU) through a high-speed communication bus (such as CAN bus).

[0085] S142, judging whether there is a potential collision risk based on the real-time environmental data and the vehicle's current state data; if so, determining the corresponding optimal avoidance path and generating a corresponding emergency steering torque command.

[0086] Among them, the ECU receives real-time environmental data and vehicle current status data from sensors, and the ECU analyzes these data through built-in algorithm models (such as collision detection algorithms) to determine whether there is a potential collision risk; for example, the radar detects that there is an obstacle ahead and the distance is close, and combines the vehicle's current speed and steering status to determine whether there is a collision risk.

[0087] Secondly, if it is determined that there is a potential risk of collision, the ECU calls the avoidance path planning algorithm to determine the optimal avoidance path, where the determination of the optimal avoidance path takes into account factors such as the vehicle's current speed, steering ability, and the location and distance of obstacles; based on the optimal avoidance path, the corresponding emergency steering torque command is generated, which includes the required steering torque size and direction. By planning the optimal avoidance path, it ensures that the vehicle can safely avoid obstacles, reduce the risk of collision, generate emergency steering torque commands, and provide specific control instructions for subsequent steering operations.

[0088] S143, transmitting to the motor installed on the steering mechanism via the high-speed communication bus, and generating a corresponding target steering torque based on the emergency steering torque command received by the motor.

[0089] Among them, the ECU transmits the generated emergency steering torque command to the motor controller installed on the steering mechanism through a high-speed communication bus (such as CAN bus). The motor controller receives and analyzes the command and controls the motor to generate the corresponding steering torque according to the command. This ensures the real-time transmission of the emergency steering torque command, reduces the delay of signal transmission, and ensures the response speed and accuracy of the steering system, thereby improving the efficiency of risk avoidance operations.

[0090] Furthermore, the motor controller controls the motor to generate corresponding steering torque according to the received emergency steering torque command. The motor transmits the steering torque to the wheels through the mechanical transmission system (such as gears, connecting rods, etc.) to realize the vehicle's emergency avoidance steering, thereby realizing the actual output of emergency avoidance steering, ensuring that the vehicle can avoid obstacles quickly and accurately, improving driving safety and reliability, and reducing the occurrence of collision accidents.

[0091] Based on executing steps S141 to S143, the solution can achieve real-time monitoring of the vehicle's surroundings and accurate judgment of potential collision risks, ensuring that emergency avoidance operations can be performed quickly and accurately when risks exist.

[0092] During the driving process of the vehicle, if there is an adjacent vehicle, the method further includes:

[0093] S151, receiving the adjacent vehicle status information of the adjacent vehicle in real time through the V2V communication module, and retrieving the corresponding environmental data.

[0094] Among them, the moving vehicle is equipped with a V2V (Vehicle-to-Vehicle) communication module, which communicates with other vehicles through wireless communication technology (such as DSRC or 5G). The V2V communication module receives real-time status information of nearby vehicles, including position, speed, acceleration, driving direction and steering intention; these status information are transmitted to the electronic control unit (ECU) through a high-speed communication bus (such as CAN bus).

[0095] In addition, the ECU retrieves the vehicle's current environmental data, including road conditions, weather conditions, traffic conditions, navigation information, etc. Similarly, environmental data is collected in real time through various sensors on the vehicle (such as cameras, radars, lidars, etc.), and these environmental data are also transmitted to the ECU through a high-speed communication bus.

[0096] S152, analyzing the state information of nearby vehicles and environmental data to obtain corresponding comprehensive analysis results.

[0097] Among them, the ECU conducts a comprehensive analysis of the received nearby vehicle status information and environmental data; the specific analysis process includes:

[0098] Position analysis: Determine the position relationship of adjacent vehicles relative to the vehicle.

[0099] Speed ​​and acceleration analysis: Evaluate the speed and acceleration changes of nearby vehicles to determine their movement trends.

[0100] Driving direction and turning intention analysis: Predict the driving direction and possible turning intention of nearby vehicles.

[0101] Environmental Factor Analysis: Evaluates current driving conditions based on environmental factors such as road conditions, weather conditions, and traffic conditions.

[0102] The comprehensive analysis results include the movement trends of adjacent vehicles, potential intersections, possible danger areas, etc. Through comprehensive analysis, we can fully understand the behavior of adjacent vehicles and environmental conditions, providing an accurate basis for generating potential dangerous situations, thereby improving the system's predictability and accuracy and reducing potential collision risks.

[0103] S153, generating a corresponding potential danger situation based on the comprehensive analysis result, and adjusting the target steering torque in advance based on the potential danger situation.

[0104] Among them, the ECU generates potential dangerous situations based on the comprehensive analysis results, and the generation of potential dangerous situations takes into account factors such as the movement trend of adjacent vehicles, possible intersections, and current driving conditions; for example, if an adjacent vehicle is accelerating towards the vehicle and the two vehicles may meet at a certain intersection, the system will generate a potential collision risk. By generating potential dangerous situations, possible collision risks can be identified in advance.

[0105] Furthermore, the ECU adjusts the target steering torque in advance according to the generated potential dangerous situation, and the purpose of adjusting the target steering torque is to optimize steering control and reduce potential collision risks. For example, if the potential dangerous situation is that an adjacent vehicle may overtake from the right, the system will adjust the steering torque in advance to make the vehicle slightly shift to the left to avoid potential collision.

[0106] At the same time, the adjusted target steering torque is transmitted to the motor installed on the steering mechanism through a high-speed communication bus. The motor generates corresponding steering torque according to the received instructions to realize the vehicle's advance risk avoidance operation.

[0107] Based on the comprehensive application of steps S151 to S153, real-time monitoring and comprehensive analysis of the status of nearby vehicles and environmental conditions can be achieved, potential dangerous situations can be identified in advance, and steering control can be optimized to reduce potential collision risks, thereby significantly improving the vehicle's risk avoidance capabilities and overall driving safety.

[0108] During the use of the vehicle, the method also includes:

[0109] S161, acquiring monitoring data corresponding to the vehicle in real time.

[0110] Similarly, the vehicle's operating status is monitored in real time through various sensors on the vehicle, and the health of the vehicle's steering system is monitored in real time through the diagnostic system (such as the OBD-II interface), including sensor status, communication status, motor status, etc. These monitoring data are transmitted to the ECU through a high-speed communication bus. By providing real-time data on the vehicle's operating status and the health of the steering system, accurate information can be provided for subsequent abnormality detection and fault judgment.

[0111] S162, judging whether there is abnormal operation or system failure during vehicle driving based on the monitoring data.

[0112] The ECU receives monitoring data from sensors and diagnostic systems. The ECU analyzes this data through built-in algorithm models (such as anomaly detection algorithms) to determine whether there is abnormal operation or system failure.

[0113] Among them, abnormal operations include:

[0114] 1. Sudden large-angle steering: detect whether the change in steering angle exceeds the preset threshold; unreasonable steering speed change: detect whether the change in steering speed exceeds the normal range.

[0115] 2. System failures include: sensor failure: detect whether the sensor returns abnormal data or no response; communication interruption: detect whether the communication bus is interrupted or data is lost; motor failure: detect whether the motor works abnormally or stops working.

[0116] S163, when abnormal operation or system failure is detected, the electronic control unit immediately activates the safety protection mechanism.

[0117] Among them, if the ECU detects abnormal operation or system failure, the safety protection mechanism is immediately activated. The safety protection mechanism includes: automatic steering angle limitation: the ECU sends a command to limit the maximum steering angle of the steering mechanism to prevent oversteering; completely lock the steering mechanism: in extreme cases, the ECU sends a command to completely lock the steering mechanism to prevent any steering operation.

[0118] These instructions are transmitted to the motor controller of the steering mechanism through a high-speed communication bus. The motor controller controls the motor to perform corresponding operations according to the received instructions. By automatically limiting the steering angle and completely locking the steering mechanism, the vehicle can remain stable in abnormal situations, reducing the risk of loss of control, improving driving safety and reliability, and protecting the lives of drivers and passengers.

[0119] Based on the comprehensive application of steps S161 to S163, the solution can realize real-time monitoring of the vehicle's operating status and the health of the steering system, accurately identify abnormal operations and system failures, and immediately activate the safety protection mechanism when necessary, significantly improving the safety and reliability of the vehicle and ensuring that effective protection measures can be taken quickly in abnormal situations to ensure driving safety.

[0120] The present application embodiment discloses a steering wheel control method based on the SBW system, referring to Figure 2 , the devices include but are not limited to:

[0121] The steering intention detection module 200 acquires corresponding real-time steering angle data through a steering angle sensor installed on the steering wheel, detects the corresponding steering intention of the driver based on the real-time steering angle data, converts the steering intention into a steering electrical signal, and transmits the steering electrical signal to the electronic control unit through a high-speed communication bus, wherein the real-time steering angle data includes steering angle, steering angular velocity, steering angular acceleration and steering direction;

[0122] The target steering torque determination module 210 obtains the vehicle speed data and the corresponding environmental data corresponding to the current state of the vehicle, and the electronic control unit determines the corresponding target steering torque based on the real-time steering angle data, the vehicle speed data and the environmental data, wherein the environmental data includes road conditions, weather conditions, traffic conditions, and navigation information;

[0123] A control instruction generation module 220 is used to generate corresponding control instructions based on the target steering torque, drive the motor installed on the steering mechanism to generate corresponding steering torque according to the control instructions, and collect the corresponding real-time road feedback from the driver;

[0124] The process of determining the corresponding target steering torque also includes:

[0125] The electronic control unit calls the corresponding intelligent prediction unit to determine the current driving behavior of the driver based on the vehicle speed data and the environmental data, wherein the intelligent prediction unit is a multi-layer neural network model pre-trained and generated based on historical driving data;

[0126] The current driving behavior and real-time turning angle data are input into the intelligent prediction unit to generate corresponding multi-level prediction results, and multiple corresponding next steering torques are generated based on the multi-level prediction results, wherein the multi-level prediction results include multiple next-step steering intentions sorted based on time series, and the target steering torque includes multiple next steering torques.

[0127] Furthermore, the device includes but is not limited to:

[0128] A real-time reaction data acquisition module, which is used to collect the real-time reaction data of the driver corresponding to different feedback strengths and different feedback frequencies of the feedback motor;

[0129] A feedback mechanism determination module retrieves corresponding historical feedback data, and predicts the driver's reaction data to different feedbacks from the feedback motor based on the feedback data and the real-time reaction data, so as to determine the corresponding feedback mechanism, wherein the historical feedback data is the historical feedback strength and the historical feedback frequency corresponding to the feedback motor;

[0130] The multi-level feedback generation module is used to generate multi-level feedback according to the multi-level prediction results and the feedback mechanism, and dynamically adjust the strength and frequency of the feedback motor based on the multi-level feedback, wherein each level of prediction result corresponds to one level of feedback.

[0131] Furthermore, the device includes but is not limited to:

[0132] A real-time operation data acquisition module is used to determine a target driver and acquire corresponding real-time operation data based on the target driver, wherein the real-time operation data includes steering angle, steering angular velocity, steering angular acceleration, steering direction, and vehicle speed;

[0133] A driving habit model retrieval module is used to retrieve a corresponding driving habit model based on a target driver, wherein the driving habit model is trained in advance by a multi-layer neural network model;

[0134] A generation rule determination module inputs real-time operation data into a driving habit model for determining a generation rule for generating a target steering torque, wherein the generation rule includes adjusting the magnitude of the steering assist, the speed of the steering response, and matching the driver's driving preference.

[0135] Furthermore, the device includes but is not limited to:

[0136] The vehicle current state data retrieval module is used to retrieve the environment data corresponding to the vehicle and the vehicle current state data, wherein the environment data also includes the position and distance of obstacles in front of the vehicle;

[0137] The potential collision risk judgment module is used to judge whether there is a potential collision risk based on real-time environmental data and the current state data of the vehicle; if there is, the corresponding optimal avoidance path is determined and the corresponding emergency steering torque command is generated;

[0138] The target steering torque generation module transmits the information to the motor installed on the steering mechanism through a high-speed communication bus, and is used to generate the corresponding target steering torque based on the emergency steering torque command received by the motor.

[0139] Furthermore, the device includes but is not limited to:

[0140] The adjacent vehicle status information receiving module is used to receive the adjacent vehicle status information of the adjacent vehicle in real time through the V2V communication module and retrieve the corresponding environmental data. The adjacent vehicle status information includes position, speed, acceleration, driving direction, and steering intention;

[0141] A comprehensive analysis result acquisition module analyzes the state information of nearby vehicles and environmental data to obtain corresponding comprehensive analysis results;

[0142] Based on the comprehensive analysis results, corresponding potential dangerous situations are generated, and the target steering torque is adjusted in advance based on the potential dangerous situations.

[0143] Furthermore, the device includes but is not limited to:

[0144] A monitoring data acquisition module is used to acquire monitoring data corresponding to the vehicle in real time, wherein the detection data includes the corresponding operating status of the vehicle and the health status of the vehicle steering system;

[0145] The abnormality judgment module is used to judge whether there is abnormal operation or system failure during vehicle driving based on the monitoring data. Abnormal operation includes sudden large-angle steering and unreasonable steering speed change. System failure includes sensor failure, communication interruption and motor failure.

[0146] The safety protection mechanism startup module is used by the electronic control unit to immediately start the safety protection mechanism when abnormal operation or system failure is detected. The safety protection mechanism includes automatically limiting the steering angle and completely locking the steering mechanism.

[0147] An embodiment of the present application further discloses a steering wheel control system based on an SBW system, comprising a processor, wherein a program of any one of the above-mentioned steering wheel control methods based on the SBW system is running on the processor.

[0148] The embodiment of the present application further discloses a storage medium storing a program of any one of the above-mentioned steering wheel control methods based on the SBW system.

[0149] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A steering wheel control method based on SBW system, characterized in that: include: Acquire corresponding real-time steering angle data through a steering angle sensor installed on the steering wheel, detect the driver's corresponding steering intention based on the real-time steering angle data, convert the steering intention into a steering electrical signal, and transmit the steering electrical signal to an electronic control unit through a high-speed communication bus, wherein the real-time steering angle data includes steering angle, steering angular velocity, steering angular acceleration and steering direction; Obtaining vehicle speed data and corresponding environmental data corresponding to the current state of the vehicle, the electronic control unit determines the corresponding target steering torque based on the real-time turning angle data, the vehicle speed data and the environmental data, the environmental data including road conditions, weather conditions, traffic conditions and navigation information, the road conditions are the flatness and slipperiness of the road surface detected by cameras and sensors, the weather conditions are the temperature, humidity and rainfall detected by meteorological sensors, the traffic conditions are the front vehicles, pedestrians and obstacles detected by radar and cameras, and the navigation information is the current position, route planning and turn prompts obtained by GPS and navigation system; In the process of determining the corresponding target steering torque, it also includes: the electronic control unit calls the corresponding intelligent prediction unit to determine the driver's corresponding current driving behavior based on the vehicle speed data and the environmental data, wherein the intelligent prediction unit is a multi-layer neural network model pre-trained and generated according to historical driving data; the current driving behavior and the real-time turning angle data are input into the intelligent prediction unit to generate a corresponding multi-level prediction result, and the electronic control unit generates a plurality of corresponding next steering torques according to the multi-level prediction results, each prediction result corresponds to a future steering intention, wherein the multi-level prediction results include a plurality of next-step steering intentions sorted based on a time series, and correspondingly, the multi-level prediction results include predicting the driver's steering intentions at several future time points, and the target steering torque includes a plurality of next steering torques; A corresponding control instruction is generated based on the target steering torque, and a motor installed on the steering mechanism is driven according to the control instruction to generate a corresponding steering torque. During the driving of the vehicle, the method also includes: retrieving environmental data corresponding to the vehicle and current status data of the vehicle, wherein the environmental data also includes the position and distance of obstacles in front of the vehicle; judging whether there is a potential collision risk based on the real-time environmental data and the current status data of the vehicle; if so, determining the corresponding optimal avoidance path and generating a corresponding emergency steering torque instruction; transmitting to the motor installed on the steering mechanism through a high-speed communication bus, generating a corresponding target steering torque based on the motor according to the received emergency steering torque instruction; and collecting the corresponding real-time road surface feedback from the driver.

2. The steering wheel control method based on the SBW system according to claim 1, characterized in that: The method also includes: Real-time collection of the driver's response data to the different feedback strengths and different feedback frequencies of the feedback motor; Retrieving corresponding historical feedback data, predicting the driver's reaction data to different feedbacks from the feedback motor based on the feedback data and the real-time reaction data, and determining a corresponding feedback mechanism, wherein the historical feedback data is the historical feedback strength and the historical feedback frequency corresponding to the feedback motor; Multi-level feedback is generated according to the multi-level prediction results and the feedback mechanism, and the strength and frequency of the feedback motor are dynamically adjusted based on the multi-level feedback, wherein each level of prediction result corresponds to one level of feedback.

3. The steering wheel control method based on the SBW system according to claim 2, characterized in that: During the process of the driver driving the vehicle, the method further includes: Determine a target driver, and obtain corresponding real-time operation data based on the target driver, wherein the real-time operation data includes a steering angle, a steering angular velocity, a steering angular acceleration, a steering direction, and a vehicle speed; Retrieving a corresponding driving habit model based on the target driver, wherein the driving habit model is trained in advance by a multi-layer neural network model; The real-time operation data is input into a driving habit model to determine a generation rule for generating a target steering torque, wherein the generation rule includes adjusting the magnitude of the steering assist, the speed of the steering response, and matching the driver's driving preference.

4. The steering wheel control method based on the SBW system according to claim 3, characterized in that: If there is an adjacent vehicle, the method further includes: The V2V communication module receives the status information of the neighboring vehicles in real time and retrieves the corresponding environmental data. The status information of the neighboring vehicles includes the position, speed, acceleration, driving direction and steering intention. Analyze the adjacent vehicle status information and the environmental data to obtain a corresponding comprehensive analysis result; A corresponding potential danger situation is generated based on the comprehensive analysis result, and the target steering torque is adjusted in advance based on the potential danger situation.

5. The steering wheel control method based on the SBW system according to claim 4, characterized in that: The method also includes: Acquire monitoring data corresponding to the vehicle in real time, wherein the detection data includes the corresponding operating status of the vehicle and the health status of the vehicle steering system; Based on the monitoring data, determine whether there is abnormal operation or system failure during vehicle driving, wherein abnormal operation includes sudden large-angle steering and unreasonable steering speed change, and system failure includes sensor failure, communication interruption, and motor failure; When abnormal operation or system failure is detected, the electronic control unit immediately activates the safety protection mechanism, which includes automatically limiting the steering angle and completely locking the steering mechanism.

6. A steering wheel control device based on the SBW system, characterized in that: include: A steering intention detection module, which obtains corresponding real-time steering angle data through a steering angle sensor installed on the steering wheel, detects the driver's corresponding steering intention based on the real-time steering angle data, converts the steering intention into a steering electrical signal, and transmits the steering electrical signal to the electronic control unit through a high-speed communication bus, wherein the real-time steering angle data includes steering angle, steering angular velocity, steering angular acceleration and steering direction; a target steering torque determination module, which obtains the vehicle speed data and the corresponding environmental data corresponding to the current state of the vehicle, and the electronic control unit is used to determine the corresponding target steering torque based on the real-time steering angle data, the vehicle speed data and the environmental data, wherein the environmental data includes road conditions, weather conditions, traffic conditions and navigation information, wherein the road conditions are the flatness and slipperiness of the road surface detected by cameras and sensors, the weather conditions are the temperature, humidity and rainfall detected by meteorological sensors, the traffic conditions are the front vehicles, pedestrians and obstacles detected by radars and cameras, and the navigation information is the current position, route planning and turning prompts obtained by GPS and navigation systems; In the process of determining the corresponding target steering torque, it also includes: the electronic control unit calls the corresponding intelligent prediction unit to determine the driver's corresponding current driving behavior based on the vehicle speed data and the environmental data, wherein the intelligent prediction unit is a multi-layer neural network model pre-trained and generated according to historical driving data; the current driving behavior and the real-time turning angle data are input into the intelligent prediction unit to generate a corresponding multi-level prediction result, and the electronic control unit generates a plurality of corresponding next steering torques according to the multi-level prediction results, each prediction result corresponds to a future steering intention, wherein the multi-level prediction results include a plurality of next-step steering intentions sorted based on a time series, and correspondingly, the multi-level prediction results include predicting the driver's steering intentions at several future time points, and the target steering torque includes a plurality of next steering torques; A control instruction generation module is used to generate corresponding control instructions based on the target steering torque, and drives the motor installed on the steering mechanism to generate corresponding steering torque according to the control instruction. During the vehicle driving process, the method also includes: retrieving environmental data corresponding to the vehicle and current status data of the vehicle, wherein the environmental data also includes the position and distance of obstacles in front of the vehicle; judging whether there is a potential collision risk based on the real-time environmental data and the current status data of the vehicle; if so, determining the corresponding optimal avoidance path and generating a corresponding emergency steering torque instruction; transmitting to the motor installed on the steering mechanism through a high-speed communication bus, generating a corresponding target steering torque based on the motor according to the received emergency steering torque instruction; and collecting the corresponding real-time road surface feedback from the driver.

7. The steering wheel control device based on the SBW system according to claim 6, characterized in that: The device also includes: A real-time reaction data acquisition module, which is used to collect the real-time reaction data of the driver corresponding to different feedback strengths and different feedback frequencies of the feedback motor; A feedback mechanism determination module retrieves corresponding historical feedback data, and predicts the driver's reaction data to different feedbacks from the feedback motor based on the feedback data and the real-time reaction data, so as to determine the corresponding feedback mechanism, wherein the historical feedback data is the historical feedback strength and the historical feedback frequency corresponding to the feedback motor; A multi-level feedback generation module is used to generate multi-level feedback according to the multi-level prediction results and the feedback mechanism, and dynamically adjust the strength and frequency of the feedback motor based on the multi-level feedback, wherein each level of prediction result corresponds to one level of feedback.

8. A steering wheel control system based on the SBW system, characterized in that: It comprises a processor, in which a program of the steering wheel control method based on the SBW system as claimed in any one of claims 1 to 5 is run.

9. A storage medium, characterized in that: A program for the steering wheel control method based on the SBW system as claimed in any one of claims 1 to 5 is stored.

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