Compensation of uneven vehicle steering

Abstract

Enabling machine learning based steering torque unevenness compensation for a vehicle. For example, a system can include a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory, where the computer executable components include a machine learning component that generates a steering unevenness model based on machine learning applied to past steering data representing a position of a steering wheel of a vehicle and a steering ratio, and a torque compensation component that determines a torque to apply to the steering wheel using current position data representing a current position of the steering wheel and the steering unevenness model, the torque to apply to the steering wheel configured to offset a steering unevenness at the current position.

Description

Technical Field

[0001] The disclosed subject matter relates to vehicle steering systems, and more specifically, to machine learning-based compensation for uneven steering torque in vehicles (e.g., passenger cars). Background Technology

[0002] Vehicle steering columns often utilize one or more universal joints. These universal joints (U-joints) cause uneven angular velocities between the steering wheel and the steering gearbox or rack, resulting in uneven torque observed at the steering wheel. This occurs due to the difference in angle and corresponding phase associated with the rotation of the universal joint. Additionally, steering wheel designs that incorporate steering wheel airbags, spokes, levers, paddle shifters, user accessories, or buttons / controls often cause the center of gravity to deviate from the steering wheel's central axis, which can also lead to uneven torque observed at the steering wheel. This uneven torque is undesirable because it causes a non-linear, indirect steering feel.

[0003] The background information above regarding steering torque unevenness compensation is intended only to provide a contextual overview of some current issues, and is not exhaustive. Further contextual information will become more apparent after reading the detailed description below. Summary of the Invention

[0004] The following summary is provided to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or essential elements or to depict any scope of a particular embodiment or any scope of the claims. Its sole purpose is to present the concepts in a simplified form as a prelude to the more detailed description that follows. In one or more embodiments described herein, systems, apparatuses, computer-implemented methods, and / or computer program articles facilitate compensation for uneven steering torque.

[0005] As mentioned above, steering torque non-uniformity compensation can be improved in various ways, and various implementation methods are described herein for this purpose and / or other purposes.

[0006] According to one embodiment, the system may include a memory storing computer-executable components and a processor executing the computer-executable components stored in the memory, wherein the computer-executable components may include: a machine learning component that generates a steering non-uniformity model based on machine learning applied to past steering data representing the position and steering ratio of the vehicle's steering wheel; and a torque compensation component that determines a torque to be applied to the steering wheel using current position data representing the current position of the steering wheel and the steering non-uniformity model, the torque to be applied to the steering wheel being configured to counteract steering non-uniformity at the current position.

[0007] According to another embodiment, the non-transitory machine-readable medium may include executable instructions that, when executed by a processor, facilitate the execution of operations including: generating a steering non-uniformity model based on machine learning applied to past steering data representing the position and steering ratio of a vehicle steering wheel; and determining a torque to be applied to the steering wheel using current position data representing the current position of the steering wheel and the steering non-uniformity model, the torque to be applied to the steering wheel being configured to counteract steering non-uniformity at the current position.

[0008] According to another embodiment, the method may include: generating a steering non-uniformity model by means of a processor based on machine learning applied to past steering data representing the position and steering ratio of a vehicle steering wheel; and using current position data representing the current position of the steering wheel and the steering non-uniformity model, determining by means of a torque to be applied to the steering wheel, the torque to be applied to the steering wheel being configured to counteract steering non-uniformity at the current position. Attached Figure Description

[0009] Figure 1 A block diagram of an exemplary system according to one or more embodiments described herein is illustrated.

[0010] Figure 2 A block diagram of an exemplary system according to one or more embodiments described herein is illustrated.

[0011] Figure 3 A block diagram of an exemplary system according to one or more embodiments described herein is illustrated.

[0012] Figure 4 This is a graph illustrating an exemplary uneven steering torque according to one or more embodiments described herein.

[0013] Figure 5 This is a graph illustrating exemplary steering torque unevenness and associated compensation according to one or more embodiments described herein.

[0014] Figure 6 This is an exemplary flowchart of a process associated with steering torque non-uniformity compensation according to one or more embodiments described herein.

[0015] Figure 7 The illustration shows a flowchart of a process associated with steering torque non-uniformity compensation according to one or more embodiments described herein.

[0016] Figure 8 The illustration shows a flowchart of a process associated with steering torque non-uniformity compensation according to one or more embodiments described herein.

[0017] Figure 9It is an exemplary non-limiting computing environment in which one or more embodiments described herein can be implemented.

[0018] Figure 10 This is an exemplary, non-limiting networking environment in which one or more embodiments described herein may be implemented. Detailed Implementation

[0019] The following detailed description is illustrative only and is not intended to limit the implementation methods and / or their application or use. Furthermore, it is not intended to be construed as being bound by any express or implied information provided in the foregoing background or summary or detailed description sections.

[0020] One or more embodiments will now be described with reference to the accompanying drawings, wherein like reference numerals are always used to refer to like elements. In the following description, numerous specific details are set forth for purposes of explanation in order to provide a more thorough understanding of the one or more embodiments. However, it will be apparent that the one or more embodiments may be practiced without these specific details in various circumstances.

[0021] It should be understood that when an element is referred to as "coupled" to another element, it can describe one or more different types of coupling, including but not limited to: chemical coupling, communication coupling, capacitive coupling, electrical coupling, electromagnetic coupling, inductive coupling, operational coupling, optical coupling, physical coupling, thermal coupling, and / or other types of coupling. As mentioned herein, an "entity" can include a person, client, user, computing device, software application, agent, machine learning model, artificial intelligence, and / or another entity. It should be understood that such an entity can facilitate the implementation of the subject matter disclosed according to one or more embodiments described herein.

[0022] Now go to Figure 1 The illustration depicts an exemplary, non-limiting system 102 according to one or more embodiments of this document. System 102 may include computerized tools configured to perform various operations related to steering torque non-uniformity compensation. System 102 may include one or more of a variety of components, such as memory 104, processor 106, bus 108, machine learning (ML) component 110, torque compensation component 112, one or more sensors 114, steering component 116, and / or user interface (UI) component 118.

[0023] In various embodiments, one or more of the memory 104, processor 106, bus 108, ML component 110, torque compensation component 112, sensor 114, steering component 116 and / or UI component 118 are communicatively or operably coupled to each other (e.g., via bus or wireless network) to perform one or more functions of system 102.

[0024] It should be noted that steering component 116 may include one or more of a variety of steering components, such as a steering wheel (or steering yoke or other suitable vehicle steering mechanism), steering column, steering rack, steering gearbox / transmission, hydraulic power steering pump, electro-hydraulic power steering pump, electric steering motor in the drive of a steer-by-wire system, electronic power steering (EPAS), adjustable mass (e.g., received in or on the steering wheel), actuator (e.g., of the steering wheel or steering motor / gearbox), or other suitable steering components. Similarly, sensor 114 may include one or more of a variety of sensors, such as a steering column position sensor, steering wheel angle sensor (e.g., relative angle or rotation angle), steering wheel position sensor (e.g., telescopic position or tilt position), steer-by-wire system sensor, EPAS motor sensor, steering wheel actuator motor sensor, or other suitable sensor. In one embodiment, sensor 114 may include a position sensor that determines the position of the vehicle's steering wheel and / or steering ratio (e.g., the ratio between driver input at the steering wheel and changes in steering angle at the wheels (e.g., tires)). It should be noted that the steering system described herein may include a forward steering system, a rearward steering system, or a combination of steering systems or orientations. It should also be noted that the embodiments described herein can be implemented in one or more of a variety of vehicles, such as passenger cars, trucks, crossovers or SUVs, commercial vehicles, autonomous vehicles, internal combustion engine vehicles, electric vehicles, hybrid vehicles, fuel cell vehicles, or other suitable vehicles. Such vehicles may include four-wheeled motor vehicles, or may additionally / alternatively include three-wheeled, two-wheeled, or even unicycles.

[0025] According to one embodiment, the ML component 110 can generate a steering non-uniformity model (e.g., a steering wheel model and / or steering column model) based on machine learning applied to past steering data representing the position and steering ratio of the vehicle's steering wheel (and / or steering column). In various embodiments, the ML component can generate a non-uniformity model applicable to a variety of different vehicles or similar vehicles. In this respect, such a model can be generalized as universally applicable or customized for a specific vehicle and associated steering system. Note that the steering non-uniformity model can be configured to model the relationship between the vehicle's steering wheel / column angle, position, and / or ratio and the corresponding steering torque manifested at the steering wheel or column. In one embodiment, the steering wheel may include an adjustable steering wheel, and past steering column data may further represent the past adjustment position of the steering wheel (e.g., steering wheel angle / tilt, steering wheel extension / retraction of a telescopic steering wheel). It should also be noted that the steering ratio may vary during steering wheel rotation (e.g., due to the rotational speed and angle of the U-joint in the steering system).

[0026] According to one embodiment, using current position data representing the current position of the steering wheel (or other components such as the steering column, steering box, or steering motor) (e.g., determined using sensor 114), and a steering unevenness model, torque compensation component 112 can determine the torque to be applied to / via steering component 116 (e.g., steering wheel or steering column, and / or steering motor or actuator), which is configured to counteract (e.g., resist) steering unevenness at the current position (e.g., steering wheel, steering motor, or actuator). In this respect, by counteracting steering unevenness, a more linear and direct feel can be achieved in the steering wheel. In various embodiments, the vehicle may include electronic power steering. In this respect, torque may be applied via an electric motor of the EPAS. In an additional embodiment, the vehicle may include a drive for steer-by-wire. In this respect, the torque may be applied via an electro-feedback motor of the steering wheel (e.g., the drive of the steer-by-wire system). In another embodiment, torque may be applied via an actuator of an electro-hydraulic power steering system. It is worth noting that the ML component 110 can learn to compensate for steering inconsistencies in any of the aforementioned steering system types or other suitable steering system types. In an additional embodiment, a lookup table of torque values ​​described herein can be utilized. Such a lookup table can be predefined or determined using the machine learning described herein.

[0027] According to one embodiment, the steering component 116 may include a steering wheel, which may include an adjustable mass (e.g., inside or outside the steering wheel). In this respect, the adjustable mass may be received in the steering wheel and may be configured to vary the center of gravity of the steering wheel (e.g., when the adjustable mass moves / adjusts). For example, the center of gravity of the steering wheel may be adjusted by moving the adjustable mass relative to the steering wheel. The foregoing can be used to compensate for, for example, a steering wheel in which the center of gravity does not meet a threshold match with the center axis of the steering wheel. Furthermore, in this respect, the steering wheel may include an actuator that, based on torque (e.g., determined by the ML component 110 and / or torque compensation component 112), moves the adjustable mass to adjust the moment of inertia and / or the center of gravity of the steering wheel to compensate for a threshold match of the center of gravity of the steering wheel corresponding to the center axis of the steering wheel. In some embodiments, the adjustable mass may be configured to offset the center of gravity of the steering wheel to (e.g., when not manipulated by the user) return the steering wheel to a centered position (e.g., a position that would cause the associated vehicle to move forward or backward in a straight line). In various embodiments, the adjustable mass may be activated in response to the determination of a steering system or component failure (e.g., via system 102). For example, the adjustable mass may be activated in response to an EPAS motor or sensor failure, a pulley actuator motor drive failure, an electro-hydraulic motor or sensor failure, or other identifiable failure. In this regard, the adjustable mass may include a backup system to mitigate the effects of uneven steering; however, in additional embodiments, the adjustable mass may include a primary system.

[0028] According to one embodiment, the torque applied herein (e.g., determined by the ML component 110 and / or torque compensation component 112) may be further based on setting information representing a driving mode setting. For example, such a driving mode may correspond to one or more of a variety of steering modes or "feels" for vehicle steering as described herein. This steering mode may alter the level of steering assist, steering feedback, steering ratio, steering weight or resistance, steering rebound force, or other suitable steering adjustments. In this regard, such driving mode settings may be received via a UI component 118. Such a UI component 118 may include one or more of the associated vehicle's screen, associated vehicle's buttons, knobs or switches, associated vehicle's voice command system, or another suitable UI component or system. In this regard, the steering model herein may be further based on such driving mode settings, and the ML component 110 and / or torque compensation component 112 may adjust the torque level based on the driving mode to apply to counter-steering unevenness. In this regard, the level / amount of unevenness compensation (e.g., torque) may be increased or decreased depending on the driving or steering mode.

[0029] The various implementations described herein may employ artificial intelligence or machine learning systems and techniques to facilitate the learning of user behavior, context-based scenarios, preferences, etc., in order to promote automated actions taken with high confidence. Utility-based analytics may be used to compare the benefits of taking action relative to the costs of taking incorrect action. Probabilistic or statistical-based analyses may be employed in conjunction with the foregoing and / or the following.

[0030] It should be noted that the system and / or associated controllers, servers or machine learning components described herein may include artificial intelligence components that can employ artificial intelligence (AI) models and / or ML or a ML model, which can learn to perform the functions described above or below (e.g., by training using historical training data and / or feedback data).

[0031] In some implementations, the ML component 110 may include an AI and / or ML model that can be trained (e.g., via supervised and / or unsupervised techniques) to perform the functions described above or below using historical training data, which includes various contextual conditions corresponding to various vehicle steering non-uniformity compensation operations. In this example, such an AI and / or ML model may be further learned (e.g., via supervised and / or unsupervised techniques) to perform the functions described above or below using training data that includes feedback data, which may be collected and / or stored (e.g., in memory) by the ML component 110. In this example, such feedback data may include various instructions described above / below that respond to observed / stored context-based information input over time into a system, such as this one.

[0032] The AI / ML component here can initiate one or more operations associated with a defined level of confidence determined based on usage information (e.g., feedback data). For example, based on learning to perform the aforementioned functions using the feedback data, performance information, and / or past performance information, the ML component 110 here can initiate operations associated with determining various thresholds (e.g., a similarity threshold between the center of gravity / center of gravity and the steering wheel center axis, a motion pattern threshold, an input pattern threshold, a similarity threshold, an authentication signal threshold, an audio frequency threshold, or other suitable thresholds).

[0033] In one embodiment, the ML component 110 may perform a utility-based analysis that compares the cost of initiating the aforementioned operations with the benefits. In this embodiment, the ML component 110 may use one or more additional contextual conditions to determine various thresholds.

[0034] To facilitate the aforementioned functions, the ML component 110 described herein may perform classification, association, inference, and / or representation related to artificial intelligence principles. For example, the ML component 110 may employ an automatic classification system and / or automatic classification. In one example, the ML component 110 may employ probabilistic and / or statistical analysis (e.g., taking cost and utility into account in the analysis) to learn and / or generate inferences. The ML component 110 may employ any suitable machine learning-based, statistical, and / or probabilistic techniques. For example, the ML component 110 may employ expert systems, fuzzy logic, support vector machines (SVMs), hidden Markov models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other nonlinear training techniques, data fusion, utility-based analysis systems, systems employing Bayesian models, and / or the like. In another example, the ML component 110 may perform a set of machine learning computations. For example, ML component 110 may perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least squares machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and / or a different set of machine learning computations.

[0035] In various implementations, system 102 may include the hardware required to implement a variety of communication protocols, such as infrared (“IR”), shortwave transmission, near field communication (“NFC”), Bluetooth, Wi-Fi, Long Term Evolution (“LTE”), 3G, 4G, 5G, 6G, Global System for Mobile Communications (“GSM”), Code Division Multiple Access (“CDMA”), satellite, visual cues, radio waves, etc.

[0036] refer to Figure 2An exemplary non-limiting system 200 according to one or more embodiments herein is illustrated. According to one embodiment, system 200 may be used in conjunction with EPAS steering (e.g., via EPAS motor 216). Note that system 200 may be similar to system 102. System 200 may include one or more of sensors 202 (e.g., steering column position sensor 204 and / or steering wheel angle sensor 206), a non-uniformity compensation controller 208, and / or EPAS motor 216. Note that the non-uniformity compensation controller 208 may receive data from sensors 202 and / or EPAS motor 216. For example, the non-uniformity compensation controller 208 may receive steering column position data from steering column position sensor 204 and steering wheel angle data from steering wheel angle sensor 206. In various embodiments, the non-uniformity compensation controller 208 may additionally / alternatively receive pinion angle or motor angle data, pinion angle velocity data or motor angle velocity data, and / or motor torque data from EPAS motor 216. In this regard, the non-uniformity compensation controller 208 may utilize the steering column model 210 (e.g., generated using machine learning here) to generate a non-uniformity determination 212. This non-uniformity determination 212 may include determining the non-uniformity exhibited by the steering wheel at a given position and angle. Based on the non-uniformity determination 212, the non-uniformity compensation controller 208 may determine an increase in torque 214 applied via the EPAS motor 216. In this regard, the non-uniformity compensation controller 208 may induce the EPAS motor 216 to compensate for the non-uniformity determined at a given position and angle (e.g., by an appropriate amount of torque applied via the EPAS motor 216). Note that the non-uniformity compensation controller 208 can quickly determine the increase in torque 214, thereby maintaining uniform steering in real time during constant movement and / or rotation of the steering wheel.

[0037] Now go to Figure 3This document illustrates an exemplary non-limiting system 300 according to one or more embodiments thereof. According to one embodiment, system 300 may be utilized by steering by steer-by-wire (e.g., using a steering wheel (e.g., including a steering wheel actuator motor and a steering box communicatively coupled). Note that the steering wheel actuator motor may be received in the steering wheel to provide steering feedback and / or torque via the steering wheel. In steer-by-wire, non-uniformity may be caused by the center of gravity of the steering wheel. Note that system 300 may be similar to system 102 and / or system 200. System 300 may include one or more of sensors 302 (e.g., steering wheel position sensor 304 and / or steering wheel angle sensor 306), a non-uniformity compensation controller 308, and / or a steering wheel actuator motor 316. Note that… The uniformity compensation controller 308 can receive data from sensor 302 and / or steering wheel actuator motor 316. For example, the non-uniformity compensation controller 308 can receive steering wheel position data from steering wheel position sensor 304 and steering wheel angle data from steering wheel angle sensor 306. The non-uniformity compensation controller 308 can additionally / alternatively receive steering shaft angle data or motor angle data, steering shaft speed data or motor speed data, and / or motor torque data from steering wheel actuator motor 316. In this regard, the non-uniformity compensation controller 308 can utilize steering wheel model 310 to generate a non-uniformity determination 312. Based on the non-uniformity determination 312... 12. The non-uniformity compensation controller 308 can determine the torque increase 314 to be applied to the steering wheel actuator motor 316. Note that in system 300, non-uniformity compensation is applied only via the steering wheel actuator motor 316 (e.g., to compensate for the associated steering wheel center of gravity). To compensate for non-uniformity, the non-uniformity compensation controller 308 can generate a steering wheel model 310 (e.g., using machine learning). In one embodiment, the steering wheel model 310 (e.g., generated using machine learning herein) can address steering wheel tilt, steering wheel extension / retraction (e.g., position), steering wheel rotation position, etc. Note that the steering wheel model 310 can be adjusted for each steering wheel (e.g., to...). This addresses variations between vehicle models, or even manufacturing differences between identical steering wheels. Furthermore, steering wheel model 310 (or steering wheel model 210) can address differences in the center of gravity of the steering wheel due to user customization (e.g., replacement of the steering wheel or steering wheel accessories, such as a steering wheel cover). Further note that the non-uniformity compensation controller 308 can quickly determine the torque increase 314, thereby maintaining uniform steering in real time during constant movement and / or rotation of the steering wheel. In this regard, the non-uniformity compensation controller 308 can cause the steering wheel actuator motor 316 to compensate for non-uniformities determined at a given position and angle (e.g., by applying an appropriate amount of torque via the steering wheel actuator motor 316).

[0038] Figure 4An exemplary non-limiting graph 400 is illustrated according to one or more embodiments described herein. Graph 400 shows non-uniform lines 402 and 404. Such non-uniformity is observed in steering wheel torque vs. steering wheel angle. The torque variations observed in non-uniform lines 402 and 404 can result in inconsistent, non-linear steering at the associated steering wheel position. Figure 5 An exemplary non-limiting graph 500 is illustrated according to one or more embodiments described herein. In this respect, graph 500 illustrates target lines 502 and 504. Target lines 502 and 504 may represent a smooth slope (e.g., a straight line or a smoother curve) of steering wheel torque vs. steering wheel angle. In this respect, systems 102, 200, and / or 300 may be configured to compensate for uneven steering wheel torque in order to generate a consistent linear steering feel. For example, systems 102, 200, and / or 300 may generate a steering unevenness model and determine the torque to be applied to the steering wheel (or another suitable component described herein) to counteract unevenness (e.g., to resist deviations of unevenness line 402 or unevenness line 404 from the corresponding target lines).

[0039] Turn now Figure 6The diagram illustrates a flowchart of process 600 related to steering torque non-uniformity compensation according to one or more embodiments described herein. At 602, past steering data may be determined (e.g., using ML component 110). At 604, ML component 110 may generate a steering non-uniformity model (e.g., using a steering wheel model and / or steering column model generated using machine learning applied herein to past steering data). At 606, ML component 110 may determine current steering data (e.g., current position, current angle, current speed, current torque) (e.g., using one or more sensors 114). At 608, ML component 110 may determine non-uniformities at the steering wheel, steering column, steering rack, or steering box based on the current steering data. At 610, torque compensation component 112 may determine, based on the current steering data and the non-uniformity model, a torque to be applied to the steering wheel (or another suitable component herein) configured to counteract steering non-uniformities at the current position / angle. At 612, any faults in the steering system may be identified and / or recognized. At 614, if no fault exists (e.g., a fault that could interfere with steering torque compensation), a determined torque can be applied at 616. If such a fault does exist, the determined torque will not be applied. Instead, an alternative compensation mechanism can be enabled. In this regard, an adjustable mass can be enabled at 618. For example, the adjustable mass can be enabled in response to an EPAS motor or sensor fault, a wheel drive actuator motor fault, an electro-hydraulic motor or sensor fault, or other suitable fault. At 620, the adjustable mass can be adjusted (e.g., based on non-uniformity).

[0040] Figure 7 A flowchart illustrating a process 700 associated with steering torque non-uniformity compensation according to one or more embodiments described herein is shown. At 702, process 700 may include generating a steering non-uniformity model based on machine learning applied to past steering data representing the position and steering ratio of the vehicle's steering wheel. At 704, process 700 may include determining, using current position data representing the current position of the steering wheel and the steering non-uniformity model, a torque to be applied to the steering wheel, configured to counteract steering non-uniformity at the current position.

[0041] Figure 8A flowchart illustrating a process 800 associated with steering torque non-uniformity compensation according to one or more embodiments described herein is shown. At 802, process 800 may include generating a steering non-uniformity model by means of a processor based on machine learning, the machine learning being applied to past steering data representing the position of the vehicle's steering wheel and the steering ratio. At 802, process 800 may include using current position data representing the current position of the steering wheel and the steering non-uniformity model, the means determining a torque to be applied to the steering wheel, the torque to be applied to the steering wheel being configured to counteract steering non-uniformity at the current position.

[0042] The system described herein can be coupled (e.g., communication ground, electronic ground, operational ground, optical ground, etc.) to one or more local or remote (e.g., external) systems, sources, and / or devices (e.g., electronic control systems (ECUs), classical and / or quantum computing devices, communication devices, etc.). For example, using data cables (e.g., High Definition Multimedia Interface (HDMI), Recommended Standard (RS), Ethernet cables, etc.) and / or one or more wired networks described below, system 102 (or other systems, controllers, processors, etc.) can be coupled (e.g., communication ground, electronic ground, operational ground, optical ground, etc.) to one or more local or remote (e.g., external) systems, sources, and / or devices.

[0043] In some embodiments, the system herein can be coupled (e.g., communicative ground, electronic ground, operational ground, optical ground, etc.) to one or more local or remote (e.g., external) systems, sources, and / or devices (e.g., electronic control units (ECUs), classical and / or quantum computing devices, communication devices, etc.) via a network. In these embodiments, such a network may include one or more wired and / or wireless networks, including but not limited to cellular networks, wide area networks (WANs) (e.g., the Internet), and / or local area network (LANs). For example, system 102 can communicate with one or more local or remote (e.g., external) systems, sources, and / or devices, such as computing devices using such networks, which may include virtually any desired wired or wireless technology, including but not limited to: Power Line Ethernet, Wireless Fidelity (Wi-Fi). Fiber optic communication, Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), WiMAX, Enhanced General Packet Radio Service (Enhanced GPRS), 3GPP Long Term Evolution (LTE), 3GPP2 Ultra Mobile Broadband (UMB), High-Speed ​​Packet Access (HSPA), Zigbee and other 802.XX wireless technologies and / or traditional telecommunications technologies, Session Initiation Protocol (SIP). RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6 over low-power wireless LAN), Z-Wave, ANT, ultra-wideband (UWB) standard protocol, and / or other proprietary and non-proprietary communication protocols. In this example, system 102 may therefore include hardware such as a central processing unit (CPU), transceiver, decoder, and antenna (e.g., ultra-wideband (UWB) antenna, Bluetooth). Low-energy (BLE) antennas, etc.), quantum hardware, quantum processors, etc.), software (e.g., a set of threads, a set of processes, software in execution, quantum pulse scheduling, quantum circuits, quantum gates, etc.), or a combination of hardware and software that facilitates communication of information between the system here and remote (e.g., external) systems, sources and / or devices (e.g., computing and / or communication devices, such as, for example, smartphones, smartwatches, wireless earbuds, etc.).

[0044] The systems described herein may include one or more computer- and / or machine-readable, writable, and / or executable components and / or instructions that, when executed by a processor (e.g., processor 106, which may include a classical processor, a quantum processor, etc.), facilitate the execution of operations defined by such components and / or instructions. Furthermore, in many embodiments, any component associated with the systems herein, as described herein with or without reference to the various accompanying drawings, may include one or more computer- and / or machine-readable, writable, and / or executable components and / or instructions that, when executed by a processor, facilitate the execution of operations defined by such components and / or instructions. Therefore, according to many embodiments, the systems herein and / or any components disclosed herein and associated therewith may employ a processor (e.g., processor 106) to execute such computer- and / or machine-readable, writable, and / or executable components and / or instructions to facilitate the execution of one or more operations described herein with reference to the systems herein and / or any such components associated therewith.

[0045] The systems described herein may include any type of system, apparatus, machine, facility, component, and / or instrumentation, including a processor and / or capable of communicating with one or more local or remote electronic systems and / or one or more local or remote devices via wired and / or wireless networks. All such implementations are contemplated. For example, a system (e.g., system 302 or any other system or apparatus described herein) may include a computing device, a general-purpose computer, a special-purpose computer, an in-vehicle computing device, a communication device, an in-vehicle communication device, a server device, a quantum computing device (e.g., a quantum computer), a tablet computing device, a handheld device, a server-type computer and / or a database, a laptop computer, a notebook computer, a desktop computer, a mobile phone, a smartphone, consumer appliances and / or instrumentation, industrial and / or commercial devices, a digital assistant, a telephone supporting multimedia internet, a multimedia player, and / or other types of devices.

[0046] In order to provide additional context for the various implementations described herein, Figure 9 The following discussion is intended to provide a brief overview of a suitable computing environment 900 in which various implementations of the embodiments described herein can be carried out. Although the implementations have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the implementations may also be combined with other program modules and / or as a combination of hardware and software.

[0047] Generally, a program module contains routines, programs, components, data structures, etc., that perform specific tasks or implement specific abstract data types. Furthermore, those skilled in the art will understand that various methods can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each operatively coupled to one or more associated devices.

[0048] The embodiments illustrated in this document can also be practiced in a distributed computing environment, where certain tasks are performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can reside in both local and remote memory storage devices.

[0049] Computing devices often include various media, including computer-readable storage media, machine-readable storage media, and / or communication media, these terms being used differently herein. A computer-readable storage medium or a machine-readable storage medium can be any available storage medium accessible to a computer, and includes both volatile and non-volatile media, and both removable and non-removable media. By way of example and not limitation, a computer-readable storage medium or a machine-readable storage medium can be implemented using any method or technique for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0050] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other storage technologies, optical disc read-only memory (CDROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic tape cassettes, magnetic tape, disk storage or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible and / or non-transitory media that can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” used herein to describe storage, memory, or computer-readable media shall be understood to exclude the propagation of transient signals themselves as a modifier, and not to waive the rights of all standard storage, memory, or computer-readable media that not only propagate transient signals themselves.

[0051] Computer-readable storage media can be accessed by one or more local or remote computing devices, for example, via access requests, queries or other data retrieval protocols, to perform various operations on the information stored on the media.

[0052] Communication media often present computer-readable instructions, data structures, program modules, or other structured or unstructured data in data signals, such as modulated data signals, carrier waves, or other transmission mechanisms, and contain any information transmission or delivery medium. The term "modulated data signal" or multiple signals refers to signals having one or more characteristics set or altered in a manner that encodes information in one or more signals. By way of example and not limitation, communication media includes wired media, such as wired networks or direct wired connections, and wireless media, such as acoustic, RF, infrared, and other wireless media.

[0053] Refer again Figure 9An exemplary environment 900 for implementing various embodiments of the aspects described herein includes a computer 902, which includes a processing unit 904, a system memory 906, and a system bus 908. The system bus 908 couples system components, including, but not limited to, the system memory 906 to the processing unit 904. The processing unit 904 can be any of a variety of commercially available processors. Dual microprocessors and other multiprocessor architectures may also be used as the processing unit 904.

[0054] System bus 908 can be any of several types of bus architectures, which can be further interconnected to memory buses (with or without memory controllers), peripheral buses, and local buses using any of a variety of commercially available bus architectures. System memory 906 includes ROM 910 and RAM 912. The Basic Input / Output System (BIOS) can be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), EEPROM, etc., which covers basic routines that facilitate the transfer of information between components within computer 902, such as during startup. RAM 912 may also include high-speed RAM, such as static RAM for caching data.

[0055] Computer 902 also includes an internal hard disk drive (HDD) 914 (e.g., EIDE, SATA), one or more external storage devices 916 (e.g., floppy disk drive (FDD) 916, memory stick or flash drive reader, memory card reader, etc.), and an optical drive 920 (e.g., capable of reading from or writing to CD-ROMs, DVDs, BDs, etc.). Although the internal HDD 914 is illustrated as being located within computer 902, the internal HDD 914 may also be configured for external use within a suitable chassis (not shown). Alternatively, although not shown in environment 900, a solid-state drive (SSD) may be used to supplement or replace the HDD 914. The HDD 914, external storage device 916, and optical drive 920 may be connected to system bus 908 accordingly via HDD interface 924, external storage interface 926, and optical drive interface 928. The interface 924 for the external drive implementation may include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external driver connection technologies are within the scope of consideration for the implementations described herein.

[0056] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, etc. For computer 902, the drive and storage medium accommodate the storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to a corresponding type of storage device, those skilled in the art will understand that other types of computer-readable storage media, whether currently existing or developed in the future, may also be used in the exemplary operating environment. Furthermore, any such storage medium may contain computer-executable instructions for performing the methods described herein.

[0057] Numerous program modules can be stored in the drive and RAM 912, including an operating system 930, one or more application programs 932, other program modules 934, and program data 936. All or part of the operating system, application programs, modules, and / or data may also be cached in RAM 912. The systems and methods described herein can be implemented using a variety of commercially available operating systems or combinations of operating systems.

[0058] Computer 902 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment used for operating system 930, and the emulated hardware may optionally differ from that in the operating system 930. Figure 9 The hardware is illustrated in the diagram. In such an implementation, the operating system 930 may include one of a plurality of virtual machines (VMs) hosted on the computer 902. Furthermore, the operating system 930 may provide a runtime environment for the application 932, such as the Java Runtime Environment or the .NET Framework. A runtime environment is a constant execution environment that allows the application 932 to run on any operating system that includes a runtime environment. Similarly, the operating system 930 may support containers, and the application 932 may be in the form of a container, which is a lightweight, standalone, executable software package containing, for example, code, runtime, system tools, system libraries, and settings for the application.

[0059] Furthermore, the computer 902 may enable security modules, such as a Trusted Processing Module (TPM). For example, through the TPM, the bootloader hashes the next bootloader in time and waits for the result to match a security value before loading the next bootloader. This process can occur at any layer of the computer 902's code execution stack, for example, at the application execution level or the operating system (OS) kernel level, thereby achieving security at any code execution level.

[0060] Users can input commands and information into computer 902 via one or more wired / wireless input devices, such as keyboard 938, touchscreen 940, and pointing devices such as mouse 942. Other input devices (not shown) may include microphone, infrared (IR) remote control, radio frequency (RF) remote control or other remote control, joystick, virtual reality controller and / or virtual reality headset, gamepad, stylus, image input device (e.g., camera), gesture sensor input device, visual motion sensor input device, emotion or face detection device, biometric input device, such as fingerprint or iris scanner or the like. These and other input devices are often connected to processing unit 904 via input device interface 944, which may be coupled to system bus 908, but may also be connected via other interfaces, such as parallel port, IEEE 1394 serial port, game port, USB port, IR interface, etc. Interfaces, etc.

[0061] The monitor 946 or other types of display devices can also be connected to the system bus 908 via an interface such as the video adapter 948. In addition to the monitor 946, the computer often includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0062] Computer 902 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer 950, via wired and / or wireless communications. Remote computer 950 can be a workstation, server computer, router, personal computer, laptop computer, microprocessor-based entertainment application, peer-to-peer device, or other public network node, and often includes many or all of the elements described relative to computer 902, although for simplicity, only memory / storage device 952 is illustrated. The depicted logical connections include wired / wireless connections to a local area network (LAN) 954 and / or a larger network, such as a wide area network (WAN) 956. Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to global communication networks, such as the Internet.

[0063] When used in a LAN network environment, computer 902 can connect to local network 954 via a wired and / or wireless communication network interface or adapter 958. Adapter 958 facilitates wired or wireless communication to LAN 954 and may also include a wireless access point (AP) configured thereon for communicating with adapter 958 in wireless mode.

[0064] When used in a WAN network environment, computer 902 may include modem 960 or be connected to a communication server on WAN 956 via other means (such as via the Internet) for establishing communication on WAN 956. Modem 960 may be internal or external, wired or wireless, and may be connected to system bus 908 via input device interface 944. In a network environment, program modules depicted relative to computer 902 or parts thereof may be stored in remote memory / storage device 952. It should be understood that the network connection shown is an example, and other means of establishing communication links between computers may be used.

[0065] When used in a LAN or WAN network environment, computer 902 can access cloud storage systems or other network-based storage systems to supplement or replace external storage device 916 as described above. Generally, the connection between computer 902 and the cloud storage system can be established via LAN 954 or WAN 956, for example, by adapter 958 or modem 960. When computer 902 is connected to the associated cloud storage system, external storage interface 926 can manage the storage provided by the cloud storage system with the help of adapter 958 and / or modem 960, just as it would manage other types of external storage. For example, external storage interface 926 can be configured to provide access to cloud storage sources as if those sources were physically connected to computer 902.

[0066] Computer 902 can operatively communicate with any wireless device or entity operatively configured to communicate wirelessly, such as printers, scanners, desktop and / or laptop computers, portable data assistants, communication satellites, any device or location associated with a wirelessly detectable tag (e.g., a kiosk, newsstand, shelf, etc.), and telephones. This may include Wi-Fi and Wireless technology. Therefore, communication can be a predefined structure like traditional networks, or simply self-organizing communication between at least two devices.

[0067] Now for reference Figure 10 The diagram illustrates a schematic block diagram of a computing environment 1000 according to this specification. System 1000 includes one or more clients 1002 (e.g., computers, smartphones, tablets, cameras, PDAs). Client 1002 can be hardware and / or software (e.g., threads, processes, computing devices). For example, client 1002 may use specifications to contain cookies(s) and / or associated contextual information.

[0068] System 1000 also includes one or more servers 1004. Server 1004 can be hardware or hardware combined with software (e.g., threads, processes, computing devices). For example, server 1004 may accommodate threads to perform transformations of media items by employing aspects of this disclosure. One possible communication between client 1002 and server 1004 may be in the form of data packets adapted for transmission between two or more computer processes, wherein the data packets may contain encoded analytical headspaces and / or inputs. For example, the data packets may contain cookies and / or associated contextual information. System 1000 includes a communication framework 1006 (e.g., a global communication network such as the Internet) that can be adopted to facilitate communication between client 1002 and server 1004.

[0069] Communication can be facilitated via wired (including fiber optic) and / or wireless technologies. Client 1002 is operatively connected to one or more client data stores 1008, which can be used to store information locally on client 1002 (e.g., cookies and / or associated context information). Similarly, server 1004 is operatively connected to one or more server data stores 1010, which can be used to store information locally on server 1004.

[0070] In one exemplary implementation, client 1002 may transmit an encoded file (e.g., an encoded media item) to server 1004. Server 1004 may store the file, decode the file, or transmit the file to another client 1002. Note that, according to this disclosure, client 1002 may also transmit an uncompressed file to server 1004, and server 1004 may compress and / or transform the file. Similarly, server 1004 may encode information and transmit it to one or more clients 1002 via communication framework 1006.

[0071] The illustrated aspects of this disclosure can also be implemented in a distributed computing environment, where certain tasks are performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can reside in both local and remote memory storage devices.

[0072] The above description contains non-limiting examples of various embodiments. It is certainly not possible to describe every possible combination of components or methods in order to describe the disclosed subject matter, and those skilled in the art will recognize that further combinations and arrangements of various embodiments are possible. The disclosed subject matter is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

[0073] Regarding the various functions performed by the aforementioned components, devices, circuits, systems, etc., the terminology used to describe such components (including references to "device") is intended to also include, unless otherwise stated, any structure (e.g., functional equivalent) that performs the specified function of the described component, even if it is not structurally equivalent to the disclosed structure. Furthermore, while specific features of the disclosed subject matter may be disclosed only for one of several implementations, such features may be combined with one or more other features of other implementations, which may be necessary and advantageous for any given or particular application.

[0074] As used herein, the terms “exemplary” and / or “illustrative” are intended to mean used as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited to such examples. Furthermore, any aspect or design described herein as “exemplary” and / or “illustrative” is not necessarily to be construed as preferred or superior to other aspects or designs, nor does it imply the exclusion of equivalent structures and techniques known to those skilled in the art. Moreover, within the scope of the terms “includes,” “has,” “contains,” and other similar words used in the detailed description or claims, these terms are intended to be inclusive—as open transitional words similar to the term “comprising”—and do not exclude any additional or other elements.

[0075] As used herein, the term “or” is intended to mean inclusive rather than exclusive. For example, the phrase “A or B” is intended to include instances of A, B, and both A and B. Furthermore, the articles “a” and “an” used in this application and the appended claims should generally be interpreted as meaning “one or more” unless otherwise stated or clearly indicated from the context to be in the singular form.

[0076] The term "set" as used herein does not include an empty set, i.e., a set that contains no elements. Therefore, "set" as disclosed in this subject matter includes one or more elements or entities. Similarly, the term "group" as used herein refers to a collection of one or more entities.

[0077] The description of the illustrated embodiments disclosed herein, including those described in the abstract, is not intended to be exhaustive or to limit the disclosed embodiments to their precise forms. While specific embodiments and examples have been described herein for illustrative purposes, various modifications are possible within the scope of these embodiments and examples, as will be appreciated by those skilled in the art. In this regard, although the subject matter has been described herein in conjunction with various embodiments and corresponding drawings, it should be understood where applicable that other similar embodiments may be used, or modifications and additions may be made to the described embodiments to perform the same, similar, alternative, or substituted functions of the disclosed subject matter without departing from them. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but should be interpreted broadly and comprehensively in accordance with the following appended claims.

[0078] Other aspects of the invention are provided by the subject matter of the following provisions:

[0079] 1. The system, including:

[0080] Memory, the memory storing computer-executable components; and

[0081] A processor that executes computer-executable components stored in memory, wherein the computer-executable components include:

[0082] A machine learning component, based on machine learning applied to past steering data representing the position and steering ratio of the vehicle's steering wheel, generates a model of steering unevenness; and

[0083] A torque compensation unit, using current position data representing the current position of the steering wheel and a steering unevenness model, determines the torque to be applied to the steering wheel, the torque to be applied to the steering wheel being configured to counteract steering unevenness at the current position.

[0084] 2. The system according to any one of the preceding clauses, wherein the vehicle includes: electronic power steering, and wherein torque is applied via an electric motor for electronic power steering.

[0085] 3. The system according to any of the preceding clauses, wherein the vehicle includes: steer-by-wire drive.

[0086] 4. The system according to any of the foregoing clauses, wherein torque is applied via an electrical feedback motor of the steering wheel.

[0087] 5. The system according to any one of the preceding clauses further includes:

[0088] An adjustable mass is received in the steering wheel, wherein the adjustable mass is configured to change the center of gravity of the steering wheel.

[0089] 6. The system according to any one of the preceding clauses further includes:

[0090] An actuator, based on torque, moves an adjustable mass to adjust the moment of inertia of the steering wheel.

[0091] 7. The system according to any one of the foregoing clauses further includes:

[0092] A position sensor that determines the position of the vehicle's steering wheel and the steering ratio.

[0093] 8. The system according to any of the preceding clauses, wherein the steering non-uniformity model is configured to model the relationship between the vehicle's steering angle and the corresponding steering torque manifested at the steering wheel.

[0094] 9. The system according to any of the preceding clauses, wherein the steering wheel includes: an adjustable steering wheel, and wherein past steering column data further indicates a past adjustment position of the steering wheel.

[0095] 10. The system according to any of the preceding clauses, wherein the vehicle includes: electro-hydraulic power steering, and wherein torque is applied via an actuator of the electro-hydraulic power steering.

[0096] 11. The system of Clause 1 above and any combination of Systems 2-10 above.

[0097] 12. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate the execution of operations, including:

[0098] A model of uneven steering is generated based on machine learning applied to past steering data representing the vehicle's steering wheel position and steering ratio; and

[0099] Using current position data representing the current position of the steering wheel and a steering unevenness model, a torque to be applied to the steering wheel is determined, the torque to be applied to the steering wheel being configured to counteract steering unevenness at the current position.

[0100] 13. The non-transitory machine-readable medium according to any one of the preceding clauses, wherein the steering wheel includes: an adjustable steering wheel, and wherein the past steering column data further indicates the past adjustment position of the steering wheel.

[0101] 14. The non-transitory machine-readable medium according to any one of the preceding clauses, wherein the vehicle includes: a steer-by-wire system drive.

[0102] 15. The non-transitory machine-readable medium according to any one of the preceding clauses, wherein torque driven by the steer-by-wire system is applied via an electrical feedback motor of the steering wheel.

[0103] 16. The non-transitory machine-readable medium according to any of the preceding clauses, wherein the operation further includes:

[0104] In response to the determination of a fault associated with a component driven by the steer-by-wire system, an adjustment signal is generated, the adjustment signal being configured to adjust an adjustable mass received in the steering wheel via an actuator, the actuator moving the adjustable mass based on torque to adjust the moment of inertia of the steering wheel, wherein the adjustable mass is configured to change the center of mass of the steering wheel.

[0105] 17. The non-transitory machine-readable medium according to any one of the preceding clauses, wherein the adjustable mass body biases the center of mass of the steering wheel to return the steering wheel to a centered position.

[0106] 18. The non-transitory machine-readable medium according to any one of the preceding clauses, wherein the torque to be applied is further based on setting information representing driving mode settings received via the vehicle's user interface.

[0107] 19. The group consisting of the non-transitory machine-readable medium of Clause 12 above and any combination of the non-transitory machine-readable media 13-18 above.

[0108] 20. Methods, including:

[0109] A model of steering inconsistency is generated by a device including a processor based on machine learning, wherein the machine learning is applied to past steering data representing the position of the vehicle's steering wheel and the steering ratio; and

[0110] Using current position data representing the current position of the steering wheel and a steering unevenness model, the device determines the torque to be applied to the steering wheel, the torque to be applied to the steering wheel being configured to counteract the steering unevenness at the current position.

[0111] 21. The method according to any one of the preceding clauses, wherein the vehicle includes: electronic power steering, and wherein torque is applied via an electric motor for electronic power steering.

[0112] 22. The method according to any one of the preceding clauses, wherein the vehicle includes: electro-hydraulic power steering, and wherein torque is applied via an actuator of the electro-hydraulic power steering.

[0113] 23. Any combination of the methods in Clause 20 above and the methods in Clauses 21-22 above.

Claims

1. Vehicle steering system, including: Memory, which stores computer-executable components; A processor that executes computer-executable components stored in memory, wherein the computer-executable components include: A machine learning component, based on machine learning applied to past steering data representing the position and steering ratio of the vehicle's steering wheel, generates a model of steering unevenness; and A torque compensation unit, using current position data representing the current position of the steering wheel and a steering unevenness model, determines the torque to be applied to the steering wheel, the torque to be applied to the steering wheel being configured to counteract steering unevenness at the current position; An adjustable mass body is received in the steering wheel, wherein the adjustable mass body can be configured to change the center of gravity of the steering wheel; and An actuator, based on the torque to be applied to the steering wheel, moves an adjustable mass to adjust the center of gravity of the steering wheel.

2. The vehicle steering system according to claim 1, wherein, The vehicle includes electric power steering, and torque is applied via an electric motor for electric power steering.

3. The vehicle steering system according to claim 1, wherein, The vehicle includes a steer-by-wire drive system.

4. The vehicle steering system according to claim 1, wherein, Torque is applied via an electrical feedback motor in the steering wheel.

5. The vehicle steering system according to claim 1, wherein, The moment of inertia of the steering wheel is adjusted by moving the adjustable mass.

6. The vehicle steering system according to claim 1, further comprising: A position sensor that determines the position of the vehicle's steering wheel and the steering ratio.

7. The vehicle steering system according to claim 1, wherein, The uneven steering model is configured to model the relationship between the vehicle's steering angle and the corresponding steering torque manifested at the steering wheel.

8. The vehicle steering system according to claim 1, wherein, The steering wheel includes an adjustable steering wheel, and the past steering column data also indicates the past adjustment position of the steering wheel.

9. The vehicle steering system according to claim 1, wherein, The vehicle includes electro-hydraulic power steering, wherein torque is applied via an actuator of the electro-hydraulic power steering.

10. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processor, facilitate the execution of a vehicle steering operation, the vehicle steering operation including: A model of uneven steering is generated based on machine learning applied to past steering data representing the position of the vehicle's steering wheel and the steering ratio. as well as Using current position data representing the current position of the steering wheel and a steering unevenness model, determine the torque to be applied to the steering wheel, the torque to be applied to the steering wheel being configured to counteract the steering unevenness at the current position; The steering wheel includes an adjustable mass body, which can be configured to change the center of gravity of the steering wheel. as well as Based on the torque, the adjustable mass body is moved to adjust the center of gravity of the steering wheel.

11. The non-transitory machine-readable medium according to claim 10, wherein, The steering wheel includes an adjustable steering wheel, and the past steering column data also indicates the past adjustment position of the steering wheel.

12. The non-transitory machine-readable medium according to claim 10, wherein, The vehicle is driven by a steer-by-wire system.

13. The non-transitory machine-readable medium according to claim 12, wherein, The torque driven by the steer-by-wire system is applied via an electrical feedback motor in the steering wheel.

14. The non-transitory machine-readable medium according to claim 13, wherein, The adjustable mass can be moved to adjust the moment of inertia of the steering wheel.

15. The non-transitory machine-readable medium according to claim 14, wherein, The adjustable mass offsets the center of gravity of the steering wheel to return it to the centered position.

16. The non-transitory machine-readable medium according to claim 10, wherein, The torque to be applied is also based on settings information representing the driving mode settings received via the vehicle's user interface.

17. Vehicle steering methods, including: Based on machine learning, a device including a processor generates a model of uneven steering, wherein the machine learning is applied to past steering data representing the position of the vehicle's steering wheel and the steering ratio. as well as Using current position data representing the current position of the steering wheel and a steering unevenness model, the device determines the torque to be applied to the steering wheel, the torque to be applied to the steering wheel being configured to counteract the steering unevenness at the current position. The steering wheel includes an adjustable mass body, which can be configured to change the center of gravity of the steering wheel. as well as Based on the torque, the adjustable mass body is moved by the device to adjust the center of mass of the steering wheel to correspond to the central axis of the steering wheel.

18. The vehicle steering method according to claim 17, wherein, The vehicle includes electric power steering, and torque is applied via an electric motor for electric power steering.

19. The vehicle steering method according to claim 18, wherein, The vehicle includes electro-hydraulic power steering, wherein torque is applied via an actuator of the electro-hydraulic power steering.

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