Steer-by-wire road feeling feedback adjusting method, device and equipment

Abstract

The invention provides a steering-by-wire road feeling feedback adjusting method, device and equipment, and is applied to the technical field of intelligent driving. The method comprises the steps of obtaining road sensing parameters of a vehicle and driving data of a driver; dynamically adjusting the torque gradient of the steering wheel of the vehicle according to the road sensing parameters. And determining the feedback intensity of the vehicle according to the driving data. And adjusting rotation parameters of the steering wheel by using the torque gradient of the steering wheel and the feedback intensity, and performing physical feedback on the driver. The problems that a traditional steer-by-wire system is difficult to restore rich road feeling feedback in a traditional mechanical steering system, and a driver cannot sense the road condition comprehensively and accurately can be solved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a method, device and equipment for adjusting road feel feedback of wire-controlled steering. Background Art

[0002] With the continuous advancement of automotive technology, steer-by-wire (SBW), a new steering technology, has gradually gained widespread adoption in modern intelligent vehicles. This system transmits the driver's steering intentions via electronic signals and adjusts the vehicle's direction in real time based on the vehicle's driving state and road conditions, achieving precise steering control. However, because SBW eliminates traditional mechanical connections, steering feel relies entirely on motor simulation, which presents a series of challenges to driving experience and safety.

[0003] In steer-by-wire systems, road feel simulation technology plays a crucial role. It's not only a key component in achieving technical functionality but also a core element in enhancing driving safety and experience. Traditional steer-by-wire systems typically use fixed-parameter road feel curves to simulate road feedback torque. While this method can provide a certain degree of road feel for the driver, it often ignores actual road conditions and the driver's driving habits, making it difficult to reproduce the rich road feel feedback found in traditional mechanical steering systems. This results in a less comprehensive and accurate perception of road conditions. Summary of the Invention

[0004] The purpose of this application is to provide a road feel feedback adjustment method, device and equipment for wire-controlled steering to solve the problem that traditional wire-controlled steering systems are difficult to restore the rich road feel feedback in traditional mechanical steering systems, resulting in the driver's perception of road conditions being insufficiently comprehensive and accurate.

[0005] In a first aspect, embodiments of the present application provide a method for adjusting road feel feedback in a steer-by-wire system. The method comprises: obtaining vehicle road feel parameters and driver driving data; dynamically adjusting the vehicle's steering wheel torque gradient based on the road feel parameters; determining the vehicle's feedback intensity based on the driving data; and adjusting steering wheel rotation parameters using the steering wheel torque gradient and feedback intensity to provide physical feedback to the driver.

[0006] The steer-by-wire road feel feedback adjustment method provided in the embodiments of the present application obtains the vehicle's road feel parameters and the driver's driving data. It dynamically adjusts the steering wheel torque gradient based on the vehicle's road feel parameters and determines the intensity of the vehicle's feedback to the user based on the driving data, providing the driver with a personalized driving experience. This method then precisely adjusts the steering wheel's rotation parameters, providing the driver with physical feedback that closely resembles a traditional mechanical steering system while the vehicle is in motion. This also ensures that the steering wheel's feedback is more consistent with the driver's driving habits and needs, allowing the driver to experience the road feel of a traditional mechanical steering system while driving the vehicle, thereby enhancing the driver's driving experience.

[0007] In one possible implementation, road feel parameters include tire parameters, body parameters, and motor parameters. Dynamically adjusting the vehicle's steering wheel torque gradient based on the road feel parameters includes determining interaction parameters between the vehicle and the road surface based on the tire, body, and motor parameters. Based on the interaction parameters, optimizing a preset road feel curve to obtain an optimized road feel curve. Using the optimized road feel curve, the vehicle's steering wheel torque gradient is adjusted.

[0008] This possible implementation method, by integrating multi-dimensional road feel parameters, can accurately determine the interaction parameters between the vehicle and the road surface, providing a basis for optimizing the road feel curve. Based on the interaction parameters, the preset road feel curve is optimized, so that the optimized road feel curve can more realistically reflect the actual driving state of the vehicle under different road conditions, thereby making the adjustment of the steering wheel torque gradient more in line with actual driving needs.

[0009] In one possible implementation, the interaction parameters include: the type of road surface on which the vehicle is traveling, the contact state between the vehicle's tires and the road surface, and the friction coefficient between the tires and the road surface. Determining the interaction parameters between the vehicle and the road surface based on the tire parameters, vehicle body parameters, and motor parameters may include: identifying the type of road surface on which the vehicle is traveling using the tire parameters; determining the contact state between the vehicle's tires and the road surface based on the vehicle body parameters; and calculating the friction coefficient between the vehicle's tires and the road surface based on the motor parameters.

[0010] One possible implementation method uses the optimized road feel curve to adjust the vehicle's steering wheel torque gradient, including: collecting the vehicle's current driving condition parameters in real time, mapping the driving condition parameters to the road feel curve, and obtaining the steering wheel torque gradient.

[0011] One possible implementation method determines the intensity of vehicle feedback based on driving data, including: inputting the driving data into a pre-trained long short-term memory network, outputting the driver's steering habit data, and adjusting the intensity of the vehicle's feedback to the driver based on the steering habit data.

[0012] This possible implementation method can adjust the feedback intensity based on driving data, so that the steering wheel rotation parameters are more in line with the driver's preferences, allowing the driver to experience a driving experience that is more in line with their style during the driving process.

[0013] In one possible implementation, the method provided in an embodiment of the present application further includes: adjusting the network weights of the long short-term memory network using the collected driving data of the driver according to a preset adjustment period, so that the long short-term memory network matches the driver's steering habits.

[0014] One possible implementation uses steering wheel torque gradient and feedback strength to adjust steering wheel rotation parameters and provide physical feedback to the driver. This includes determining the vehicle's steering wheel feedback damping based on the steering wheel torque gradient and feedback strength. Physical feedback is provided to the driver using a preset base steering resistance, the vehicle's steering wheel feedback damping, and a feedback vibration pattern. The base steering resistance simulates the natural return torque of the vehicle's steering system.

[0015] In a second aspect, an embodiment of the present application provides a road feel feedback adjustment device for wire-controlled steering, which includes: an acquisition module, an adjustment module and a determination module.

[0016] Among them, the acquisition module is used to obtain the vehicle's road feeling parameters and the driver's driving data.

[0017] The adjustment module is used to dynamically adjust the vehicle's steering wheel torque gradient according to road feel parameters.

[0018] The determination module is used to determine the feedback intensity of the vehicle according to the driving data.

[0019] The adjustment module is also used to adjust the steering wheel rotation parameters using the steering wheel torque gradient and feedback strength to provide physical feedback to the driver.

[0020] In one possible implementation, road feel parameters include tire parameters, vehicle body parameters, and motor parameters. The adjustment module is specifically configured to determine interaction parameters between the vehicle and the road surface based on the tire, vehicle body, and motor parameters. Based on the interaction parameters, a preset road feel curve is optimized to generate an optimized road feel curve. The optimized road feel curve is used to adjust the vehicle's steering wheel torque gradient.

[0021] In one possible implementation, the interaction parameters include: the type of road surface on which the vehicle is traveling, the contact state between the vehicle's tires and the road surface, and the friction coefficient between the tires and the road surface. The determination module is specifically configured to identify the type of road surface on which the vehicle is traveling using the tire parameters. The contact state between the vehicle's tires and the road surface is determined based on the vehicle body parameters. The friction coefficient between the vehicle's tires and the road surface is calculated based on the motor parameters.

[0022] One possible implementation involves an adjustment module that collects the vehicle's current driving condition parameters in real time, maps the driving condition parameters to a road feel curve, and obtains a steering wheel torque gradient.

[0023] In one possible implementation, a determination module is specifically configured to input driving data into a pre-trained long short-term memory network, output the driver's steering habit data, and adjust the intensity of the vehicle's feedback to the driver based on the steering habit data.

[0024] In one possible implementation, the determination module is further configured to adjust the network weights of the LSTM network using collected driver driving data according to a preset adjustment cycle, so that the LSTM network matches the driver's steering habits. In another possible implementation, the adjustment module is specifically configured to determine the vehicle's steering wheel feedback damping based on the steering wheel torque gradient and feedback intensity. Physical feedback is provided to the driver using a preset basic steering resistance, the vehicle's steering wheel feedback damping, and a feedback vibration pattern. The basic steering resistance is used to simulate the natural return torque of the vehicle's steering system.

[0025] In a third aspect, embodiments of the present application provide a steer-by-wire road feel feedback adjustment device, which functions to implement the steer-by-wire road feel feedback adjustment method described in the first aspect or any possible implementation of the first aspect. This function can be implemented in hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the aforementioned functions.

[0026] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, the computer can execute the road feel feedback adjustment method of the wire-controlled steering according to the above-mentioned first aspect or any possible implementation method of the first aspect.

[0027] In a fifth aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the road feel feedback adjustment method for wire-controlled steering according to the first aspect or any possible implementation method.

[0028] Among them, the technical effects brought about by any implementation method in the second to fifth aspects can refer to the technical effects brought about by the possible implementation method in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 A system architecture diagram of a road feel feedback adjustment system for steer-by-wire provided in an embodiment of the present application; FIG2 is a flow chart of a method for adjusting road feel feedback of steer-by-wire according to an embodiment of the present application; Figure 3 An algorithm framework diagram of a long short-term memory network provided in an embodiment of the present application; Figure 4 A schematic structural diagram of a road feel feedback adjustment device for steer-by-wire provided in an embodiment of the present application; Figure 5 Another system architecture diagram of a road feel feedback adjustment system for steer-by-wire provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0032] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0033] In traditional steer-by-wire systems, road feel simulation uses algorithms and motors to mimic the "road feedback" perceived by the driver in traditional mechanical steering systems. However, these algorithms struggle to accurately simulate nonlinear mechanical behaviors such as tire slip and sudden changes in grip, potentially leading to feedback distortion. Furthermore, the accuracy of road feel simulation is highly dependent on the road recognition capabilities of the vehicle's sensors (such as cameras and radar). In the event of sensor failure or signal interference (such as heavy rain or strong sunlight), feedback may fail or be misinterpreted.

[0034] Based on this, embodiments of the present application provide a method for adjusting road feel feedback in a steer-by-wire system. The method comprises obtaining a vehicle's road feel parameters and a driver's driving data. Based on the road feel parameters, the vehicle's steering wheel torque gradient is dynamically adjusted. Based on the driving data, the vehicle's feedback intensity is determined. The steering wheel torque gradient and feedback intensity are used to adjust steering wheel rotation parameters, providing physical feedback to the driver.

[0035] The steer-by-wire road feel feedback adjustment method provided in the embodiments of the present application obtains the vehicle's road feel parameters and the driver's driving data. It dynamically adjusts the steering wheel torque gradient based on the vehicle's road feel parameters and determines the intensity of the vehicle's feedback to the user based on the driving data, providing the driver with a personalized driving experience. This method then precisely adjusts the steering wheel's rotation parameters, providing the driver with physical feedback that closely resembles that of a traditional mechanical steering system during driving, allowing the driver to experience the road feel of a traditional mechanical steering system while driving the vehicle, thereby enhancing the driver's driving experience.

[0036] The solution provided in the embodiments of the present application will be described below with reference to specific drawings.

[0037] On the one hand, the embodiment of the present application provides a road feel feedback adjustment system for wire-controlled steering. Figure 1 As shown, the road feel feedback adjustment system 100 for steer-by-wire may include a sensor 101 , a control unit 102 and a feedback module 103 .

[0038] The sensor 101 is used to collect the vehicle's road feeling parameters in real time and send the road feeling parameters to the control unit 102. For example, the sensor may include a pressure sensor, an acoustic sensor, an inertial sensor, and a motor.

[0039] Specifically, the pressure sensor can be used to collect the ground pressure distribution between the tire and the ground in real time. For example, the pressure sensor can be an embedded pressure sensor array, which is arranged on the inside of the tire tread. The sound sensor can be used to capture the noise spectrum generated by the contact between the tire and the road in real time. For example, the sound sensor can be a tire noise microphone, which is installed on the inside of the tire hub. The inertial sensor can be used to collect the vehicle body data during driving. For example, the inertial sensor can be arranged on the important part of the vehicle body. Among them, the vehicle body data may include angular velocity, acceleration, speed, etc. The motor is used to monitor the current ripple during the vehicle steering process in real time. For example, the motor can be the steering motor of the vehicle.

[0040] The control unit 102 is configured to combine the road feel parameters collected by the sensors with the user's driving data stored in the system, and determine the steering wheel rotation parameters using the road feel feedback adjustment method for steer-by-wire provided in the embodiments of the present application. The control unit 102 encapsulates the rotation parameters into feedback instructions and sends the feedback instructions to the feedback module 103.

[0041] The feedback module 103 is configured to receive a feedback instruction sent by the control unit and provide physical feedback to the driver based on the steering wheel rotation parameters in the feedback instruction.

[0042] It should be noted that the above Figure 1 The illustrated steer-by-wire road feel feedback adjustment system 100 is merely an example of an application scenario of the present application solution, and does not limit the application scenario of the present application solution.

[0043] On the one hand, the embodiment of the present application provides a method for adjusting road feel feedback of wire-controlled steering. Figure 1 The illustrated steer-by-wire road feedback control system 100 is implemented. Figure 2 As shown, the method may include the following steps.

[0044] S201, obtaining the vehicle's road feeling parameters and the driver's driving data.

[0045] Among them, road feel parameters may include tire parameters, body parameters and motor parameters.

[0046] Specifically, tire parameters may include the ground contact pressure distribution between the tire and the ground, and tire noise during vehicle operation. Body parameters may include the vehicle's yaw rate and lateral acceleration during operation. Motor parameters may include the current ripple of the induction motor during vehicle operation.

[0047] The driving data may include the user's driving habit data, such as the angular velocity of the steering wheel when the user turns.

[0048] For example, during a vehicle's steering, a pressure sensor on the inside of the tire tread collects real-time information about the ground contact pressure distribution between the tire and the road. An acoustic sensor mounted on the inside of the wheel hub captures the noise spectrum generated by the tire's contact with the road. Body sensors collect the vehicle's yaw rate and lateral acceleration during driving. The motor monitors current ripple during steering.

[0049] S202: Dynamically adjust the steering wheel torque gradient of the vehicle according to the road feel parameters.

[0050] Specifically, the multimodal data from the road feel parameters are fused to calculate the interaction parameters between the vehicle's tires and the road surface. Based on these interaction parameters, the vehicle's steering wheel torque gradient is dynamically adjusted.

[0051] One possible implementation method is to determine the interaction parameters between the vehicle and the road surface based on tire parameters, body parameters, and motor parameters.

[0052] The interaction parameter can include a road surface type on which the vehicle travels, a contact state between a tire of the vehicle and the road surface, and a friction coefficient between the tire and the road surface.

[0053] Specifically, the tire parameter is used to identify the road surface type on which the vehicle travels.

[0054] For example, the tire ground pressure distribution is used to identify a pressure fluctuation frequency of the vehicle ground. If the pressure fluctuation frequency is a high-frequency small-amplitude fluctuation frequency (for example, a frequency of 50-200 Hz), it can be determined that the road surface type on which the vehicle travels is an asphalt / cement road surface. If the pressure fluctuation frequency is a low-frequency large-amplitude fluctuation (for example, a frequency of 10-50 Hz), it can be determined that the road surface type on which the vehicle travels is a gravel / non-paved road surface. If it is detected that the pressure distribution of the pressure fluctuation frequency is uniform and the amplitude decreases, it can be determined that the road surface type on which the vehicle travels is an icy / snowy road surface.

[0055] Meanwhile, the tire noise frequency is calculated by using a voiceprint analysis algorithm to determine the road surface type. If the tire noise frequency is a wideband noise, for example, the main frequency of the tire noise frequency is in the range of 2-5 KHz, it can be determined that the road surface type is an asphalt road at this time. If the tire noise frequency is a pulse noise, for example, the peak frequency band of the tire noise frequency is in the range of 500 Hz-1 KHz, it can be determined that the road surface type is a gravel road at this time. If the tire noise frequency is a sound pressure level reduction of 20-30 dB, it can be determined that the road surface type is an icy / snowy road at this time.

[0056] Specifically, the contact state between the tire of the vehicle and the road surface is determined according to the vehicle body parameter.

[0057] For example, the real-time tire side slip angle is calculated according to the vehicle yaw rate and the vehicle speed by the following dynamic model formula.

[0058] α = arctan ( ( - ) / ) ) - δ wherein , is a vehicle speed component, is a yaw rate, lf is a front track, δ is a steering angle, and α is a tire side slip angle.

[0059] The side slip force of the vehicle is calculated according to the tire side slip angle and the lateral acceleration by the following formula.

[0060]

[0061] wherein m is the mass of the vehicle, ay is the lateral acceleration, and Cα is the side slip stiffness.

[0062] Specifically, the friction coefficient between the tire of the vehicle and the road surface is calculated according to the motor parameter.

[0063] For example, the vehicle's resistance characteristics can be extracted by performing frequency domain analysis on the three-phase current ripple of the vehicle's motor. If the current fluctuations in the three-phase current ripple are stable, for example, THD < 5%, the vehicle's current resistance characteristics can be determined to be those of a high-friction road surface, such as dry asphalt. If the three-phase current ripple has current spikes, for example, THD > 15%, the vehicle's current resistance characteristics can be determined to be those of a low-friction road surface, such as ice or snow.

[0064] The friction coefficient between the vehicle's tires and the road surface is calculated using the following formula based on the effective value of the current ripple and the rated current.

[0065]

[0066] in, is the friction coefficient between the tire and the road, K is the calibration coefficient, Iripple is the effective value of the current ripple, is the rated current.

[0067] This process integrates multimodal road feel parameters to accurately calculate the interaction parameters between the tire and the road. For example, on rainy or icy roads, it can quickly sense the drop in adhesion and dynamically adjust the steering wheel torque gradient, increasing torque to enhance the driver's sense of control, helping the driver operate the steering wheel more smoothly and reducing the risk of vehicle loss of control.

[0068] Furthermore, after determining the interaction parameters between the vehicle and the road surface, the preset road feel curve is optimized based on the interaction parameters to obtain an optimized road feel curve.

[0069] Specifically, a Kalman filter and neural network are used to perform a weighted fusion of interaction parameters. For example, the road type, the contact state between the vehicle's tires and the road surface, and the friction coefficient between the tires and the road surface are integrated to obtain the vehicle's road feel curve parameters. The road feel curve parameters are then used to optimize the preset road feel curve to obtain an optimized road feel curve. Finally, the optimized road feel curve is used to adjust the vehicle's steering wheel torque gradient.

[0070] Furthermore, the optimized road feel curve is used to adjust the vehicle's steering wheel torque gradient.

[0071] Specifically, the vehicle's current driving condition parameters are collected in real time, mapped to a road feel curve, and the steering wheel torque gradient is obtained.

[0072] The driving condition parameters may include vehicle speed, load, tire wear, tire pressure, tire temperature, vehicle suspension travel, steering motor voltage, steering motor temperature, etc.

[0073] During this process, dynamic adjustment of the steering wheel torque gradient based on road feel parameters provides the driver with more precise and realistic steering feedback tailored to varying road conditions and driving environments. For example, on highways, the torque gradient can be optimized for smoother and more stable steering. On complex urban roads, the torque gradient sensitivity can be increased, allowing the driver to more acutely perceive road surface changes, enhancing driving comfort and controllability.

[0074] S203: Determine the feedback intensity of the vehicle based on the driving data.

[0075] Specifically, the user's driving data is collected according to a preset period, the user's driving habit data is tracked and analyzed from the driving data, and the feedback intensity of the vehicle to the user is determined based on the user's driving habit data.

[0076] One possible implementation method is to collect driving data according to a preset collection frequency and collection period, such as the user's driving behavior data, the vehicle's response data to the user, the context data of the vehicle's driving environment, and the user's active adjustment data.

[0077] The user's driving behavior can include steering angular velocity, steering acceleration, steering wheel torque, hand torque peak, and correction frequency. Vehicle response data to the user can include yaw rate delay, lateral acceleration overshoot, and trajectory tracking error. Contextual data about the vehicle's driving environment can include vehicle speed, road curvature, and estimated road friction coefficient. User-initiated adjustments can include whether the user manually adjusts the steering wheel power assist mode, such as comfort, sport, and off-road.

[0078] Furthermore, the collected driving data is input into a pre-trained long short-term memory network to output the driver's steering habit data.

[0079] Among them, such as Figure 3 FIG2 is an algorithm framework diagram of a long short-term memory network provided by an embodiment of the present application. The specific configuration of the long short-term memory network is shown below. The long short-term memory network is configured with an input gate, a forget gate, an output gate, and a memory unit.

[0080] The input gate is used to determine how much information in the currently input driving data is written into the memory unit. The calculation formula is as follows:

[0081]

[0082] in, is the output of the input gate, is the candidate memory unit state, σ is the Sigmoid function, W and b are the weight matrix and bias vector respectively.

[0083] The forget gate is used to control the state of the memory unit at the previous moment Ct The retention ratio is -1. The calculation formula is as follows:

[0084] The output gate is used to calculate the hidden state based on the current memory cell state. The calculation formula is as follows:

[0085]

[0086] Memory cells are the core of LSTM networks, responsible for maintaining and updating long-term dependency information throughout sequence processing. Memory cells have a relatively simple structure, consisting primarily of one or more neurons, whose state is propagated through time steps and updated only linearly.

[0087] Furthermore, according to a preset adjustment cycle, the network weights of the long short-term memory network are adjusted using the collected driving data of the driver, so that the long short-term memory network matches the driver's steering habits.

[0088] Specifically, the user's driving behavior data is fed into a pre-trained LSTM network to determine the user's operating style, such as aggressive or gentle. The vehicle's response data to the user is fed into a pre-trained LSTM network to determine the vehicle's response speed. The vehicle's driving environment context data is fed into a pre-trained LSTM network to determine the vehicle's driving scenario, such as high-speed driving, low-speed driving, or parking. The user's active adjustment data is fed into a pre-trained LSTM network to determine the user's driving preference.

[0089] The vehicle's feedback intensity is determined based on the user's operating style, the vehicle's response speed to the user, the vehicle's driving scenario, and the user's driving preferences.

[0090] Going further, the vehicle's feedback intensity to the driver is adjusted based on steering habit data.

[0091] For example, if a user's driving preference is to steer hard and turn the steering wheel sharply during steering, the vehicle's feedback intensity to the driver can be increased to enhance the user's sense of force when operating the steering wheel.

[0092] During this process, the feedback intensity is adjusted according to the driving data so that the steering wheel rotation parameters can be more in line with the driver's preferences, allowing the driver to experience a driving experience that is more in line with his or her style.

[0093] S204: Adjust the steering wheel rotation parameters using the steering wheel torque gradient and feedback strength to provide physical feedback to the driver.

[0094] Specifically, the feedback damping of the steering wheel to the driver is determined according to the steering wheel torque gradient and the feedback strength, and physical feedback is provided to the user based on the feedback damping of the steering wheel to the driver.

[0095] One possible implementation method is to determine the vehicle's steering wheel feedback damping based on the steering wheel torque gradient and feedback intensity. Physical feedback is then provided to the driver using a preset base steering resistance, the vehicle's steering wheel feedback damping, and a feedback vibration pattern.

[0096] The basic steering resistance is used to simulate the natural self-aligning torque inherent in the vehicle's steering system, while the feedback vibration mode is used to sense the road surface type and provide vibration feedback to the user based on the road surface type.

[0097] For example, a basic, stable steering force feedback foundation is provided for the steering wheel, starting with a preset basic steering resistance. On this basis, physical feedback is provided to the driver in combination with steering wheel feedback damping and feedback vibration mode.

[0098] It should be noted that the feedback vibration mode can also convey warning information to the driver through the vibration of the steering wheel in specific circumstances, such as lane departure warning or collision risk prompt. Its parameters such as vibration frequency, amplitude and duration can be flexibly configured according to actual needs.

[0099] The steer-by-wire road feedback adjustment method provided in the embodiments of this application dynamically adjusts the steering wheel torque gradient based on road conditions by acquiring the vehicle's road feel parameters and the driver's driving data. The method also determines the feedback intensity based on the driving data, allowing precise adjustments to the steering wheel's rotation parameters to provide physical feedback to the driver. This not only improves driving safety and stability, but also provides a personalized driving experience based on road conditions and driving habits, reducing driver fatigue and enhancing driver confidence. It also automatically adapts to different driving scenarios, making the driving process easier, more comfortable, and safer.

[0100] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the working principle of the device. It can be understood that in order to realize the above functions, the road feel feedback adjustment device of the wire-controlled steering includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In the embodiments of the present application, the road feel feedback adjustment device for steer-by-wire can be divided into functional modules based on the above-mentioned method examples. For example, different functional modules can be divided according to different functions, or two or more functions can be integrated into a single processing module. The above-mentioned integrated modules can be implemented in the form of hardware or software functional modules.

[0102] It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. In actual implementation, there may be other division methods. Figure 4 FIG. 1 shows a possible schematic diagram of the road feel feedback adjustment device for steer-by-wire in the above-mentioned embodiments. Figure 4 As shown, the road feel feedback adjustment device 400 for steer-by-wire may include: an acquisition module 401 , an adjustment module 402 and a determination module 403 .

[0103] The acquisition module 401 is used to support the execution of the road feel feedback adjustment device 400 of the wire-controlled steering. Figure 2 Schematic diagram of S201 in the road feel feedback adjustment method for wire-controlled steering.

[0104] Adjustment module 402, used to support the road feel feedback adjustment device 400 of the steer-by-wire control to execute Figure 2 Schematic diagram of S202 or S204 in the road feel feedback adjustment method of wire-controlled steering.

[0105] Determining module 403, for supporting the road feel feedback adjustment device 400 of the steer-by-wire control Figure 2 Schematic diagram of S403 in the road feel feedback adjustment method for steer-by-wire.

[0106] In a possible implementation, the road sense parameter includes a tire parameter, a vehicle body parameter, and a motor parameter. The road sense feedback adjustment device for steer-by-wire provided in the embodiments of the present application can also be used to determine an interaction parameter between the vehicle and the road surface according to the tire parameter, the vehicle body parameter, and the motor parameter. The preset road sense curve is optimized based on the interaction parameter to obtain an optimized road sense curve. The steering wheel torque gradient of the vehicle is adjusted by using the optimized road sense curve.

[0107] In a possible implementation, the interaction parameter includes a road surface type on which the vehicle travels, a contact state between the tire of the vehicle and the road surface, and a friction coefficient between the tire and the road surface. The road sense feedback adjustment device for steer-by-wire provided in the embodiments of the present application can also be used to identify the road surface type on which the vehicle travels by using the tire parameter. The contact state between the tire of the vehicle and the road surface is determined according to the vehicle body parameter. The friction coefficient between the tire of the vehicle and the road surface is calculated according to the motor parameter.

[0108] In a possible implementation, the road sense feedback adjustment device for steer-by-wire provided in the embodiments of the present application can also be used to collect a current driving condition parameter of the vehicle in real time. The driving condition parameter is mapped to the road sense curve to obtain the steering wheel torque gradient.

[0109] In a possible implementation, the road sense feedback adjustment device for steer-by-wire provided in the embodiments of the present application can also be used to input the driving data into a pre-trained long short-term memory network to output steering habit data of the driver. The feedback strength of the vehicle to the driver is adjusted according to the steering habit data.

[0110] In a possible implementation, the road sense feedback adjustment device for steer-by-wire provided in the embodiments of the present application can also be used to adjust the network weight of the long short-term memory network by using the collected driving data of the driver according to a preset adjustment period, so that the long short-term memory network matches the steering habit of the driver.

[0111] In a possible implementation, the road sense feedback data includes a steering wheel feedback damping of the vehicle. The road sense feedback adjustment device for steer-by-wire provided in the embodiments of the present application can also be used to determine the steering wheel feedback damping of the vehicle according to the steering wheel torque gradient and the feedback strength. The driver is physically fed back by using a preset basic steering resistance, the steering wheel feedback damping of the vehicle, and a feedback vibration mode. The basic steering resistance is used to simulate the natural return torque of the steering system of the vehicle.

[0112] It should be noted that all related contents of each step involved in the method embodiments described above can be cited to the function description of the corresponding function module, which will not be described here.

[0113] The road sense feedback adjustment device for steer-by-wire 300 provided in the embodiments of the present application is used to perform the above Figure 2The road feel feedback adjustment method of the wire-controlled steer can therefore achieve the same effect as the road feel feedback adjustment method of the wire-controlled steer described above.

[0114] An embodiment of the present application further provides a steer-by-wire road feel feedback adjustment device, which can execute the steer-by-wire road feel feedback adjustment method and related steps in the above-mentioned method embodiment.

[0115] An embodiment of the present application further provides a computer-readable storage medium having instructions stored thereon, which, when executed, execute the road feel feedback adjustment method and related steps of the wire-controlled steering in the above-mentioned method embodiment.

[0116] An embodiment of the present application further provides a computer program product, which, when executed on a computer, enables the computer to execute the road feel feedback adjustment method and related steps for steer-by-wire in the above-mentioned method embodiment.

[0117] In some embodiments, the methods described herein may be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or on other non-transitory media or articles of manufacture.

[0118] The present application also provides a road feel feedback adjustment system 100 for steer-by-wire. Figure 5 As shown, the steer-by-wire road feedback adjustment system 100 includes at least one processor 501 and at least one interface circuit 502 .

[0119] As an example, when the steer-by-wire road feel feedback adjustment system 100 includes a processor and an interface circuit, the processor may be Figure 5 The processor 501 shown in the solid line frame (or the processor 501 shown in the dotted line frame) may be Figure 5 The interface circuit 502 shown in the solid line frame (or the interface circuit 502 shown in the dotted line frame) is shown in the solid line frame. When the steer-by-wire road feel feedback adjustment system 100 includes two processors and two interface circuits, the two processors include Figure 5 The processor 501 shown in the solid line frame and the processor 501 shown in the dotted line frame, the two interface circuits include Figure 5 The interface circuit 502 shown in the solid line frame and the interface circuit 502 shown in the dotted line frame are not limited to this.

[0120] The processor 501 and the interface circuit 502 can be interconnected via a line. For example, the interface circuit 502 can be used to receive signals. For another example, the interface circuit 502 can be used to send signals to other devices (such as the processor 501). For example, the interface circuit 502 can read computer instructions stored in the memory and send the computer instructions to the processor 501. The processor 501 executes the instructions and, in conjunction with the input and output devices, implements the various steps in the above embodiments, such as implementing Figure 2 Of course, the road feel feedback adjustment system for steer-by-wire can also include other discrete components, which are not specifically limited in this embodiment of the present application.

[0121] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0125] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the contributing part or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0126] The above content is only a specific embodiment of this application, but the scope of protection of this application is not limited to this. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for adjusting road feel feedback in a steer-by-wire system, characterized in that: The method comprises: Obtain the vehicle's road feeling parameters and the driver's driving data; Dynamically adjusting the steering wheel torque gradient of the vehicle according to the road feel parameter; determining a feedback intensity of the vehicle based on the driving data; The steering wheel torque gradient and the feedback strength are used to adjust the rotation parameters of the steering wheel, and physical feedback is provided to the driver.

2. The method according to claim 1, characterized in that The road feeling parameters include tire parameters, vehicle body parameters, and motor parameters; dynamically adjusting the steering wheel torque gradient of the vehicle according to the road feeling parameters includes: determining interaction parameters between the vehicle and a road surface based on the tire parameters, the vehicle body parameters, and the motor parameters; Optimizing a preset road feel curve based on the interaction parameter to obtain an optimized road feel curve; The optimized road feel curve is used to adjust the steering wheel torque gradient of the vehicle.

3. The method according to claim 2, characterized in that The interaction parameters include: the type of road surface on which the vehicle is traveling, the contact state between the tires of the vehicle and the road surface, and the friction coefficient between the tires and the road surface; determining the interaction parameters between the vehicle and the road surface based on the tire parameters, the vehicle body parameters, and the motor parameters includes: identifying the type of road surface on which the vehicle is traveling using the tire parameters; determining a contact state between the tires of the vehicle and the road surface according to the vehicle body parameters; The friction coefficient between the tire of the vehicle and the road surface is calculated based on the motor parameters.

4. The method according to claim 2, characterized in that The adjusting the steering wheel torque gradient of the vehicle by using the optimized road feel curve includes: Real-time collection of current driving condition parameters of the vehicle; The driving condition parameters are mapped to the road feel curve to obtain the steering wheel torque gradient.

5. The method according to claim 1, wherein Determining the feedback intensity of the vehicle according to the driving data includes: Inputting the driving data into a pre-trained long short-term memory network and outputting the driver's steering habit data; The feedback intensity of the vehicle to the driver is adjusted according to the steering habit data.

6. The method according to claim 5, characterized in that The method further comprises: According to a preset adjustment cycle, the network weights of the long short-term memory network are adjusted using the collected driving data of the driver, so that the long short-term memory network matches the steering habits of the driver.

7. The method according to claim 1, characterized in that The adjusting the steering wheel rotation parameters by using the steering wheel torque gradient and the feedback strength to provide physical feedback to the driver includes: determining a steering wheel feedback damping of the vehicle according to the steering wheel torque gradient and the feedback strength; The physical feedback is provided to the driver using a preset basic steering resistance, the vehicle's steering wheel feedback damping, and a feedback vibration pattern; the basic steering resistance is used to simulate the natural return torque of the vehicle's steering system.

8. A road feel feedback adjustment device for steer-by-wire, characterized in that: The device comprises: An acquisition module is used to obtain the vehicle's road feeling parameters and the driver's driving data; an adjustment module, configured to dynamically adjust a steering wheel torque gradient of the vehicle according to the road feel parameter; a determination module, configured to determine a feedback intensity of the vehicle based on the driving data; The adjustment module is further configured to adjust the rotation parameters of the steering wheel using the steering wheel torque gradient and the feedback intensity, thereby providing physical feedback to the driver.

9. A road feel feedback adjustment device for steer-by-wire, characterized in that: The road feel feedback adjustment device for steer-by-wire includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, and the processor executing the machine-executable instructions to implement the road feel feedback adjustment method for steer-by-wire according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the road feel feedback adjustment method for steer-by-wire according to any one of claims 1 to 7.

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