Training a vehicle to adapt to a driver

Abstract

A system can electronically train a vehicle to adapt to a driver. The system can train the vehicle to adapt to the driver's abilities, conditions, and / or personality. The system can change control of the vehicle in response to input from the driver to match a pattern of control produced by a predetermined model (e.g., a safe driver model). The vehicle can appear to be driven by a safe driver. A driver with lower driving abilities can apply physical controls in a possibly slow, unstable, or insufficient pattern. The vehicle can be trained to adjust the conversion from UI signals to drive-by-wire signals so that the converted signals appear to be applied by a more capable driver on the road. Through training via machine learning, the conversion can improve over time.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Patent Application No. 16 / 785,341, filed February 7, 2020, and titled “TRAINING A VEHICLE TO ACCOMMODATE A DRIVER,” the entire disclosure of which is incorporated herein by reference. TECHNICAL FIELD

[0003] At least some embodiments disclosed herein relate to training a vehicle to accommodate a driver electronically. BACKGROUND

[0004] Advanced Driver Assistance Systems (ADAS) are electronic systems that assist drivers of vehicles while driving. ADAS improves car safety and road safety. ADAS can use electronic technologies such as electronic control units and power semiconductor devices. Most road accidents happen due to human error; therefore, ADAS that automate some controls of a vehicle can reduce human error and road accidents. Such systems have been designed to automate, adapt, and enhance vehicle systems to achieve safety and improve driving. Safety features of ADAS are designed to avoid collisions and accidents by providing technology that alerts the driver to potential problems, or by implementing safeguards and taking over control of the vehicle. Adaptive features can automate lighting, provide adaptive cruise control and collision avoidance, provide pedestrian collision avoidance mitigation (PCAM), alert the driver to other cars or hazards, provide a lane departure warning system, provide automatic lane centering, show a view in blind spots, or connect to a navigation system.

[0005] Advanced driver assistance systems or similar systems can also be implemented in general vehicles in addition to cars and trucks. Also, such vehicles can include boats and airplanes as well as vehicles or vehicle equipment for military, construction, agricultural, or recreational use. Vehicles can be customized or personalized via vehicle electronics and advanced driver assistance systems.

[0006] Vehicle electronics can include electronic systems used in vehicles. Vehicle electronics can include electronics for the drivetrain of a vehicle, the body or interior features of a vehicle, entertainment systems in a vehicle, and other parts of a vehicle. Ignition, engine, and transmission electronics can be found in vehicles with internal combustion powered machinery. Related elements for controlling electric vehicle systems are also found in hybrid and electric vehicles such as hybrid or electric motor vehicles. For example, electric cars can rely on power electronics to control the main propulsion electric motor and manage the battery system.

[0007] For ADAS and other types of vehicle systems, vehicle electronics can be a distributed system. Also, the distributed system in a vehicle can include powertrain control modules and powertrain electronics, body control modules and body electronics, interior electronics and chassis electronics, safety and entertainment electronics, and electronics for passenger and driver comfort systems. Furthermore, vehicle electronics can include electronics for vehicle automation. Such electronics can include or operate with mechatronics, artificial intelligence, and distributed systems. A vehicle that uses automation for complex tasks including navigation can be referred to as semi-autonomous. A vehicle that relies solely on automation can be referred to as autonomous. The Society of Automotive Engineers (SAE) has classified autonomy into six levels. Level 0 or no automation. Level 1 or driver assistance, where the vehicle can autonomously control steering or speed in specific situations to assist the driver. Level 2 or partial automation, where the vehicle can autonomously control both steering and speed in specific situations to assist the driver. Level 3 or conditional automation, where the vehicle can autonomously control both steering and speed in normal environmental conditions, but requires driver supervision. Level 4 or high automation, where the vehicle can autonomously complete travel in normal environmental conditions without driver supervision. And Level 5 or full autonomy, where the vehicle can autonomously complete travel in any environmental conditions. BRIEF DESCRIPTION OF DRAWINGS

[0008] The disclosure will be more fully understood from the following embodiments given by way of example and from the drawings of various embodiments of the present disclosure.

[0009] Figures 1 to 3 An example networked system that includes at least a mobile device and a vehicle and is configured to implement to electronically train a vehicle to adapt to a driver according to some embodiments of the present disclosure is described.

[0010] Figures 4 to 6 An example operation that can be performed by aspects of the networked system depicted in Figures 1 to 3 a flowchart of example operations that can be performed by aspects of the networked system depicted in DETAILED DESCRIPTION

[0011] At least some embodiments disclosed herein relate to electronically training a vehicle to adapt to a driver. For example, some example embodiments can relate to training a vehicle to adapt to a driver's abilities, conditions, and / or personality. A vehicle system can change implemented control of the vehicle in response to input from the driver to match a control pattern produced by a predetermined model (e.g., a predetermined safe driver model). Thus, the technology described herein can provide a vehicle that appears to be driven by a safe driver in situations that can not be driven by a safe driver. For example, a driver with lower driving abilities can apply physical controls with possibly slow, unstable, weak, or insufficient patterns. However, a vehicle can be trained to adjust a transition from physical controls to a drive-by-wire control such that the transitioned control appears to be applied by a typical or more capable driver on the road. For example, the transition can be trained to match or mimic ability levels, driving habits, and / or driving styles. Also, through training via machine learning, the transition can improve over time.

[0012] In some embodiments, a driver can control driving of a vehicle via user input into a user interface (UI). In such embodiments, a system can receive or sense input from a driver, and then the system can control the vehicle accordingly. The transition from user input signals to control signals for physical mechanisms used to drive the vehicle can occur via electronics of the vehicle, and the transition can be adjusted according to ADAS. Also, the transition can be trained to match or mimic ability levels, driving habits, and / or driving styles. Also, through training via machine learning, the transition can improve over time.

[0013] In such embodiments and other embodiments, a UI can be, be part of, or include a car control. For example, a UI can be a gas pedal, a brake pedal, or a steering wheel. Also, a UI can be part of or include electronics and / or electromechanical devices, and can be part of or include a haptic UI (touch), a visual UI (sight), an auditory UI (sound), an olfactory UI (smell), a balance UI (balance), or a gustatory UI (taste), or any combination thereof.

[0014] A set of mechanical components for controlling the drive of a vehicle can include: (1) a braking mechanism on the wheels (for stopping the wheels from spinning); (2) a throttling mechanism on the engine or motor (for regulating how much gas enters the engine, or how much current enters the motor), which determines how fast the drive shaft can spin and thus how fast the car can go; and (3) a steering mechanism for the orientation of the front wheels (e.g., so the vehicle travels in the direction the wheels are pointed). These mechanisms can control the braking, acceleration, and steering of the vehicle. The user indirectly controls these mechanisms through UI elements that can be operated by the user, typically a brake pedal, an accelerator pedal, and a steering wheel. The pedals and wheel are not necessarily mechanically connected to the drive mechanisms for braking, acceleration, and steering. Also, such parts can have or be proximate to sensors that measure the amount of pressure on the pedals and / or the amount of turning of the wheel by the driver. Furthermore, the sensed control inputs are transmitted to a control unit via wires (and thus can be drive-by-wire).

[0015] Adjustable aspects for driving a vehicle can include drive configurations and preferences that can be adjusted from a controller via automotive electronics (e.g., adjustments in transmission, engine, chassis, passenger environment, and safety features via respective automotive electronics). Drive aspects can also include typical drive aspects and / or drive-by-wire aspects, such as controlling the steering, braking, and acceleration of the vehicle. Aspects for driving a vehicle can also include settings according to SAE controls for different levels of automation, such as controlling to set no automation preferences / configurations (level 0), driver-assistance preferences / configurations (level 1), partial automation preferences / configurations (level 2), conditional automation preferences / configurations (level 3), high automation preferences / configurations (level 4), or full preferences / configurations (level 5). Aspects for driving a vehicle can also include controlling settings for drive modes, such as sport or performance mode, fuel economy mode, traction mode, all-electric mode, hybrid mode, AWD mode, FWD mode, RWD mode, and 4WD mode.

[0016] In vehicles, drivers can control the vehicle via physical control elements (e.g., steering wheel, brake pedal, throttle pedal, accelerator pedal, shift tab, etc.) that interface with drive components by mechanical linkages and some electromechanical linkages. However, more and more vehicles are currently interfacing control elements with mechanical powertrain elements (e.g., braking systems, steering mechanisms, drivetrains, etc.) via electronic control elements or modules (e.g., electronic control units or ECUs). The electronic control elements or modules can be part of drive-by-wire technology.

[0017] Drive-by-wire technology can include electrical or electromechanical systems for performing vehicle functions that are traditionally accomplished by mechanical linkages. The technology can replace traditional mechanical control systems with electronic control systems using electromechanical actuators and human-machine interfaces such as pedals and steering feel simulators. Components such as steering columns, intermediate shafts, pumps, hoses, belts, coolers, and vacuum servers and master cylinders can be removed from the vehicle. There are different degrees and types of drive-by-wire technology.

[0018] A vehicle with drive-by-wire technology can include a modulator (e.g., a modulator that includes or is part of an ECU and / or ADAS) that receives input from a user or driver (e.g., via more conventional controls or via drive-by-wire controls or some combination thereof). The modulator can then modulate the input or convert the input to match the input of a "safe driver" using the driver's input. The input of the "safe driver" can be represented by a model of the "safe driver".

[0019] A modulator (e.g., a modulator that includes or is part of an ECU and / or ADAS) can be trained or adjusted so that the conversion from user input to a "safe driver" occurs with a high success rate. Over time, the modulator can become more effective at the conversion. Thus, over time, a vehicle with a trainable modulator can be trained to more successfully accommodate the abilities or personalities of "unsafe" drivers. The modulator can use machine learning and AI. For example, the modulator can include an artificial neural network (ANN) and the ANN can be trained over time. Further, there are at least three components in training a vehicle to accommodate the abilities or personalities or habits of a driver. A first component parameterizes the input characteristics of a driver. A second component parameterizes the output characteristics of a safe or capable driver. A third component determines a modulation technique and / or algorithm that converts the first component to the second component regularly or consistently (or to some extent). For example, the conversion has some success rate that meets some threshold. The training or adjustment of the modulation technique and / or algorithm can be based on the success rate or outcome of the output compared to a desired output. The desired output is the output associated with a safe driver or the like. The input is the input of any driver using the vehicle.

[0020] A "safe driver" model can be generated based on historically safe driving data from sensors and gauges in vehicles driven by safe drivers or the like. The model can be generic or more specific to the type of vehicle and even specific to the make and model of the vehicle.

[0021] Figures 1 to 3An example networked system 100 is described that includes at least a mobile device and a vehicle (see, e.g., mobile devices 140, 150, and 302 study vehicles 102, 202, and 130) and is configured to implement instances of the electronic training of a vehicle to accommodate a driver in accordance with some embodiments of the present disclosure.

[0022] The networked system 100 is networked via one or more communication networks 120. The communication networks (e.g., the one or more communication networks 120) described herein can include at least a local to device network such as Bluetooth, a wide area network (WAN), a local area network (LAN), an intranet, a mobile wireless network such as 4G or 5G, an extranet, the Internet, and / or any combination thereof. The nodes (see, e.g., mobile devices 140, 150, and 302 and vehicles 102, 202, and 130) of the networked system 100 can each be part of a peer-to-peer network, a client-server network, a cloud computing environment, etc. Moreover, any of the devices, computing devices, vehicles, sensors or cameras, and / or user interfaces described herein can include a computer system of some sort (see, e.g., computing systems 104 and 204). And, such a computer system can include a network interface to other devices in a LAN, an intranet, an extranet, and / or the Internet. The computer system can also operate in the capacity of a server or a client machine in client-server network environments, as a peer machine in peer-to-peer (or distributed) network environments, or as a server or a client machine in a cloud computing infrastructure or environment.

[0023] As shown in Figure 1 The networked system 100 can include at least a vehicle 102 that includes a vehicle computing system 104 (including an advanced driver assistance system 106), a body and controllable portions of the body (not depicted), a powertrain and controllable portions of the powertrain (not depicted), a body control module 108 (which is a type of ECU), a powertrain control module 110 (which is a type of ECU), a power steering control unit 112 (which is a type of ECU) in accordance with some embodiments of the present disclosure. The vehicle 102 also includes a plurality of user interface elements (see, e.g., UI elements 114a-b) that are some of the vehicle's example car controls 115. Also, the vehicle 102 includes a plurality of sensors (see, e.g., sensors 116a-b) and a controller area network (CAN) bus 118 that connects at least the vehicle computing system 104, the body control module 108, the powertrain control module 110, the power steering control unit 112, the plurality of UI elements, and the plurality of sensors to each other. Moreover, as shown, the vehicle 102 is connected to the network 120 via the vehicle computing system 104. Further, as shown, the vehicle 130 and the mobile devices 140 and 150 are connected to the network 120. And, thus, are communicatively coupled to the vehicle 102.

[0024] The vehicle 102 includes vehicle electronics, which includes at least electronics for controllable portions of the body, controllable portions of the powertrain, and controllable portions of the power steering. The vehicle 102 includes controllable portions of the body, and such portions and subsystems are connected to the body control module 108. The body includes at least a frame to support the powertrain. A chassis of the vehicle can be attached to the frame of the vehicle. The body can also include an interior for at least one driver or passenger. The interior can include a seat. Controllable portions of the body can also include one or more power doors and / or one or more power windows. The body can also include any other known portions of the body of the vehicle. Also, controllable portions of the body can also include a convertible top, a sunroof, power seats, and / or any other type of controllable portion of the body of the vehicle. The body control module 108 can control the controllable portions of the body.

[0025] Further, the vehicle 102 also includes controllable portions of the powertrain. Controllable portions of the powertrain have portions and subsystems connected to the powertrain control module 110. Controllable portions of the powertrain can include at least an engine, a transmission, drive axles, suspension and steering systems, and powertrain electrical systems. The powertrain can also include any other known portions of the powertrain of the vehicle, and controllable portions of the powertrain can include any other known controllable portions of the powertrain. Further, controllable portions of the power steering can be controlled via the power steering control unit 112.

[0026] The plurality of UI elements of the vehicle 102 (see, e.g., UI elements 114a-b) can include any type of UI. The UI elements can be, be part of, or include an automotive control. For example, the UI can be a gas pedal, a brake pedal, or a steering wheel. Further, the UI can be part of or include an electronic device and / or an electro-mechanical device, and can be part of or include a tactile UI (touch), a visual UI (sight), an auditory UI (sound), an olfactory UI (smell), a balance UI (balance), or a gustatory UI (taste), or any combination thereof.

[0027] The plurality of sensors of the vehicle 102 (see, e.g., sensors 116a-b) can include any type of sensor or camera configured to sense and / or record one or more characteristics or properties of the plurality of UI elements or outputs thereof. The sensors of the vehicle 102 can also be configured to generate data corresponding to the one or more characteristics or properties of the plurality of UI elements or outputs thereof from the sensed and / or recorded characteristics or properties. The sensors of the vehicle 102 can also be configured to output the generated data corresponding to the one or more characteristics or properties. Any of the plurality of sensors can also be configured to send the generated data corresponding to the one or more characteristics or properties to the computing system 104 or other electronic circuitry (e.g., the body control module 108, the powertrain control module 110, and the power steering control unit 112) of the vehicle 102, e.g., via the CAN bus 118.

[0028] A set of mechanical components for controlling the drive of a vehicle can include: (1) a braking mechanism on the wheels (to stop the wheels from spinning), (2) a throttling mechanism on the engine or motor (to regulate how much gas goes into the engine, or how much current goes into the motor), which determines how fast the drive shaft can spin and thus how fast the car can go, and (3) a steering mechanism for the orientation of the front wheels (e.g., so the vehicle travels in the direction the wheels are pointed). These mechanisms control the braking, acceleration, and steering of the vehicle. The user indirectly controls these mechanisms through UI elements that can be operated by the user, typically the brake pedal, the accelerator pedal, and the steering wheel. The pedals and the steering wheel are not necessarily mechanically connected to the drive mechanisms for braking, acceleration, and steering. Moreover, such parts can have or be proximate to sensors that measure the amount of pressure on the pedals and / or the amount of rotation of the steering wheel by the driver. Furthermore, the sensed control inputs are transmitted via wires to a control unit (and thus can be drive-by-wire).

[0029] In some embodiments, the vehicle 102 can include a body, a powertrain, and a chassis. The vehicle 102 can also include a plurality of electronic control units (ECUs) configured to control the drive of the vehicle (see, e.g., the body control module 108, the powertrain control module 110, and the power steering control unit 112). The vehicle 102 can also include a plurality of user UI elements configured to be manipulated by a driver to indicate the degree of control exerted by the driver (see, e.g., UI elements 114a-b of example vehicle controls 115).

[0030] The plurality of UI elements (e.g., UI elements 114a-b) can be configured to measure signals indicative of a degree of control exerted by the driver. The plurality of UI elements can also be configured to electronically transmit the signals to the plurality of ECUs. The ECUs (e.g., see body control module 108, powertrain control module 110, and power steering control unit 112) can be configured to generate control signals for driving the vehicle 102 based on the measured signals received from the plurality of UI elements.

[0031] The vehicle 102 can also include an advanced driver assistance system (e.g., see advanced driver assistance system 106). The advanced driver assistance system 106 (ADAS 106) can be configured to identify a pattern of the driver interacting with the UI elements (e.g., UI elements 114a-b of example car controls 115). The ADAS 106 can also be configured to determine a deviation of the pattern from a predetermined model (e.g., a predetermined model of a regular driver, a predetermined model of a safe driver, etc.). The ADAS 106 can also be configured to adjust the plurality of ECUs (e.g., body control module 108, powertrain control module 110, and power steering control unit 112) in accordance with the deviation in order to convert the signals measured by the UI elements into control signals for driving the vehicle 102. For example, the ADAS 106 can be configured to change a transfer function used by the ECUs to control the driving of the vehicle based on the deviation.

[0032] In such embodiments and other embodiments, the ADAS 106 can be further configured to adjust the plurality of ECUs (e.g., body control module 108, powertrain control module 110, and power steering control unit 112) in accordance with sensor data indicative of environmental conditions of the vehicle in order to convert the signals measured by the UI elements (e.g., UI elements 114a-b) into control signals for driving the vehicle 102. Also, the ADAS 106 can be further configured to determine a response difference between the measured signals generated by the plurality of UI elements and driving decisions autonomously generated by the ADAS 106 in accordance with the predetermined model and the sensor data indicative of the environmental conditions of the vehicle 102. Further, the ADAS 106 can be further configured to train an ANN to identify the deviation based on the response difference.

[0033] In such embodiments and other embodiments, for the determination of the deviation, the ADAS 106 can be configured to input the transmitted signals indicative of the degree of control into the ANN. Also, the ADAS 106 can be configured to determine at least one feature of the deviation based on an output of the ANN. Further, for training the determination of the deviation, the ADAS 106 can be configured to train the ANN. For training the ANN, the ADAS 106 can be configured to adjust the ANN based on the deviation.

[0034] In such embodiments and other embodiments, the predetermined model can be derived from a correlation model of a preselected safe driver. Further, the predetermined model can be derived from a correlation model of a driver having a preselected driver ability level. The predetermined model can also be derived from a correlation model of a driver having a preselected driving habit. The predetermined model can also be derived from a correlation model of a driver having a preselected driving style. Also, the predetermined model can also be derived from any combination thereof.

[0035] In such embodiments and other embodiments, the plurality of UIs (e.g., UI elements 114a-b of example car controls 115) can include a steering control (e.g., a steering wheel or a GUI or another type of UI equivalent, such as a voice input UI for steering). Further, the plurality of UIs can include a braking control (e.g., a brake pedal or a GUI or another type of UI equivalent, such as a voice input UI for braking). The plurality of UIs can also include a throttle control (e.g., an accelerator pedal or a GUI or another type of UI equivalent, such as a voice input UI for accelerating the vehicle). Also, the degree of control exerted by the driver can include detected user interaction with at least one or any combination of the steering control, the braking control, or the throttle control.

[0036] In such embodiments and other embodiments, the ADAS 106 can be configured to change a transfer function used by an ECU (e.g., the body control module 108, the powertrain control module 110, and the power steering control unit 112) to control driving of the vehicle 102 based on the bias. Also, the transfer function can include or be derived from at least one transfer function for controlling at least one of a steering mechanism of the vehicle 102, a throttle mechanism of the vehicle, or a braking mechanism of the vehicle or any combination thereof.

[0037] Further, the plurality of UIs (e.g., UI elements 114a-b of example car controls 115) can include a transmission control (e.g., a manual transmission and driver operated clutch or a GUI or another type of UI equivalent, such as a voice input UI for changing a speed of the vehicle). Also, the degree of control exerted by the driver can include detected user interaction with the transmission control. The transfer function can include or be derived from a transfer function for controlling a drivetrain of the vehicle 102.

[0038] In such embodiments and other embodiments, the vehicle 102 can include a plurality of automotive controls configured to be manipulated by the driver to indicate a degree of control exerted by the driver (e.g., see example automotive controls 115). As shown, the automotive controls 115 can include a plurality of UI elements (e.g., see UI elements 114a-b). The vehicle 102 can also include a plurality of sensors configured to detect a degree of control exerted by the driver on the plurality of automotive controls (e.g., the UI elements can measure the detected signals). The plurality of sensors can also be configured to electronically transmit signals indicative of the detected degree of control to the plurality of ECUs (and / or in some embodiments, the UI can electronically transmit the measured signals to the plurality of ECUs). In such example embodiments, the ECUs can be configured to generate control signals for driving the vehicle based on the signals received from the plurality of sensors and / or from the plurality of UI elements, depending on the embodiment.

[0039] In such embodiments and other embodiments, the ADAS 106 can be configured to receive transmitted signals indicative of detected degrees of control exerted by the driver on the plurality of automotive controls (e.g., example automotive controls 115). The ADAS 106 can also be configured to generate a filter for the driver based on deviations of the transmitted signals from predetermined models (e.g., a regular driver model, a safe driver model, etc.). For example, with the generation of the filter, the ADAS 106 can be configured to identify patterns of the driver interacting with the UI elements and determine deviations of the patterns from the predetermined models. Further, the ADAS 106 can be configured to change a transfer function used by the ECUs (e.g., the body control module 108, the powertrain control module 110, and the power steering control unit 112) to control driving of the vehicle 102 based on the generated filter.

[0040] In such embodiments and other embodiments, to train the generation of the filter, the ADAS 106 can be configured to determine differences between the changed transfer function and a predetermined transfer function (e.g., the predetermined transfer function can be in the predetermined models). The ADAS 106 can also be configured to adjust the generation of the filter based on the differences between the changed transfer function and the predetermined transfer function. For the generation of the filter, the ADAS 106 can be configured to input the transmitted signals indicative of detected degrees of control exerted by the driver on the plurality of automotive controls (e.g., example automotive controls 115) into an ANN. Also, the ADAS 106 can be configured to determine at least one feature of the filter based on an output of the ANN.

[0041] In such embodiments and other embodiments, to train the generation of the filter, the ADAS 106 can be configured to train the ANN. Also, to train the ANN, the ADAS 106 can be configured to determine a difference between the altered transfer function and a predetermined transfer function and adjust the ANN based on the difference between the altered transfer function and the predetermined transfer function. Further, the predetermined model can be derived from a relevant model of a preselected safe driver. Moreover, the predetermined model can be derived from a relevant model of a driver having a preselected driver ability level. The predetermined model can also be derived from a relevant model of a driver having a preselected driving habit. The predetermined model can also be derived from a relevant model of a driver having a preselected driving style. Also, the predetermined model can also be derived from any combination thereof.

[0042] In such embodiments and other embodiments, the plurality of vehicle controls (and / or UI elements - e.g., GUI elements) can include a steering control (e.g., a steering wheel or a GUI or another type of UI equivalent, e.g., a voice input UI for steering). The plurality of vehicle controls can also include a braking control (e.g., a brake pedal or a GUI or another type of UI equivalent, e.g., a voice input UI for braking). The plurality of vehicle controls can also include a throttle control (e.g., an accelerator pedal or a GUI or another type of UI equivalent, e.g., a voice input UI for accelerating the vehicle). Also, the detected degree of control exerted by the driver on the plurality of vehicle controls can include detected user interaction with at least one or any combination thereof of the steering control, the braking control, or the throttle control. In such instances and other instances, the ADAS 106 can be configured to alter a transfer function for use by the ECU to control driving of the vehicle based on the filter. Also, the transfer function can include or be derived from at least one transfer function for controlling at least one or any combination thereof of a steering mechanism of the vehicle, a throttle mechanism of the vehicle, or a braking mechanism of the vehicle.

[0043] Further, the plurality of vehicle controls can include a transmission control (e.g., a manual transmission and driver operated clutch or a GUI or another type of UI equivalent, e.g., a voice input UI for changing a speed of the vehicle). Also, the detected degree of control exerted by the driver can include detected user interaction with the transmission control. The transfer function can include or be derived from a transfer function for controlling a drivetrain of the vehicle.

[0044] In some embodiments, the electronic circuitry of a vehicle (e.g., see vehicles 102 and 202) that can comprise or be part of a computing system of the vehicle can comprise at least one of: engine electronics, transmission electronics, chassis electronics, passenger environment and comfort electronics, in-vehicle entertainment electronics, in-vehicle safety electronics, or navigation system electronics, or any combination thereof (e.g., see body control module 108 and 220, powertrain control module 110 and 222, power steering control unit 112 and 224, battery management system 226, and infotainment electronics 228 shown in FIGS. 1 and 2, respectively). In some embodiments, the electronic circuitry of the vehicle can comprise electronics for an autonomous driving system. Figure 1 and 2 In some embodiments, the electronic circuitry of a vehicle (e.g., see vehicles 102 and 202) that can comprise or be part of a computing system of the vehicle can comprise at least one of: engine electronics, transmission electronics, chassis electronics, passenger environment and comfort electronics, in-vehicle entertainment electronics, in-vehicle safety electronics, or navigation system electronics, or any combination thereof (e.g., see body control module 108 and 220, powertrain control module 110 and 222, power steering control unit 112 and 224, battery management system 226, and infotainment electronics 228 shown in FIGS. 1 and 2, respectively). In some embodiments, the electronic circuitry of the vehicle can comprise electronics for an autonomous driving system.

[0045] Adjustable aspects for driving the vehicle 102 or 202 can include drive configurations and preferences that can be adjusted from a controller via automotive electronics (e.g., adjustments in transmission, engine, chassis, passenger environment, and safety features via respective automotive electronics). Drive aspects can also include typical drive aspects and / or by-wire drive aspects, such as controlling steering, braking, and acceleration of the vehicle (e.g., see body control module 108, powertrain control module 110, and power steering control unit 112). Aspects for driving the vehicle can also include settings according to SAE controls for different levels of automation, such as controlling to set no automation preferences / configurations (level 0), driver assist preferences / configurations (level 1), partial automation preferences / configurations (level 2), conditional automation preferences / configurations (level 3), high automation preferences / configurations (level 4), or full preferences / configurations (level 5). Aspects for driving the vehicle can also include controlling settings for drive modes, such as sport or performance mode, fuel economy mode, traction mode, all-electric mode, hybrid mode, AWD mode, FWD mode, RWD mode, and 4WD mode.

[0046] In some embodiments, the computing system of the vehicle (e.g., computing system 104 or 204) may include a central control module (CCM), a central timing module (CTM), and / or a general electronic module (GEM). Furthermore, in some embodiments, the vehicle may include an ECU, which may be any embedded system controlling one or more automotive electronic devices within the vehicle's electrical system or subsystems. Types of ECUs may include engine control modules (ECMs), powertrain control modules (PCMs), transmission control modules (TCMs), brake control modules (BCMs or EBCMs), CCMs, CTMs, GEMs, body control modules (BCMs), suspension control modules (SCMs), door control units (DCUs), etc. ECU types may also include power steering control units (PSCUs), one or more human-machine interface (HMI) units, powertrain control modules (PCMs)—which may at least function as ECMs and TCMs, seat control units, speed control units, telematics control units, transmission control units, brake control modules, and battery management systems.

[0047] like Figure 2 As shown, the network system 100 may include at least a vehicle 202, which includes at least a vehicle computing system 204, a main body (not shown) with an interior (not shown), a powertrain (not shown), an air conditioning control system (not shown), and an infotainment system (not shown). The vehicle 202 may also include other vehicle components.

[0048] A computing system 204, which can have similar structure and / or functionality as the computing system 104, can be connected to a communication network 120, which can include at least a local-to-device network such as Bluetooth, a wide area network (WAN), a local area network (LAN), an intranet, a mobile wireless network such as 4G or 5G, an extranet, the Internet, and / or any combination thereof. The computing system 204 can be a machine capable of (sequentially or otherwise) executing a set of instructions that specify actions to be taken by that machine. Further, while a single machine is illustrated for the computing system 204, it should be considered that the term "machine" includes any collection of machines, either individual or collective, that individually or collectively execute a set, or multiple sets, of instructions to perform a method or operation. Moreover, it can include at least a bus (e.g., see bus 206) and / or motherboard, one or more controllers (e.g., one or more CPUs, e.g., see controller 208), a main memory that can include temporary data storage (e.g., see memory 210), at least one type of network interface (e.g., see network interface 212), a storage system that can include permanent data storage (e.g., see data storage system 214), and / or any combination thereof. In some multi-device embodiments, one device can complete some portions of the methods described herein, then send the results of the completion over a network to another device, so that the other device can continue with other steps of the methods described herein.

[0049] Figure 2An example portion of a computing system 204 that can include and implement an advanced driver assistance system 106 (or ADAS 106) is also illustrated. The computing system 204 can be communicatively coupled to the network 120, as shown. The computing system 204 includes at least a bus 206, a controller 208 (e.g., a CPU) that can execute instructions of the ADAS 106, a memory 210 that can hold instructions of the ADAS 106 for execution, a network interface 212, a data storage system 214 that can store instructions of the ADAS 106, and other components 216 - which can be any type of components found in mobile or computing devices, such as GPS components, I / O components such as cameras and various types of user interface components (which can include one or more of the UI elements described herein) and sensors (which can include one or more of the sensors described herein). The other components 216 can include one or more user interfaces (e.g., GUIs, auditory user interfaces, haptic user interfaces, car controls, etc.), displays, different types of sensors, haptic, audio, and / or visual input / output devices, additional dedicated memory, one or more additional controllers (e.g., GPUs), or any combination thereof. The computing system 204 can also include a sensor interface configured to interface with sensors of the vehicle 202, which can be one or more of any of the sensors described herein (e.g., see sensors 219a, 219b, and 219c). In some embodiments, the bus 206 communicatively couples the controller 208, the memory 210, the network interface 212, the data storage system 214, the other components 216, and the sensors and the sensor interface. The computing system 204 includes a computer system that includes at least the controller 208, the memory 210 (e.g., read-only memory (ROM), flash memory, dynamic random-access memory (DRAM) (e.g., synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), static random-access memory (SRAM), cross-point memory, crossbar memory, etc.), and the data storage system 214 in communication with each other via the bus 206 (which can include multiple buses).

[0050] In some embodiments, the computing system 204 can include a set of instructions for causing a machine to perform any one or more of the methodologies discussed herein, when the set of instructions are executed by the machine. In such embodiments, the machine can be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, and / or the Internet (e.g., the network 120) through a network interface 212. The machine can operate in the capacity of a peer machine in peer-to-peer (or distributed) networks, as a server or a client machine in client-server networks, or as a server or a client machine in a cloud computing infrastructure or environment.

[0051] Controller 208 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit, or the like. More particularly, the processing device can be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, single instruction multiple data (SIMD), multiple instruction multiple data (MIMD), or a processor implementing other instruction sets, or processors implementing a combination of instruction sets. Controller 208 can also be one or more special-purpose processing devices such as an ASIC, a programmable logic for example a FPGA, a digital signal processor (DSP), network processor, or the like. Controller 208 is configured to execute instructions for performing the operations and steps discussed herein. Controller 208 can further include a network interface device such as network interface 212 to communicate over one or more communication networks, such as network 120.

[0052] Data storage system 214 can include a machine-readable storage medium (also known as a computer-readable medium) on which is stored one or more sets of instructions or software embodying any one or more of the methodologies or functions described herein. Data storage system 214 can have execution capabilities such that it can at least partially execute instructions residing in the data storage system. Instructions can also reside in memory 210 and / or controller 208, which also constitute machine-readable storage media, during execution thereof by a computer system, either completely or at least partially. Memory 210 can be or include a main memory of system 204. Memory 210 can have execution capabilities such that it can at least partially execute instructions residing in the memory.

[0053] Vehicle 202 can also have a vehicle body control module 220 of the body, a powertrain control module 222 of the powertrain, a power steering control unit 224, a battery management system 226, infotainment electronics 228 of the infotainment system, and a CAN bus 218 connecting at least the vehicle computing system 204, the vehicle body control module, the powertrain control module, the power steering control unit, the battery management system, and the infotainment electronics. Further, as shown, vehicle 202 is connected to network 120 via vehicle computing system 204. Further, as shown, vehicles 130 and mobile devices 140 and 150 are connected to network 120. And thus, are communicatively coupled to vehicle 202.

[0054] The vehicle 202 is also shown as having a plurality of sensors (see, e.g., sensors 219a, 219b, and 219c) that can be part of the computing system 204. In some embodiments, the CAN bus 218 can connect the plurality of sensors, the vehicle computing system 204, the vehicle body control module, the powertrain control module, the power steering control unit, the battery management system, and the infotainment electronics to at least the computing system 204. The plurality of sensors can be connected to the computing system 204 via a sensor interface of the computing system.

[0055] As shown in Figure 3 The networking system 100 can include at least the mobile device 302, as shown in

[0056] Depending on the embodiment, the mobile device 302 can be or include a mobile device such as a smartphone, a tablet computer, an IoT device, a smart television, a smart watch, glasses or other smart appliance, an in-vehicle information system, a wearable smart device, a game console, a PC, a digital camera, or any combination thereof. As shown, the mobile device 302 can be connected to a communication network 120 that includes at least a local-to-device network such as Bluetooth, a wide area network (WAN), a local area network (LAN), an intranet, a mobile wireless network such as 4G or 5G, an extranet, the Internet, and / or any combination thereof.

[0057] Each of the mobile devices described herein can be, or be replaced by, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a web appliance, a server, a network router, switch or bridge, or any machine capable of (sequentially or otherwise) executing a set of instructions that specify actions to be taken by that machine. The computing system of a vehicle described herein can be a machine that is capable of (sequentially or otherwise) executing a set of instructions that specify actions to be taken by that machine.

[0058] Moreover, while a single machine is illustrated for the computing system and mobile devices described herein, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Further, each of the illustrated mobile devices can each include at least a bus and / or motherboard, one or more controllers (e.g., one or more CPUs), a main memory that can include temporary data storage, at least one type of network interface, a storage system that can include permanent data storage, and / or any combination thereof. In some multi-device embodiments, one device can complete some portions of the methods described herein, then send the results of the completion over a network to another device so that the other device can continue with other steps of the methods described herein.

[0059] Figure 3 Example portions of a mobile device 302 according to some embodiments of the present disclosure are also illustrated. As shown, the mobile device 302 can be communicatively coupled to the network 120. The mobile device 302 includes at least a bus 306, a controller 308 (e.g., a CPU), a memory 310, a network interface 312, a data storage system 314, and other components 316 (which can be any type of components found in mobile or computing devices, such as GPS components, for example, various types of user interface components and sensors (e.g., biometric sensors), and I / O components such as cameras). The other components 316 can include one or more user interfaces (e.g., GUIs, auditory user interfaces, haptic user interfaces, etc.), displays, different types of sensors, haptic (e.g., biometric sensors), audio and / or visual input / output devices, additional specialized memory, one or more additional controllers (e.g., GPUs), or any combination thereof. The bus 306 communicatively couples the controller 308, the memory 310, the network interface 312, the data storage system 314, and the other components 316. The mobile device 302 includes a computer system that includes at least the controller 308, the memory 310 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) (e.g., synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), static random access memory (SRAM), cross-point memory, crossbar memory, etc.), and the data storage system 314 in communication with each other via the bus 306 (which can include multiple buses).

[0060] In other words, Figure 3A block diagram of a mobile device 302 for a computer system in which embodiments of the disclosure can operate. In some embodiments, the computer system can include a set of instructions for causing a machine to perform some of the methodologies discussed herein, when the set of instructions are executed by the machine. In such an embodiment, the machine can be connected (e.g., networked, via the network interface 312) to other machines in a LAN, an intranet, an extranet, and / or the Internet (e.g., network 120). The machine can operate in the capacity of a peer machine in peer-to-peer (or distributed) networks environments, as a server or a client machine in client-server network environments, or as a server or a client machine in a cloud computing infrastructure or environment.

[0061] The controller 308 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device can be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, single instruction multiple data (SIMD), multiple instruction multiple data (MIMD), or a processor implementing other instruction sets, or processors implementing a combination of instruction sets. The controller 308 can also be one or more special-purpose processing devices such as an ASIC, a programmable logic for example a FPGA, a digital signal processor (DSP), network processor, or the like. The controller 308 is configured to execute instructions for performing the operations and steps discussed herein. The controller 308 can further include a network interface device such as the network interface 312 to communicate over one or more communication networks, such as the network 120.

[0062] The data storage system 314 can include a machine-readable storage medium (also known as a computer-readable medium) on which is stored one or more sets of instructions or software embodying any one or more of the methodologies or functions described herein. The data storage system 314 can have execution capabilities, e.g., it can execute at least in part instructions residing in the data storage system. Instructions can also reside, completely or at least partially, within the memory 310 and / or within the controller 308 during execution thereof by the computer system, which also constitute machine-readable storage media. The memory 310 can be or include a main memory of the device 302. The memory 310 can have execution capabilities, e.g., it can execute at least in part instructions residing in the memory.

[0063] While the memory, controller, and data storage device are shown in the example embodiment as each being a single part, each should be considered to include a single part or multiple parts that can store instructions and perform their respective operations. The term "machine-readable storage medium" should also be considered to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the present disclosure. Therefore, the term "machine-readable storage medium" should be

[0064] As shown in Figure 3 , the mobile device 302 can include a user interface (see, e.g., other components 316). The user interface can be configured to provide a graphical user interface (GUI), a tactile user interface, or an audible user interface, or any combination thereof. For example, the user interface can be or include a display that is connected to at least one of a wearable structure, a computing device, or a video camera, or any combination thereof, that can also be part of the mobile device 302, and that can be configured to provide a GUI. Moreover, the embodiments described herein can include one or more user interfaces of any type, including tactile UI (touch), visual UI (sight), audible UI (sound), olfactory UI (smell), equilibrium UI (balance), and gustatory UI (taste).

[0065] Moreover, as shown in Figure 3 , the mobile device 302 can include a computing system (see, e.g., bus 306, controller 308, memory 310, network interface 312, and data storage system 314, all of which are components of the computing system). The computing system of the mobile device 302 can be configured to take biometric and / or non-biometric data from its user itself and its sensors (see, e.g., other components 316), and then send the biometric and / or non-biometric data to a vehicle connected to the mobile device via its network interface and network 120.

[0066] Figure 4 A flowchart of example operations of a method 400 that can be performed by aspects of the networking system depicted in Figures 1 to 3 , in accordance with some embodiments of the present disclosure, is illustrated. For example, the method 400 can be performed by the computing system and / or other portions of any vehicle and / or mobile device depicted in Figures 1 to 3 .

[0067] In Figure 4In this method, method 400 begins at step 402, wherein multiple sensors in the vehicle detect signals indicating the degree of control applied by the driver using multiple user interface (UI) elements in the vehicle. At step 404, method 400 continues to measure signals indicating the degree of control applied by the driver using the multiple UI elements. At step 406, method 400 continues to electronically transmit the measured signals via the multiple UI elements to multiple electronic control units (ECUs) of the vehicle and the vehicle's advanced driver assistance system (ADAS). At step 408, method 400 continues to generate control signals for driving the vehicle via the ECUs based on the measured signals received from the multiple UI elements. At step 410, method 400 continues to identify the driver's pattern interacting with the multiple UI elements via the ADAS based on the measured signals received from the multiple UI elements. At step 412, method 400 continues to determine the deviation of the pattern from a predetermined model via the ADAS. At step 414, method 400 continues to adjust the multiple ECUs according to the deviation via the ADAS to convert the signals measured by the UI elements into control signals for driving the vehicle.

[0068] Figure 5 The description explains that some embodiments of this disclosure can be derived from... Figures 1 to 3 The flowchart illustrates an example operation of method 500 performed by various aspects of a networked system. For example, method 500 may be implemented by a computing system and / or... Figures 1 to 3 This is performed on any vehicle and / or other part of the moving device depicted. As shown, method 500 begins with steps 402 to 412 of method 400. Then, at step 502, method 500 continues to generate signal modulation for the driver based on the determined deviation via an advanced driver assistance system. At step 504, method 500 continues to adjust multiple ECUs according to the generated signal modulation via the advanced driver assistance system to convert signals measured by the UI elements into control signals for driving the vehicle.

[0069] Figure 6 The description explains that some embodiments of this disclosure can be derived from... Figures 1 to 3 The flowchart illustrates an example operation of method 600 performed by various aspects of a networked system. For example, method 600 may be implemented by a computing system and / or... Figures 1 to 3 This is performed on any vehicle and / or other part of the moving device depicted. As shown, method 600 begins with steps 402 to 412 of method 400. Then, at step 602, method 600 continues by altering the transfer functions of multiple UI elements based on the determined deviation using an advanced driver assistance system. At step 604, method 600 continues by adjusting multiple ECUs according to the altered transfer functions using the advanced driver assistance system to convert signals measured by the UI elements into control signals for driving the vehicle.

[0070] In some embodiments, it is understood that the steps of the methods 400, 500, or 600 can be implemented as a continuous process, e.g., each step can run independently by monitoring input data, performing operations, and outputting data to subsequent steps. Further, such steps for each method can be implemented as a discrete event process, e.g., each step can be triggered by an event that it should trigger and produce some output. It is also understood that, Figures 4 to 6 Each of the diagrams in Figures 1 to 3 The methods partially presented in Figures 4 to 6 The steps depicted in each of the diagrams in

[0071] It is understood that the vehicles described herein can be any type of vehicle unless otherwise specified. Vehicles can include cars, trucks, boats, and airplanes, as well as vehicles or vehicle equipment used for military, construction, agricultural, or recreational purposes. Electronics used by the vehicle, a portion of the vehicle, or a driver or passenger of the vehicle can be considered vehicle electronics. Vehicle electronics can include electronics for engine management, ignition, radios, on-board computers, telematics, infotainment systems, and other portions of the vehicle. Vehicle electronics can be used with or through ignition and engine and transmission controls, which can be found in vehicles with internal combustion drive, such as gasoline-powered cars, trucks, motorcycles, boats, airplanes, military vehicles, forklifts, tractors, and excavators. Further, vehicle electronics can be used by or with related elements for controlling electrical systems found in hybrid and electric vehicles, such as hybrid or electric cars. For example, electric vehicles can use power electronics for primary propulsion motor control as well as managing battery systems. Also, autonomous vehicles rely almost entirely on vehicle electronics.

[0072] Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self- consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0073] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, functions or constructs of the same name

[0074] The disclosure also relates to an apparatus for performing the operations herein. This apparatus can be specially constructed for the intended purposes, or it can include a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program can be stored in a computer readable storage medium, such as any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

[0075] The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems can be used with programs in accordance with the teachings herein, or it can prove convenient to construct a more specialized apparatus to perform the method. The structure for a variety of these systems will appear as shown in the description below. In addition, the present disclosure is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages can be used to implement the teachings of the disclosure described herein.

[0076] The disclosure can be provided as a computer program product, or software, that can include a machine-readable medium having stored thereon instructions, which can be used to program a computer (or other electronic devices) to perform a process according to the present disclosure. The machine-readable medium can include any mechanism for storing information in a form accessible by a machine (e.g., computer), such as machine-readable (e.g., computer-readable) media, in some embodiments, machine-readable (e.g., computer-readable) media include machine- (e.g., computer-) readable storage media, such as read-only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory components, etc.

[0077] In the foregoing specification, embodiments of the disclosure have been described with reference to specific embodiments thereof. It will be evident that various modifications can be made to the disclosure without departing from the broader spirit and scope of embodiments of the disclosure as set forth in the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.

Claims

1. An apparatus comprising: Multiple electronic control units (ECUs) configured to control the drive of the vehicle; Multiple user interface (UI) elements configured to be operated by the driver of the vehicle to indicate the degree of control applied, the degree of control being at least partially based on the driver's interaction with at least one or any combination of steering control, braking control, or throttle control, the multiple UI elements being configured to: The measurement indicates the degree of control exerted by the driver; and The signal is transmitted to an ECU among the plurality of ECUs, wherein the ECU is configured to generate control signals for driving the vehicle based on the measured signal received from the UI element; and Advanced Driver Assistance Systems (ADAS) are configured as follows: Identify the driver's pattern of interacting with the UI element; Compare the described pattern with a predetermined model; and The plurality of ECUs are adjusted, at least in part, based on the comparison, so as to convert the signals measured by the UI elements into control signals for driving the vehicle, such that the driving of the vehicle matches the predetermined model.

2. The device of claim 1, wherein the ADAS is further configured to adjust the plurality of ECUs based on sensor data indicating environmental conditions of the vehicle, so as to convert the signal measured by the UI element into the control signal for driving the vehicle.

3. The device according to claim 2, wherein the ADAS is further configured to: Based on the predetermined model and sensor data indicating the environmental conditions of the vehicle, determine the response difference between the measured signals generated by the plurality of UI elements and the driving decisions autonomously generated by the ADAS; and The artificial neural network (ANN) is trained based on the differences in the responses to compare the patterns.

4. The device according to claim 1, wherein the ADAS is configured to: The transmitted signal, indicating the degree of control, is input into the artificial neural network (ANN); and At least one feature of the comparison is determined based on the output of the ANN.

5. The device of claim 4, wherein the ADAS is configured to train the ANN and adjust the ANN based on the comparison.

6. The device of claim 1, wherein the predetermined model is derived from a relevant model of a preselected safe driver, a relevant model of a driver with a preselected driver ability level, a relevant model of a driver with a preselected driving habit, or a relevant model of a driver with a preselected driving style, or any combination thereof.

7. The device of claim 1, wherein the plurality of UI elements include steering control, braking control, and throttle control.

8. The device of claim 7, wherein the ADAS is configured to change a transfer function for use by the ECU to control the drive of the vehicle based on the comparison, and wherein the transfer function includes at least one transfer function or derived from at least one transfer function for controlling at least one or any combination of the vehicle's steering mechanism, the vehicle's throttle mechanism, or the vehicle's braking mechanism.

9. The device of claim 8, wherein the plurality of UI elements includes transmission control, and wherein the degree of control exerted by the driver includes detected user interaction with the transmission control.

10. The device of claim 9, wherein the transfer function includes or is derived from a transfer function for controlling the transmission mechanism of the vehicle.

11. An apparatus comprising: Multiple electronic control units (ECUs) configured to control the drive of the vehicle; Multiple vehicle controls configured to be operated by the driver of the vehicle to indicate the degree of control applied, the degree of control being based at least in part on the interaction between the driver and at least one or any combination of steering control, braking control, or throttle control; Multiple sensors, configured as follows: Detect the degree of control exerted by the driver on the plurality of vehicle controls; and The signal indicating the detected level of control is transmitted to an ECU among the plurality of ECUs, wherein the ECU is configured to generate control signals for driving the vehicle based on the signals received from the plurality of sensors; and Advanced Driver Assistance Systems (ADAS) are configured as follows: Receive the transmitted signal indicating the detected degree of control exerted by the driver on the plurality of vehicle controls; A filter is generated for the driver by comparing the transmitted signal with a predetermined model; and The generated filter alters the transfer function used by the ECU to control the vehicle's drive, thereby matching the vehicle's drive to the predetermined model.

12. The device of claim 11, wherein the ADAS is configured to: Compare the changed transfer function with the predetermined transfer function; and The generation of the filter is adjusted based on the comparison between the changed transfer function and the predetermined transfer function.

13. The device of claim 12, wherein the ADAS is configured to: The transmitted signal, indicating the detected degree of control exerted by the driver on the plurality of vehicle controls, is input into an artificial neural network (ANN); and At least one feature of the filter is determined based on the output of the ANN.

14. The device of claim 13, wherein the ADAS is configured to train the ANN, and The ANN is adjusted based on the comparison between the changed transfer function and the predetermined transfer function.

15. The device of claim 12, wherein the predetermined transfer function is derived from a transfer function relating to a preselected safe driver, a transfer function relating to a driver with a preselected driver competence level, a transfer function relating to a driver with preselected driving habits, or a transfer function relating to a driver with a preselected driving style, or any combination thereof.

16. The device of claim 11, wherein the plurality of vehicle controls include steering control, braking control, and throttle control.

17. The device of claim 16, wherein the ADAS is configured to change a transfer function used by the ECU to control the drive of the vehicle based on the filter, and wherein the transfer function includes at least one transfer function or derived from at least one transfer function for controlling at least one or any combination of the vehicle's steering mechanism, the vehicle's throttle mechanism, or the vehicle's braking mechanism.

18. A method comprising: Signals indicating the degree of control exerted by the driver using multiple user interface (UI) elements within the vehicle are detected by multiple sensors in the vehicle. The signals indicating the degree of control exerted by the driver are measured through the plurality of UI elements; The measured signals are transmitted through the multiple UI elements to multiple electronic control units (ECUs) of the vehicle and the advanced driver assistance system (ADAS) of the vehicle; The ECU generates control signals for driving the vehicle based on the measured signals received from the plurality of UI elements; The ADAS identifies the driver's interaction patterns with the plurality of UI elements based on the measured signals received from the plurality of UI elements; The ADAS compares the pattern with a predetermined model. and The ADAS adjusts the plurality of ECUs at least in part based on the comparison in order to convert the signals measured by the UI elements into control signals for driving the vehicle, such that the driving of the vehicle matches the predetermined model.

19. The method of claim 18, further comprising: The ADAS generates signal modulation for the driver based on the comparison; and The ADAS adjusts the plurality of ECUs according to the generated signal modulation in order to convert the signal measured by the UI element into the control signal for driving the vehicle.

20. The method of claim 18, further comprising: The ADAS changes the transfer functions of the plurality of UI elements based on the comparison. and The ADAS adjusts the plurality of ECUs according to the modified transfer function in order to convert the signals measured by the UI element into control signals for driving the vehicle.

Images