202. The in-vehicle network 202 may include a medium through which the various control units, components, and/or systems (for example, the plurality of functional components 106, the ECU 108, the display device 110, the sensor system 112, the vehicle control system 114) of the vehicle 102 may communicate with each other. In accordance with an embodiment, the in-vehicle network 202 may exist in the vehicle 102 to connect various devices or components in the vehicle 102, in accordance with various wired and wireless communication protocols. Examples of the wired and wireless communication protocols for the in-vehicle network 202 may include, but are not limited to, a vehicle area network (VAN), a CAN bus, Domestic Digital Bus (D2B), Time-Triggered Protocol (TTP), FlexRay, IEEE 1394, Carrier Sense Multiple Access With Collision Detection (CSMA/CD) based data communication protocol, Inter-Integrated Circuit (I2C), Inter Equipment Bus (IEBus), Society of Automotive Engineers (SAE) J1708, SAE J1939, International Organization for Standardization (ISO) 11992, ISO 11783, Media Oriented Systems Transport (MOST), MOST25, MOST50, MOST150, Plastic optical fiber (POF), Power-line communication (PLC), Serial Peripheral Interface (SPI) bus, and/or Local Interconnect Network (LIN).

The in-vehicle network 202 may be linked with the sensor system 112, which may acquire the sensor information (shown in FIG. 3). The sensor information may be shared with the control circuitry 204 of the system 104 for further processing. The sensor information may include, for example, operational parameters associated with the vehicle 102 and the ambient information. Each of the operational parameters may correspond to a functional component of the plurality of functional components 106 of the vehicle 102. For example, a parameter such as AP position may correspond to an acceleration pedal of the vehicle 102. The ambient information may be associated with an environment outside of the vehicle 102.

The control circuitry 204 may include suitable logic, circuitry, and/or interfaces code that may be configured to execute program instructions associated with different operations to be executed by the system 104. The control circuitry 204 may include one or more specialized processing units. In an embodiment, such specialized processing units may be implemented as an integrated processor or a cluster of processors that perform the functions of the one or more specialized processing units, collectively. For example, the control circuitry 204 may include a microprocessor, a microcontroller, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a Field-Programmable Gate Array (FPGA), or any other digital or analog circuitry configured to interpret and/or to execute program instructions and/or process data. Examples of the control circuitry 204 may include a Central Processing Unit (CPU), a Graphical Processing Unit (GPU), an x86-based processor, an x64-based processor, a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, and/or other hardware processors.

The memory 206 may include suitable logic, circuitry, interfaces, and/or code that may be configured to store the program instructions executable by the control circuitry 204. In at least one embodiment, the memory 206 may be configured to store the machine learning model 208, the sensor information, and other information, such as a user profile and historical mode settings. The memory 206 may be a persistent storage medium, a non-persistent storage medium, or a combination thereof. Example implementations of the memory 206 may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and/or a Secure Digital (SD) card.

The machine learning model 208 may be a classifier model or a regression that may be trained to identify a relationship between inputs, such as features in a training dataset. For example, the machine learning model 208 may predict values of mode parameters associated with a selected driving mode of the vehicle 102 based on inputs (based on the sensor information). The machine learning model 208 may be defined by its hyper-parameters, for example, weights, cost function, input size, number of layers, and the like. After several epochs of the training on the feature information in the training dataset, the machine learning model 208 may be trained to output a prediction/classification result for a set of inputs. The prediction result may be indicative of a class label (in case of classification) or a continuous mode parameter value (in case of a regression task) for each input of the set of inputs (e.g., input features extracted from new/unseen instances).

The machine learning model 208 may include electronic data, which may be implemented as, for example, a software component of an application executable on the system 104. The machine learning model 208 may rely on libraries, external scripts, or other logic/instructions for execution by a processing device, such as the control circuitry 204. The machine learning model 208 may utilize code and routines configured to enable a computing device to perform one or more operations. Additionally, or alternatively, the machine learning model 208 may be implemented using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, the machine learning model 208 may be implemented using a combination of hardware and software. Examples of the machine learning model 208 may include, but are not limited to, a Multilayer Perceptron (MLP) regressor, a linear regression model, a logistic regression model, a random forest model, an artificial neural network, and a decision tree.

The functions or operations executed by the system 104, as described in FIG. 1, may be performed by the control circuitry 204. Operations executed by the control circuitry 204 are described in detail, for example, in FIGS. 3, 4A, 4B, 5, 6, 7A 7B, 8A, and 8B.

FIG. 3 is a block diagram that illustrates an exemplary sequence of operations to update values of mode parameters of a driving mode of a vehicle, in accordance with an embodiment of the disclosure. FIG. 3 is explained in conjunction with elements from FIG. 1 and FIG. 2. With reference to FIG. 3, there is shown a block diagram 300 that includes a sequence of operations from 302 to 310.

At 302, data acquisition may be performed. The system 104 may receive sensor information 302A that includes the operational parameters associated with the vehicle 102 and the ambient information associated with an environment outside the vehicle 102. The data acquisition 302 may be performed via the sensor system 112 that may include a plurality of sensors (not shown) integrated into the plurality of functional components 106 of the vehicle 102 and/or other non-functional components (e.g., chassis) of the vehicle 102. Each of the operational parameters may corresponds to a functional component of the plurality of functional components 106 of the vehicle 102. Example of the operational parameters associated with the vehicle 102 may include, but is not limited to, an AP position of the vehicle 102, a master cylinder pressure associated with a brake pedal of the vehicle 102, a steering angle associated with an electric power steering of the vehicle 102, and a plurality of suspension parameters associated with a suspension system of the vehicle 102.

The ambient information may include, for example, a condition of a road in an active route of the vehicle 102, a terrain type associated with the active route, an amount of precipitation on the road, a type of the road, a weather condition for a current location of the vehicle 102 in the active route.

At 304, a driving profile 312 may be determined. The system 104 may be configured to determine the driving profile 312 associated with the user of the vehicle 102, based on the sensor information 302A. Specifically, the driving profile 312 may include a mapping between input features (based on the operational parameters and the ambient information) and output variables such as the individual mode parameters associated with a driving mode (e.g., a smart mode) of the vehicle 102. Such mode parameters be used to may control the operation of the plurality of functional components 106 of the vehicle 102.

In accordance with an embodiment, the system 104 may learn the driving profile 312 by processing the sensor information 302A using a pre-trained machine learning model. Specifically, the system 104 may be configured to apply the pre-trained machine learning model (such as the machine learning model 208) on the sensor information 302A to determine the driving profile 312 associated with the user (e.g., the driver) of the vehicle 102.

In accordance with an embodiment, the system 104 may be further configured to acquire historical sensor data (not shown) associated with the vehicle 102, and the acquired data may be stored in the memory 206. The historical sensor data may include historical data points related to the operational parameters associated with the vehicle 102 and the ambient information associated with the environment around the vehicle 102. The system 104 may apply the machine learning model 208 on the historical sensor data to further determine the driving profile 312.

The driving profile 312 may include, for example, an AP map 312A associated with an AP component of the vehicle 102, a steering feedback 312B associated with a steering component of the vehicle 102, tuning information 312C associated with a suspension component of the vehicle 102, a level of regenerative braking 312D associated with a braking component of the vehicle 102, scene detection information 312E associated with the environment outside the vehicle 102.

The AP map 312A may indicate a desired output of a powertrain at a current speed value. The sensor system 112 may collect the acceleration pedal position data and a speed detection sensor (not shown) associated with the sensor system 112 may determine a speed of the vehicle 102. The AP map 312A may also indicate an amount of adjustment that may be required for an acceleration pedal 310A in accordance with the current speed of the vehicle 102. Typically, the user may depress the acceleration pedal 310A, which may indicate a desired adjustment of the acceleration pedal 310A required to maintain the speed of the vehicle 102 at the current value.

The steering feedback 312B may indicate a desired or an optimum steering angle that is to be maintained for a smooth steering of the vehicle 102. The sensor system 112 may collect a steering angle associated with the electric power steering 310C of the vehicle 102 and an angle sensor (not shown) associated with the sensor system 112 may determine a rotational effort or torque that the user applies to the steering wheel. The steering feedback 312B shows a value of adjustment for the steering angle in accordance with the rotational effort or torque applied by the user (as detected by the sensor system 112). The electric power steering 310C may include an electric motor which may be placed on a steering column of the electric power steering 310C. The electric motor may receive command from the ECU 108 regarding the value of adjustment in the steering angle to assist the user to steer the electric power steering 310C to a desired or optimum steering angle.

The tuning information 312C may indicate a desired or an optimum adjustment of the damping force of the suspension system 310D. The sensor system 112 may measure a value of damping force experienced by the suspension system 310D of the vehicle 102. The tuning information 312C shows an amount of adjustment in the damping force, in accordance with current speed, acceleration, and braking of the vehicle 102 (as detected by the sensor system 112). The tuning information 312C may be used to adjust a speed of compression or rebound of a spring, such as, but not limited to, a spiral spring, a leaf spring, or a coil spring associated with the suspension system 310D.

In an exemplary embodiment, the suspension system 310D may include an air suspension, which may include alteration of stiffness of the spring by adjusting the effective volume of the spring associated with the suspension system 310D. The adjustment of the effective volume of the spring may be achieved via a solenoid valve to connect the spring to an extra volume (for example, an accumulator). Further, the extra volumes may allow the spring rate to be altered based on the ambient information, which may correspond to the road condition. In order to stiffen the spring while the vehicle 102 may be cruising at a higher speed, the solenoid may disconnect the extra volume. In case a softer spring rate is required based on the road condition, the solenoid may connect the extra volume.

The level of regenerative braking 312D may represent the intended or ideal amount of braking that the driver can implement using the brake pedal 310B or deceleration of the vehicle 102 that may be caused due to a level of adjustment of the acceleration pedal 310A. Depending on the user's driving profile, the level of regenerative braking 312D may be modified. The level of regenerative braking 312D may indicate a level of braking that needs to be adjusted depending on the vehicle's acceleration, current speed, and/or the state of the road. The level of regenerative braking 312D in one example embodiment may be at least one of low, moderate, and standard. The vehicle 102 may brake less and may be preferred for an open or empty road if the level of regenerative braking 312D is lower; conversely, if the level of regenerative braking 312D is higher and the road has heavy traffic, the vehicle 102 may brake more and may be preferred for that situation.

The scene detection information 312E may indicate environmental conditions, such as, but not limited to, objects in the active route of the vehicle 102, at least one pothole on the road, an impact generated on the vehicle 102, a road condition, a terrain type, or a weather condition. The system 104 may apply adjustments to at least one of the plurality of functional components 106 while the sensor system 112 collects the ambient information. In an exemplary embodiment, the sensor system 112 may include a plurality of sensors (not shown) which may be configured to measure any physical impact on the vehicle 102. Based on a level of the measured impact experienced by the vehicle 102, the ECU 108 may activate the SRS 310E (such as airbag(s)).

In accordance with an embodiment, a plurality of sensors in the sensor system 112 may be configured to monitor parameters or events that may contribute to skidding, plowing and other loss-of-traction events. The ECU 108 may activate the VSA 310F to improve the user's driving experience by enhancing control and stability of the vehicle 102 during acceleration, braking or cornering of the vehicle 102. The VSA 310F may reduce throttle and brake individual wheels of the vehicle 102 to help restore the movement of the vehicle 102 along an intended path.

At 306, a mode selection may be performed. At any time-instant, the user or the system 104 may select a driving mode associated with the vehicle 102. The selected driving mode may be referred to as a smart mode. The smart mode may allow the system 104 to dynamically update values of various mode parameters associated with the selected driving mode based on the driving profile 312. Such parameters may be related to acceleration, steering, suspension, regenerative braking, safety, and the like.

At 308, values of the mode parameters may be updated. Once the driving mode (i.e., the smart mode) is selected, the system 104 may update the values of the mode parameters associated with the selected driving mode. The update may be performed based on the driving profile 312. As an example, the values of the mode parameters may be updated so that the value of acceleration is 10 in a comfort mode, the value of suspension in the normal mode is 16, the value of steering in the sport mode is 22, and the value of the level of regenerative braking in the comfort mode is 8 at a particular time instant. Other examples of the update are provided, for example, in FIGS. 4A, 4B, 5, 6, 7A,7B, 8A and 8B.

In accordance with an embodiment, the sensor system 112 may include sensors which may be configured to detect a cruise operation of the vehicle 102. The system 104 may be configured to detect an execution of the cruise control operation for a duration of a movement of the vehicle 102. Based on the detection, the update of the values of the mode parameters may be paused for the duration.

At 310, functional components may be controlled. The system 104 may be configured to control, via the ECU 108 of the vehicle 102, the plurality of functional components 106 of the vehicle 102 based on the updated values of the mode parameters. Even if the user is unable to define the desired driving experience, the vehicle 102 can quickly adapt to provide the desired driving experience by dynamically adjusting the values of the mode parameters (in close to real-time) and controlling the plurality of functional components 106 (in close to real-time).

In accordance with an embodiment, the display device 110 may include the GUI 110A that may be configured to render required information to a user (i.e., a driver/occupant of the vehicle). For example, the GUI 110A may render information regarding the selected driving mode and the updated values of the mode parameters.

FIGS. 4A and 4B are diagrams that collectively illustrate a graphic user interface (GUI) of a display device associated with a vehicle, in accordance with an embodiment of the disclosure. FIGS. 4A and 4B are explained in conjunction with elements from FIG. 1, FIG. 2, and FIG. 3. With reference to FIGS. 4A and 4B, there is shown a diagram 400 of the GUI 110A, which may be rendered on the display device 110 of the vehicle 102. The GUI 110A may display graphical elements that may correspond to user-selectable options for a view selection, a display control, and other interactive user-options. Additionally, the GUI 110A may render a selected driving mode, the mode parameters 110B associated with the selected driving mode, and the updated values of the mode parameters 110B.

The GUI 110A may include a first UI element 402, which may save updated values of the mode parameters 110B as an individual driving mode. In case a user feels comfortable with the updated values of the mode parameters 110B, the user may save the updated values as the individual driving mode by clicking on the first UI element 402. The GUI 110A may further include a second UI element 404, which may share the individual driving mode with a vehicle that may be associated with a person who is different from the user. For example, the user may share, via a platform, the individual driving mode with a friend's vehicle that supports custom driving modes. The platform may include one of a mail, a social media platform, a cloud server, a Vehicle-to-Vehicle (V2V) network, or a WAN network.

The GUI 110A may include a first option 406, which when selected, may lock at least one of the updated values of the mode parameters 110B. For example, the system 104 may receive a selection of the first option 406 and may ignore the update of at least one of the values of the mode parameters 110B based on the selection of the first option 406. In some instances, the first option 406 may be selected if the user is dissatisfied with the updated values of the mode parameters 110B and desires to lock some or all of the values of the mode parameters 110B. In some embodiments, the first option 406 may be automatically selected to lock the update of the values of the mode parameters 110B if the vehicle 102 is determined to be in a cruise mode or a snow mode.

The GUI 110A may include a slider UI element 408 for each of the mode parameters. Each segment of a length of the slider UI element 408 may represent a range of values associated with a type of mode for a corresponding mode parameter. In an exemplary embodiment, each segment may correspond to one of a comfort mode, a normal mode, or a sport mode. The slider UI element 408 may be indicative of the range of values of the mode parameters 110B. As shown, for example, the range for the comfort mode is lowest, the range for the sport mode is highest, and the range for the normal mode is between that of the comfort mode and the sport mode.

In accordance with an embodiment, the GUI 110A may further include an indicator 410, which may indicate the updated values of mode parameters 110B. The indicator 410 may move along the length of the slider UI element 408 as individual values of the mode parameters 110B are updated. Specifically, the update of each value of the mode parameters 110B may be followed by a movement of the indicator 410 along the slider UI element 408.

In accordance with an embodiment, the GUI 110A may include a prompt overlay 412, which may provide an option to switch to a preset driving mode that may be for a weather condition or a road traffic condition. In an embodiment, the system 104 may store the preset driving mode in the memory 206 and conditions to trigger the prompt overlay 412. At any time instant, the system 104 may receive the ambient information that may specify weather and road traffic conditions. If such conditions match the conditions stored in the memory 206 for the preset driving mode, the GUI 110A may display the prompt overlay 412. The system 104 may receive a selection of the option in the prompt overlay 412 and may select the preset driving mode based on the selection. After the selection of the preset driving mode, the system 104 may control the plurality of functional components 106 based on values of the mode parameters 110B associated with the preset driving mode. For example, the preset driving mode may be a snow mode for snowy conditions. A message may be rendered on the GUI 110A to indicate that the vehicle 102 is currently in the selected preset driving mode. While the preset mode (e.g., snow mode) is active, the updated values of the mode parameters 110B for the smart mode may not be used. If the vehicle 102 exits the preset driving mode, then the system 104 may switch to the updated values of the mode parameters 110B for the smart mode.

FIG. 5 is an exemplary scenario diagram that illustrates a change from a preset driving mode to a smart driving mode, in accordance with an embodiment of the disclosure. FIG. 5 is explained in conjunction with elements from FIG. 1, FIG. 2, FIG. 3, and FIGS. 4A and 4B. With reference to FIG. 5, there is shown a scenario diagram 500 that includes the GUI 110A at a time instant T1 and the GUI 110A at time instant T2.

At T1, an initial driving mode may be selected as a default or preset mode and values of the mode parameters may be adjusted to be in the range of the comfort mode.

At T2, the system 104 may receive the sensor information 302A. Based on the sensor information 302A, the system 104 may determine the driving profile 312 and may switch to a dynamic driving mode (also referred to as a smart mode). Based on the determined driving profile 312, the system 104 may update the values of the individual mode parameters. The update may be performed regularly or continuously in near real time while the vehicle 102 stays in the smart mode. The updated values of the mode parameters are depicted by new positions of the indicators. As an example, the value for steering may change from a value associated with the comfort mode to a value associated with the sport mode, while the value for suspension may change from a value associated with the comfort mode to a value associated with the normal mode.

FIG. 6 is an exemplary scenario diagram that illustrates an update in mode parameters in a smart driving mode at different time instant, in accordance with an embodiment of the disclosure. FIG. 6 is explained in conjunction with elements from FIG. 1, FIG. 2, FIG. 3, FIGS. 4A and 4B, and FIG. 5. With reference to FIG. 6, there is shown a scenario diagram 600 that includes the GUI 110A at a time instant T1 and the GUI 110A at time instant T2. At T1, the vehicle 102 may be in the smart mode and the values of the mode parameters corresponding to the smart mode are shown.

At T2, the system 104 may receive the sensor information 302A and may update the driving profile 312 based on the received sensor information 302A. Based on the updated driving profile 312, the system 104 may further update the values of the mode parameters, as shown (at T2). The updated values of the mode parameters are depicted by new positions of the indicators. In an exemplary embodiment, the value for the acceleration may update from a value in the comfort mode to a value in the sport mode, the value for steering may update from a value in the sport mode to a value in the normal mode, the value for suspension may update from a value in the normal mode to a value in the sport mode, and the value of regenerative braking may update to a new value within the comfort mode (for example, the value at T1 may be 2 and at time T2 may be 8).

FIGS. 7A and 7B are diagrams that collectively illustrate an exemplary scenario for updating values of mode parameters based on a winding road condition, in accordance with an embodiment of the disclosure. FIGS. 7A and 7B are explained in conjunction with elements from FIG. 1, FIG. 2, FIG. 3, FIGS. 4A and 4B, FIG. 5, and FIG. 6. With reference to FIGS. 7A and 7B, there is shown an exemplary scenario 700 that includes the GUI 110A at a time instant T1, a GUI 110A with a prompt overlay 702 for a winding road condition at a time instant T2, and the GUI 110A at time instant T3.

At T1, values of mode parameters may be adjusted as per the smart mode. As shown, for example, the GUI 110A includes the slider for each mode parameter (i.e., acceleration, steering, suspension, and braking) which is within a specific range of values. In the smart mode, the system 104 may update individual mode parameters based on the driving profile. In some instances, the user may be allowed to make changes in the values of the individual mode parameters via one or more options in the GUI 110A.

At T2, the system 104 may detect a winding road condition based on the ambient information (e.g., road images). Based on the detection, the system 104 may display, on the GUI 110A, the prompt overlay 702 which provides an option to switch to a preset driving mode, such as the sport mode.

At T3, the system 104 may receive, via the GUI 110A, a selection of the option in the prompt overlay 702. Based on the selection, the preset driving mode (such as the sport mode) may be selected. The system 104 may further control the plurality of functional components 106 based on values of the mode parameters associated with the preset driving mode. When a particular mode such as the sport mode is active, the system 104 may not update individual mode parameters of the particular mode based on the driving profile. The updated values of the mode parameters are depicted by new positions of the indicators in the GUI 110A.

FIGS. 8A and 8B are diagrams that collectively illustrate an exemplary scenario for updating values of mode parameters based on a steady drive condition, in accordance with an embodiment of the disclosure.