Claims:We claim:

1. A method (300) for monitoring vehicle tire pressure, comprising:
receiving (301), by an electronic control unit (ECU) (102) from a plurality of ECUs in the vehicle, one or more vehicle parameters in real-time;
predicting (302), by the electronic control unit (ECU) (102), tire pressure values based the one or more vehicle parameters using one or more artificial intelligence (AI) models, wherein the one or more AI models are trained to predict the tire pressure values, wherein training the one or more AI models comprises:
providing the one or more AI models with a training data set including the one or more vehicle parameters and tire pressure values;
determining a change in the tire pressure value for a change in the one or more vehicle parameters; and
generating a correlation between the one or more vehicle parameters and the tire pressure values;
and
displaying (303), by the electronic control unit (ECU), on a display unit (103) of the vehicle, the tire pressure values.

2. The method as claimed in claim 1, wherein the one or more artificial intelligence (AI) model is one of a machine learning (ML) or deep neural network (DNN).

3. The method as claimed in claim 1, wherein the one or more vehicle parameters comprises at least one of, a vehicle speed, a wheel speed, a steering wheel angle, a steering wheel torque, an engine torque, an engine revolution per minute (RPM), an acceleration pedal position, a brake command, a vehicle drag, a wheel radius.

4. The method as claimed in claim 1, wherein generating the correlation comprises:
obtaining one or more coefficients defining a relation between the tire pressure values and the one or more vehicle parameters; and
determining optimized coefficients from the one or more coefficients using the one or more AI models; wherein the optimized coefficients are used for predicting the tire pressure values in real-time.

5. The method as claimed in claim 1, wherein one or more AI models are trained with a large data set collected at different tire pressures and driving conditions.

6. The method as claimed in claim 5, wherein the large data set is generated using a measurements made during a plurality of test run at different driving conditions.

7. An electronic control unit (ECU) for monitoring vehicle tire pressure, comprising: a processor (203); and a memory (202); wherein the processor (203) is configured to:
receive from a plurality of ECUs in the vehicle, one or more vehicle parameters in real-time;
predict tire pressure values based the one or more vehicle parameters using one or more artificial intelligence (AI) models, wherein the one or more AI models are trained to predict the tire pressure values, wherein training the one or more AI models comprises:
provide the one or more AI models with a training data set including the one or more vehicle parameters and tire pressure values;
determine a change in the tire pressure value for a change in the one or more vehicle parameters; and
generate a correlation between the one or more vehicle parameters and the tire pressure values;
and
display on a display unit (103) of the vehicle, the tire pressure values.

5. The electronic controller unit (ECU) as claimed in claim 4, wherein the one or more artificial intelligence (AI) model is one of a machine learning (ML) or deep neural network (DNN).

6. The electronic controller unit (ECU) as claimed in claim 4, wherein generating the correlation comprises:
obtaining one or more coefficients defining a relation between the tire pressure values and the one or more vehicle parameters; and
determining optimized coefficients from the one or more coefficients using the one or more AI models; wherein the optimized coefficients are used for predicting the tire pressure values in real-time.

7. The electronic controller unit (ECU) as claimed in claim 4, wherein one or more AI models are trained with a large data set collected at different tire pressures and driving conditions.

8. The electronic controller unit (ECU) as claimed in claim 7, wherein the large data set is generated using a measurements made during a plurality of test run at different driving conditions. .
Dated this 27th December 2021

GOPINATH ARENUR SHANKARAJ
IN/PA 1852
Of K&S Partners
AGENT FOR THE APPLICANT
, Description:FORM 2
THE PATENTS ACT, 1970
[39 OF 1970]
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
[See section 10 and Rule 13]

TITLE: “A METHOD AND SYSTEM FOR TIRE PRESSURE MONITORING”

Name and Address of the Applicant: TATA MOTORS LIMITED, an Indian company having its registered office at Bombay House, 24 Homi Mody Street, Hutatma Chowk, Mumbai 400 001, Maharashtra

Nationality: Indian

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[001] The present disclosure relates in general to automobiles. Particularly the present disclosure relates to a system and method for monitoring tire pressure in vehicles using Artificial Intelligence (AI) Models.

BACKGROUND
[002] Monitoring accurate level of tire pressure is essential in vehicles to ensure safety and maximize the vehicles’ performance. Maintaining right level of tire pressure improves tire life of vehicle and provides better steering response, increases fuel efficiency, and provides an overall optimal driving experience.

[003] In existing vehicles, direct and/or indirect methods are used for monitoring tire pressure. In direct tire pressure monitoring systems, the dedicated pressure sensors are directly mounted on the wheels or tires of a vehicle for pressure measurement. Hence, such systems require additional sensor to measure the tire pressure. While in the indirect tire pressure monitoring the systems do not directly use air pressure sensors inside the tires, but by comparing vehicle parameters like wheel radius, relative wheel speeds via the Anti-lock Brake System (ABS) wheel speed sensors. Indirect tire pressure monitoring systems are not accurate as they use empirical formula for determining the tire pressure.

[004] The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgment or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY
[005] Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

[006] Disclosed herein is a method of monitoring vehicle tire pressure using Artificial Intelligence (AI) models. The method comprises receiving one or more vehicle parameters by an electronic control unit (ECU) from a plurality of ECUs in the vehicle in real-time. The one or more vehicle parameters comprises at least one of, a vehicle speed, a wheel speed, a steering wheel angle, a steering wheel torque, an engine torque, an engine revolution per minute (RPM), an acceleration pedal position, a brake command, Further, predicting tire pressure values based on the one or more vehicle parameters using one or more artificial intelligence (AI) models. The one or more AI models are trained to predict the tire pressure values. Training the one or more AI models comprises providing the one or more AI models with a training data set including the one or more vehicle parameters and tire pressure values. Further, determining a change in the tire pressure value for a change in the one or more vehicle parameters. Correlation between the one or more vehicle parameters and the tire pressure values is generated. Thereafter, displaying the tire pressure values on a display unit of the vehicle. Further, the present disclosure discloses an electronic controller unit (ECU) for monitoring vehicle tire pressure comprising a processor (203) and a memory (202). The processor (203) is configured to receive one or more vehicle parameters from a plurality of ECUs in the vehicle in real-time. The one or more vehicle parameters comprises at least one of, a vehicle speed, a wheel speed, a steering wheel angle, a steering wheel torque, an engine torque, an engine revolution per minute (RPM), an acceleration pedal position, a brake command, a vehicle drag, a wheel radius. Further, processor (203) is configured to predict tire pressure values based the one or more vehicle parameters using one or more artificial intelligence (AI) models, wherein the one or more AI models are trained to predict the tire pressure values. Training the one or more AI models comprises providing the one or more AI models with a training data set including the one or more vehicle parameters and tire pressure values. Further, determining a change in the tire pressure value for a change in the one or more vehicle parameters and generating a correlation between the one or more vehicle parameters and the tire pressure values. Further, generating a correlation between the one or more vehicle parameters and the tire pressure values comprises obtaining one or more coefficients defining a relation between the tire pressure values and the one or more vehicle parameters. Further, determining optimized coefficients from the one or more coefficients using the one or more AI models wherein the optimized coefficients are used for predicting the tire pressure values in real-time. The large data set is generated using measurements made during the plurality of test run at different driving conditions.
[007] Thereafter, displaying the tire pressure values on a display unit of the vehicle by the electronic control unit (ECU).

[008] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features may become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[009] The novel features and characteristic of the disclosure are set forth in the appended claims. The disclosure itself, however, as well as a preferred mode of use, further objectives, and advantages thereof, may best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. One or more embodiments are now described, by way of example only, with reference to the accompanying figures wherein like reference numerals represent like elements and in which:

[0010] Fig. 1 shows an environment illustrating monitoring vehicle tire pressure, in accordance with some embodiments of the present disclosure;

[0011] Fig. 2 shows a detailed block diagram of an ECU for monitoring vehicle tire pressure, in accordance with some embodiments of the present disclosure;

[0012] Fig. 3 shows a flowchart illustrating method steps for monitoring vehicle tire pressure, in accordance with some embodiments of the present disclosure; and

[0013] Fig. 4a shows an exemplary diagram of tire pressure value at deflated condition of tire in vehicle, in accordance with some embodiments of the present disclosure.

[0014] Fig. 4b shows an exemplary diagram of tire pressure value at inflated condition of tire in vehicle, in accordance with some embodiments of the present disclosure.

[0015] Fig. 5 shows an illustration of artificial neural networks (ANN) for monitoring tire pressure, in accordance with some embodiments of the present disclosure.

[0016] Fig. 6a depicts an exemplary simulation of a track for an AI model for monitoring tire pressure, in accordance with some embodiments of the present disclosure.

[0017] Fig. 6b depicts observed vehicle parameters for the track simulation, in accordance with some embodiments of the present disclosure.

[0018] Fig. 6c depicts the tire modelling for representing real world scenario in accordance with some embodiments of the present disclosure.

[0019] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it may be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes, which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION
[0020] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0021] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and may be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.

[0022] The terms “comprises”, “includes” “comprising”, “including” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” or “includes…a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[0023] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0024] Figure 1 shows an environment illustrating monitoring vehicle tire pressure. The environment includes a vehicle (101), an Electronic Control Unit (ECU) (102) present inside the vehicle (101), a display unit (103). In an embodiment, the ECU (102) monitors the vehicle tire pressure using one or more Artificial Intelligence (AI) models. The system (100) comprises receiving one or more vehicle parameters from a plurality of ECUs (not shown) of the vehicle (101). In an embodiment, the vehicle (101) comprises the plurality of ECUs. For example, a first ECU to monitor and control a steering, a second ECU to monitor and control a brake pedal, a third ECU to monitor and control an accelerator pedal, and so on. The ECU (102) may be a master ECU of the vehicle (101) such as the body control unit (BCU). The ECU (102) predicts the tire pressure value of the vehicle (101) and displays the tire pressure value on the display unit (103). In an embodiment, the display unit (103) may be a part of an instrument cluster of the vehicle (101). The tire pressure value may also be displayed on a user device such as a mobile phone.

[0025] Fig. 2 shows a detailed block diagram of the ECU (102). The ECU (102) may include Central Processing Unit (“CPU” or “processor”) (203) and a memory (202) storing instructions executable by the processor (203). The processor (203) may include at least one data processor for executing program components for executing user or system-generated requests. The memory (202) may be communicatively coupled to the processor (203). The ECU (102) further includes an Input/ Output (I/O) interface (201). The I/O interface (201) may be coupled with the processor (203) through which an input signal or/and an output signal may be communicated.

[0026] In some embodiments, ECU (102) comprises modules (204). The modules (204) may be stored within the memory (202). In an example, the modules (204) are communicatively coupled to the processor (203) configured in the computing system (100) and may also be present outside the memory (202) as shown in Fig. 2 and implemented as hardware. As used herein, the term modules (204) may refer to an application specific integrated circuit (ASIC), a Field Programmable Gate Array (FPGA), an electronic circuit, a processor (203) (shared, dedicated, or group), and memory (202) that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In some other embodiments, the modules (204) may be implemented using at least one of ASICs and FPGAs.

[0027] In an embodiment, an I/O interface (201) may enable communication between the ECUs (102) The I/O interface (201) may include at least one of, a CAN port, an Ethernet port, a Flex Ray port, and the like.

[0028] In one implementation, the modules (204) may include, for example, a communication module (205), a prediction module (206) and auxiliary module (207). It may be appreciated that such aforementioned modules (204) may be represented as a single module or a combination of different modules (204).

[0029] In an embodiment the communication module (205) is configured to receive one or more vehicle parameters either from the plurality of ECU (102) or sensors via the I/O interface (201). In an embodiment, the communication module (205) is configured to receive the one or more vehicle parameters in real-time. The one or more vehicle parameters includes, a vehicle speed, a wheel speed, a steering wheel angle, a steering wheel torque, an engine torque, an engine revolution per minute (RPM), an acceleration pedal position, a brake command, a and the like are collected from the plurality of ECUs (102). The communication module (205) may be configured to receive the one or more vehicle parameters at regular intervals. For example, the one or more parameters may be received in real-time, or every hour, or daily, or weekly, or monthly. As the user drives, the one or more vehicle parameters in real time are passed on to the Artificial Intelligence (AI) algorithm implemented by the ECU’s (102). Further, the communication module (205) provides the predicted tire pressure value for displaying on the display unit (103). In an embodiment, the one or more vehicle parameters may be obtained from the one or more sensors/ECU’s (102).

[0030] In an embodiment, the prediction module (206) predicts the target values (vehicle tire pressure values) based on the one or more vehicle parameters collected by the plurality of ECUs (102). For example, the prediction module (206) may include a regression model which is a supervised machine learning algorithm that is trained with a dataset to obtain optimum set of coefficients that map the input parameters to target variables (tire pressure values). A person skilled will appreciate that other AI models can also be used to predict the tire pressure value. The prediction module (206) is configured to train the one or more AI models with a large data set collected at different tire pressure values and driving conditions. The one or more AI models are developed and trained using measurement data (one or more parameters) collected for different tire pressure conditions during a training phase. The one or more AI models predicts the tire pressure and provides user with real-time pressure value. The one or more AI models are trained to obtain a set of coefficients that can define the dependency of tire pressure on the one or more vehicle parameters. Optimized set of coefficients are then deployed in the trained AI model which is further used for prediction in real-time scenarios. The prediction module (206) may include AI or Machine learning algorithms which incorporate all practical variabilities hence tend to be more accurate than other indirect tire pressure monitoring systems.

[0031] In an embodiment, the simulation model may be tested on a system to check the performance of real model when implemented in real world scenario. The model may comprise the components like ECU (102) or sensors for receiving the one or more vehicle parameters implemented by a software. The training data set includes the one or more vehicle parameters. The one or more vehicle parameters of the training data set may be generated using measurements made during a plurality of test run of the vehicle (101). The simulation model is only a representation of the terrain/ track of the test run. Also, the simulation model illustrates what parameters are measured during the test run. Further, predicting the vehicle tire pressure values using the one or more AI models corresponding to the vehicle parameters based on the parameters measured during the plurality of test run. Further, displaying the tire pressure value on the output interface of simulation software.

[0032] In an embodiment, the auxiliary module (207) may include a notification module or an alert module. In an embodiment, alert module alerts user/driver through screen flash along with a beep sound in case the pressure goes beyond/ below a threshold value. Based on the severity of the alert or notification, a navigation module may provide directions to nearest service station wherein, current vehicle is connected to internet through the ECU (102). For example, when front left tire pressure value of a vehicle goes low compared to the threshold value, the notification pops up on a driver mobile or on display unit (103) of a vehicle as “Low tire pressure” and alerts the driver to take necessary precautions.

[0033] Figure 3 shows a flowchart illustrating a method for monitoring vehicle tire pressure, in accordance with some embodiment of the present disclosure. The order in which the method (300) may be described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof.

[0034] At the step (301), receiving one or more parameters of the vehicle (101) from the plurality of ECUs (102) in real-time. For example, the one or more vehicle parameters can include a vehicle speed, a wheel speed, a steering wheel angle, a steering wheel torque, an engine torque, an engine revolution per minute (RPM), an acceleration pedal position, a brake command, a vehicle drag, a wheel radius and the like are collected from ECUs (102). The one or more vehicle parameters are recorded by the plurality of ECU (102) or different sensors and are communicated to the ECU (102) for predicting the vehicle tire pressure values.

[0035] At the step (302), predicting tire pressure values based the one or more vehicle parameters using one or more artificial intelligence (AI) models, wherein the one or more AI models are trained to predict the tire pressure values. For example, the one or more AI models can be supervised machine learning models or unsupervised models. In an embodiment, the one or more AI models are Machine Learning (ML) models or Artificial Neural Networks (ANN) models. Training the one or more AI models comprises providing the one or more AI models with a training data set including the one or more vehicle parameters and tire pressure values, wherein one or more AI models are trained with a large data set collected at different tire pressures and driving conditions. In an embodiment, the large data set is generated using measurements made during the plurality of test run at different driving conditions. Further, determining a change in the tire pressure value for a change in the one or more vehicle parameters and generating a correlation between the one or more vehicle parameters and the tire pressure values. Generating correlation comprises obtaining one or more coefficients defining a relation between the tire pressure values and the one or more vehicle parameters. Further, determining optimized coefficients from the one or more coefficients using the one or more AI models, wherein the optimized coefficients are used for predicting the tire pressure values in real-time.

[0036] At step (303), displaying the tire pressure values on the display unit (103) of the vehicle (101). The display unit (103) displays the vehicle tire pressure values for all wheels. For example, the tire pressure values are indicated in PSI (Pounds per square inch). In an embodiment, the alert or the notification module provides an alert to the driver when the tire pressure value deviates from defined threshold. The predicted tire pressure value from the prediction module (206) is sent to display unit (103) through appropriate connections between ECU’s (102) and the display unit (103) For example, if front left wheel is deflated and the display unit (103) displays a tire pressure value in psi and alerts the driver through screen flash & beep.

[0037] Figure 4a, is an illustration scenario of vehicle tire when in the deflated condition, where vehicle parameters are used to detect the tire pressure and to indicate the tire pressure value on the display unit (103). For example, when a vehicle rear right wheel is in deflated condition, the ECU (102) determines that the vehicle right wheel is deflated using the one or more vehicle parameters. Thus, an alert with the beep sound or screen flash is provided to take necessary precautions.

[0038] Figure 4b, is an illustration scenario of inflated vehicle tire where tire pressure values are in stable condition. The normal tire pressure value of vehicle is indicated on the display unit (103) For example, when a vehicle rear right wheel is in inflated condition, determined considering the one or more vehicle parameters when passed on to the one or more AI models for predicting tire pressure value and displayed on display unit (103) with the normal or stable tire pressure value.

[0039] Figure 5 is an exemplary AI model, wherein the one or more AI models can be artificial neural networks (ANN) model containing input nodes. For illustration purposes, the ANN is disclosed in the Figure 5. The ANN comprises one or more inputs nodes, one or more hidden nodes and one or more output nodes. The one or more inputs nodes are the one or more vehicle parameters of the vehicle (101). During the training stage, training data set is provided to the ANN. The one or more vehicle parameters of the training data set can be obtained using a simulation model as shown in Figure 6a. The training data set is generated using measurements made during the plurality of test run at different driving conditions. In an embodiment different terrains can be Mud Road, Tar Road, hill station, Ice Road and the like. In an embodiment track conditions may include values of track length, altitude, vehicle speed, maximum steering angle. The performance of the one or more parameters for different road conditions is recorded for creating a dataset.

[0040] Figure 6a depicts an exemplary simulation graph which is a representation for different track details. In an embodiment, vehicle tire pressure value may be tested by driving in the plurality of test run at different driving conditions. The different driving conditions can be simulated as shown in the Figure, and the vehicle (101) can be driven in such conditions during the plurality of test run. The track details comprise values of track length, altitude, vehicle speed, maximum steering angle. The simulation graph is obtained from the simulation model. During the training stage, training data set is provided to the ANN. The training data set includes the one or more vehicle parameters measured during the plurality of test run.

[0041] Figure 6b depicts observed vehicle parameters for the track simulation to monitor vehicle tire pressure. Likewise, the one or more vehicle parameters are measured during the plurality of test run. The one or more vehicle parameters may include vehicle speed, Steering Data, Wheel Speed Data, Engine Data and the like, wherein these parameters are received by different ECUs or sensors. For example, wheel speed value will be less when driven on mud road and faster when driven on highway. The one or more vehicle parameters are recorded for different terrain conditions and used by the prediction module (206) to predict the accurate tire pressure value using the one or more AI models.

[0042] Figure 6c shows the tire modelling for representing real world scenario. The simulation model can be tested for different terrain conditions. The simulated graphs in the Figure 6c clearly illustrate the parameters values at variable conditions. Variable conditions can depend upon different tire type, loading, road friction etc. The simulated graphs depict the performance of the one or more vehicle parameters for variability conditions. For example, tire model is tested for tire pressure versus deflection the graph is a straight line, wherein the tire pressure is in balanced condition.

[0043] Referring back to Figure 5, the one or more hidden layer is between input layers and output layers, where artificial neurons take in a set of weighted inputs and produce an output through an activation function. In an embodiment, the activation function helps in determining suitable output for a given input. A feedback network may also be provided increasing efficiency of the ANN. The output layer takes the inputs which are passed in from the one or more hidden layers before it and performs the calculations through its neurons and then the output (tire pressure value) is computed. For example, Bayesian Regularization Algorithm gives prediction accuracy of 99 percentile, by using input parameters like wheel Speeds, Vehicle Speed, Steering Wheel Angle, Steering Wheel Torque, Engine Torque, Braking Torque.

[0044] The method and system for vehicle tire pressure monitoring is of low cost, as the system and method are implemented using existing components and no additional sensors are required. The system is based on AI technique which provides accurate predicted tire pressure values. The proposed technology can be expanded to other commercial vehicles based on additional testing and AI model modification. The proposed system provides real-time display of tire pressure value by providing alert to the drivers if tire pressure values go beyond/ below the threshold value, so that driver can take necessary precautions.

[0045] The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

[0046] The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.

[0047] The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

[0048] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

[0049] When a single device or article is described herein, it may be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it may be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices, which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

[0050] The illustrated operations of Fig. 3 show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above-described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.

[0051] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is, therefore, intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

[0052] While various aspects and embodiments have been disclosed herein, other aspects and embodiments may be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

REFERRAL NUMERALS:

Reference number Description
100 System
101 Vehicle
102 ECU
103 Display Unit
201 I/O interface
202 Memory
203 Processor
204 Modules
205 Communication Module
206 Prediction Module
207 Auxiliary Module