Claims:We claim:

1.A method for providing an adaptive torque which consist of Electric Power Assisted Steering (EPAS) torque and a positive or negative correction torque, to control a steering system (103) of a moving vehicle (101), the method comprising:
continuously receiving, by an adaptive torque control system (105), plurality of inputs related to a moving vehicle (101) from one or more sensors (102) associated with the moving vehicle (101);
estimating, by the adaptive torque control system (105), a correction torque based on the plurality of inputs, using a pre-trained machine learning model;
performing, by the adaptive torque control system (105), correction of an EPAS torque using the correction torque to output an adaptive torque; and
providing, by the adaptive torque control system (105), the adaptive torque to a steering system (103) of the moving vehicle (101) to control the steering system (103) in real-time.

2. The method as claimed in claim 1, wherein estimating the correction torque comprises:
determining, by the adaptive torque control system (105), at least one variability condition related to the plurality of inputs from the one or more sensors (102); and
estimating, by the adaptive torque control system (105), the correction torque based on the at least one variability condition.

3. The method as claimed in claim 2, wherein the at least one variability condition indicates change in driving patterns of driver in the moving vehicle, and condition of at least one of the moving vehicle and a road in route of the moving vehicle.

4. The method as claimed in claim 1, wherein the pre-trained machine learning model is trained using historic information related to vehicle load conditions, road friction coefficient, gradient conditions, yaw rate conditions, lateral acceleration, pitch angle, one or more tire sizes, one or more tire pressures, vehicle speed conditions, one or more vehicle steering wheel angle conditions, one or more vehicle steering wheel torque conditions and one or more driving patterns, and corresponding adaptive torque values.

5. The method as claimed in claim 1, wherein the plurality of inputs indicates at least one of one or more vehicle steering wheel angle conditions, one or more vehicle steering wheel torque conditions, yaw rate, lateral acceleration, pitch angle, one or more tire sizes, one or more tire pressures, vehicle load conditions, road friction coefficient conditions, road gradient conditions, vehicle speed condition and one or more driving patterns.

6. An adaptive torque control system (105) provides an adaptive torque which consist of Electric Power Assisted Steering (EPAS) torque and a positive or negative correction torque, to control a steering system (103) of a moving vehicle (101), the adaptive torque control system (105) comprises:
a processor (106); and
a memory (108) communicatively coupled to the processor (106), wherein the memory (108) stores processor-executable instructions, which, on execution, cause the processor (106 to:
continuously receive plurality of inputs related to a moving vehicle (101) from one or more sensors (102) associated with the moving vehicle (101);
estimate a correction torque based on the plurality of inputs, using a pre-trained machine learning model;
perform correction of an EPAS torque using the correction torque to output an adaptive torque; and
provide the adaptive torque to a steering system (103) of the moving vehicle (101) to control the steering system (103) in real-time.

7. The adaptive torque control system (105) as claimed in claim 6, wherein the processor (106) is configured to estimate the correction torque by:
determining at least one variability condition related to the plurality of inputs from the one or more sensors (102); and
estimating the correction torque based on the at least one variability condition.

8 The adaptive torque control system (105) as claimed in claim 7, wherein the at least one variability condition indicates change in driving patterns of driver in the moving vehicle, and condition of at least one of the moving vehicle and a road in route of the moving vehicle.

9. The adaptive torque control system (105) as claimed in claim 6, wherein the pre-trained machine learning model is trained using historic information related to vehicle load conditions, road friction coefficient, gradient conditions, yaw rate conditions, lateral acceleration, pitch angle, one or more tire sizes, one or more tire pressures, vehicle speed conditions, one or more vehicle steering wheel angle conditions, one or more vehicle steering wheel torque conditions and one or more driving patterns, and corresponding adaptive torque values.

10. The adaptive torque control system (105) as claimed in claim 6, wherein the plurality of inputs indicates at least one of one or more tire sizes, one or more tire pressures, one or more vehicle steering wheel angle conditions, one or more vehicle steering wheel torque conditions, yaw rate, lateral acceleration, pitch angle, vehicle load conditions, road friction coefficient conditions, road gradient conditions, vehicle speed condition and one or more driving patterns.
, Description:TECHNICAL FIELD
[001] Present disclosure relates to managing a torque in automotive vehicles. In particular, the present disclosure deals with providing adaptive torque to control a steering system of a moving vehicle.

BACKGROUND
[002] Generally, Electric Power Assistance Steering (EPAS) systems are used in a motor vehicle for controlling a torque of steering wheel of the vehicle. Usually, while driving on a road in a vehicle, a driver may come across one or more conditions such as direction curves, road gradients, rough roads and so on. In an example scenario, consider the driver is driving on a bumpy road with varying gradient conditions. Such change in road conditions may result in steering wheel torque variations and critical maneuvers may be required to control the steering wheel torque variations. The steering wheel torque variations may also depend on, for example, vehicle load condition, road gradient conditions, driving patterns, tire pressure, tire size, road surface friction and so on. These critical maneuvers can lead to driver fatigue resulting in loss of control over the steer and cause accidents.

[003] Currently, steering system is controlled by balanced tuning approach, in which the steering torque is controlled based on inputs which are only speed of the vehicle, angle of steering wheel and torque of the steering wheel. Such steering torque may be provided based on predefined set of inputs. No additional inputs or factors are considered to determine or adjust the steering torque. Also, conventional systems for determining such steering wheel torque do not consider category of driver i.e., the driver is a male driver and a female driver.

[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] In an embodiment, the present disclosure relates a method of providing an adaptive torque which consist of Electric Power Assisted Steering (EPAS) torque and a positive or negative correction torque, to control a steering system of a moving vehicle. The method is performed by an adaptive torque control system. The method comprises continuously receiving plurality of inputs related to a moving vehicle from one or more sensors associated with the moving vehicle. The method comprises estimating a correction torque based on the plurality of inputs, using a pre-trained machine learning model. Further, the method comprises performing correction of the EPAS torque using the correction torque to output an adaptive torque. The adaptive torque is provided to the steering system of the moving vehicle to control the steering system in real-time.

[007] In an embodiment, the present disclosure relates to an adaptive torque control system for providing an adaptive torque which consist of Electric Power Assisted Steering (EPAS) torque and a positive or negative correction torque, to control a steering system of a moving vehicle. The adaptive torque control system comprises a memory and one or more processors. The processor is configured to continuously receive plurality of inputs related to the moving vehicle from one or more sensors associated with the moving vehicle. The processor is configured to estimate a correction torque based on the plurality of inputs, using a pre-trained machine learning model. Further, the processor is configured to perform correction of the EPAS torque using the correction torque to output an adaptive torque. The adaptive torque is provided to the steering system of the moving vehicle to control the steering system in real-time.

[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] Figure 1 shows an exemplary environment of an adaptive torque control system for controlling steering system of a moving vehicle, in accordance with some embodiments of the present disclosure;

[0011] Figure 2 shows a detailed internal block diagram of an adaptive torque control system for controlling steering system of a moving vehicle, in accordance with some embodiments of the present disclosure;

[0012] Figure 3 shows an exemplary embodiment of an adaptive torque control system for controlling steering system of a moving vehicle, in accordance with some embodiments of the present disclosure;

[0013] Figures 4a and 4b show exemplary plots illustrating results using an adaptive torque control system for controlling steering system of a moving vehicle, in accordance with some embodiments of the present disclosure;

[0014] Figure 5 shows a flowchart illustrating method for controlling steering system of a moving vehicle, in accordance with some embodiments of the present disclosure; and

[0015] Figure 6 shows a general-purpose computer system implementing an adaptive torque control system for controlling steering system of a moving vehicle, in accordance with embodiments of the present disclosure.

[0016] 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
[0017] 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.

[0018] 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.

[0019] The terms “comprise”, “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.

[0020] 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.

[0021] The present disclosure relates to a method and an adaptive torque control system for providing adaptive torque to a steering system of a moving vehicle. Proposed system and method focus on adaptively controlling the steering system of the moving vehicle in real-time. The adaptive torque control system is configured to continuously receive plurality of inputs from one or more sensors associated with the moving vehicle. A correction torque is estimated based on the plurality of inputs using a pre-trained machine learning model. The correction torque is used for correcting an Electric Power Assisted Steering (EPAS) torque to output an adaptive torque. The adaptive torque is provided to the steering system to control the steering system in real-time. Using the adaptive torque, steering wheel torque is controlled adaptively considering variations in driver pattern and conditions of road and vehicle. Thus, driving experience is improved and critical maneuver can be handled easily.

[0022] Figure 1 shows an exemplary environment (100) of an adaptive torque control system (105) for controlling a steering system (103) of a moving vehicle (101), in accordance with some embodiments of the present disclosure. The exemplary environment (100) comprises the moving vehicle (101), a communication network (104), the adaptive torque control system (105) and a EPAS torque provide unit (111). The moving vehicle (101) may include one or more sensors (102), and the steering system (103). The adaptive torque control system (105) may be associated with any vehicle which includes the steering system (103), and direction of movement of such vehicle is controlled by steering wheel of the steering system (103). The one or more sensors (102) may be sensor 1 (102a), sensor 2 (102b) ……. sensor N (102n) (as shown in the figure).

[0023] When the vehicle is moving on a road, the one or more sensors (102) associated with the moving vehicle (101) may be configured to measure plurality of conditions. In an embodiment, the plurality of conditions may relate to conditions of the moving vehicle (101), conditions of road and so on. In an embodiment, the one or more sensors (102) may be configured to sense driving patterns of a driver of the moving vehicle (101). In an embodiment, the one or more sensors (102) may include, but are not limited to, at least one of tire pressure sensor, steering wheel sensor, speed sensor, angle sensor, lateral acceleration sensor, yaw rate sensor and so on which helps in identifying vehicle loading condition, road condition and driver pattern. The tire pressure sensor may measure one or more parameters related to tires of the vehicle. The one or more parameters, may include, but are not limited to size and pressure of the tires. The measured load may vary based on number of passengers or load of baggage in the moving vehicle (101). The one or more conditions may include, but are not limited to, surface condition of the road, gradients of the road, road friction coefficient and so on. In an embodiment, the driving patterns may include, but is not limited to, tension of arms of the driver while driving the moving vehicle (101), pressure exerted by the driver on the steering wheel and so on. In an embodiment, the driving patterns may also include information about the driver being a male driver or a female driver. The angle sensor may be configured to determine pitch angle of the moving vehicle (101). The yaw rate sensor may be configured to determine yaw rate of the moving vehicle (101). The lateral acceleration sensor may be configured to determine the lateral acceleration of the moving vehicle (101). The steering wheel sensor may measure steering wheel torque, steering wheel angles and so on. The speed sensor may measure speed of the moving vehicle (101), speed of the steering wheel and so on. The plurality of conditions and the driving patterns measured by the one or more sensors (102) may be provided to the adaptive torque control system (105) as the plurality of inputs. In an embodiment, the one or more sensors (102) may be triggered to provide the plurality of inputs to the adaptive torque control system (105), when the vehicle is detected to move. The adaptive torque control system (105) is configured to control the steering system (103) in the moving vehicle (101) using the plurality of inputs.

[0024] The steering system (103) of the moving vehicle (101) comprises the steering wheel, steering column, intermediate shaft, rack and pinion gear and a suspension system which allows the driver to control direction of the moving vehicle (101) while driving. The steering wheel torque may be caused by several aspects including, for example, influence of vehicle engine load on steering, a variance of traction between two drive wheels and so on. The adaptive torque control system (105) may be configured to control the steering system (103) by providing the adaptive torque to the steering system (103).

[0025] The EPAS torque provide unit (111) may be configured to provide EPAS torque to the adaptive torque control system (105). The EPAS torque is determined using conventional systems and method. The EPAS torque is required to attenuate effort of the driver required to steer the vehicle i.e., the torque applied on the steering wheel. Steering angle sensor and torque sensor measure the position of steering wheel and the torque applied by the driver on the steering wheel, respectively. Later, this data is fed to the Electronic Control Unit (ECU) of the moving vehicle (101). In addition, ECU also monitors the overall speed of the moving vehicle (101). Based on this information, ECU calculates the EPAS torque which is required to be applied. The adaptive torque control system (105) is configured to correct the EPAS torque, which is provided to the steering system (103), by considering additional factors. The EPAS torque provide unit (111) is configured to communicate the EPAS torque that is determined for the steering system (103) in real-time to the adaptive torque control system (105).

[0026] In an embodiment, the adaptive torque control system (105) may communicate with the one or more sensors (102) and the steering system (103) via the communication network (104). In an embodiment, the communication network (104) may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), Controller Area Network (CAN), wireless network (e.g., using Wireless Application Protocol), the Internet, and the like.

[0027] The adapting torque control system (105) may be implemented in a variety of computing systems, such as a computer, a server, a network server, a cloud-based server, and the like. The adapting torque control system (105) may include at least one Central Processing Unit (also referred to as “CPU” or “processor”) (106) and a memory (108) storing instructions executable by the processor (106). The processor (106) may comprise at least one data processor for executing program components to execute user requests or system-generated requests. The memory (108) is communicatively coupled to the processor (106). The memory (108) stores instructions, executable by the processor (106), which, on execution, may cause the adaptive torque control system (105) to control the steering system (103) of the moving vehicle (101). In an embodiment, the memory (108) may include modules (109) and data (110). The modules (109) may be configured to perform the steps of the present disclosure using the data (110), to provide an adaptive torque to the steering system (103) of the moving vehicle (101). In an embodiment, each of the modules (109) may be a hardware unit which may be outside the memory (108) and coupled with the adapting torque control system (105). As used herein, the term modules (109) refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide described functionality. The modules (109) when configured with the described functionality defined in the present disclosure will result in a novel hardware. The adapting torque control system (105) further comprises an Input/ Output (I/O) interface (107). The I/O interface (107) is coupled with the processor (106) through which an input signal or/and an output signal is communicated. The input signal and the output signal may represent data received by the adaptive torque control system (105) and data transmitted by the adaptive torque control system (105), respectively. In an embodiment, the adaptive torque control system (105) may be configured to receive and transmit data via the I/O interface (107). The received data may comprise the plurality of inputs measured by the one or more sensors (102). The adaptive torque control system (105) may receive EPAS torque from the EPAS torque provide unit (111). The transmitted data may include the adaptive torque i.e., desired torque obtained by correcting the EPAS torque. The adaptive torque is provided to the steering system (103) of the moving vehicle (101) to control the steering system (103) in real-time.

[0028] In an embodiment, the adaptive torque control system (105) may either be integral part of the vehicle or may be a cloud-based system. In an embodiment, the adaptive torque control system (105) may be associated with one or more vehicles. The adaptive torque control system (105) may be configured to communicate with each of the one or more vehicles to control steering system (103) of respective vehicle.

[0029] Figure 2 shows a detailed internal block diagram of the adaptive torque control system (105) for controlling the steering system (103) of the moving vehicle (101), in accordance with some embodiments of the present disclosure. In some implementations, the adaptive torque control system (105) may include the memory (108) storing instructions, executable by the processor (106), which, on execution, may cause the adaptive torque control system (105) to provide the adaptive torque to the steering system (103) of the moving vehicle (101) to control the steering system (103) in real-time. In an embodiment, the memory (108) may include data (110) and one or more modules (109). In an embodiment, each of the one or more modules (109) may be a hardware unit which may be outside the memory (108) and coupled with the adaptive torque control system (105).

[0030] In an embodiment, the data (110) may include for example, one or more sensor data (201), pre-trained data (202), model data (203), EPAS torque data (204), adaptive torque data (205) and other data (206).

[0031] In an embodiment, the one or more sensor data (201) may include the plurality of inputs sensed by the one or more sensors (102). In an embodiment, the one or more sensors (102) are incorporated within the vehicle. In an embodiment, the sensor 1 may sense one or more vehicle steering wheel angle conditions, the sensor 2 may sense one or more vehicle steering wheel torque conditions, the sensor 3 may sense one or more tire sizes and one or more tire pressures, the sensor 4 may sense the vehicle load conditions, the sensor 5 may sense the vehicle speed condition, the sensor 6 may sense the one or more driving patterns and so on. These sensed data are stored as the one or more sensor data (201) in the memory (108) of the adaptive torque control system (105). The one or more sensor data (201) may be used by the modules (109) for providing the adaptive steering torque to the steering system (103) of the moving vehicle (101).

[0032] In an embodiment, the pre-trained data (202) may include trained, and validated data set related to the vehicle. In an embodiment, the pre-trained data (202) may be data that is used for training the pre-trained machine learning model. In an embodiment, the pre-trained data (202) may include historic information related to vehicle load conditions, road friction coefficient, gradient conditions, yaw rate conditions, lateral acceleration, pitch angle, one or more tire sizes, one or more tire pressures, vehicle speed conditions, one or more vehicle steering wheel angle conditions, one or more vehicle steering wheel torque conditions and one or more driving patterns, and corresponding adaptive torque values. In an embodiment, the corresponding adaptive torque values may be pre-defined values for every set of the historic information.

[0033] In an embodiment, the model data (203) may be output of the pre-trained machine learning model. In an embodiment, the model data (203) may be the correction torque. In an embodiment, the correction torque is estimated by the adaptive torque control system (105) based on the at least one variability condition related to the plurality of inputs. The correction torque is used to correct the EPAS torque. The correction torque may be of a positive value or a negative value.

[0034] In an embodiment, the EPAS torque data (204) (also referred to as the EPAS torque) may include output from the EPAS torque provide unit (111). The EPAS torque data (204) may be provided by the EPAS torque provide unit (111) to the adaptive torque control system (105) to determine the adaptive torque. In an embodiment, the EPAS torque may be determined based on the steering wheel angle, the steering wheel torque and the vehicle speed.

[0035] In an embodiment, the adaptive torque data (205) may include the adaptive torque which is output of the adaptive torque control system (105). The adaptive torque is obtained after performing the correction of the EPAS torque using the correction torque. The obtained adaptive torque is provided to the steering system (103) of the vehicle to control the steering system (103).

[0036] In an embodiment, the other data (206) may store data, including temporary data, temporary files, and temporary images, generated by the modules (109) for performing the various functions of the adaptive torque control system (105).

[0037] In one implementation, the modules (109) may include, for example, a receiving module (207), an estimating module (208), a correction module (209), an adaptive torque provide module (210) and other modules (211). It may be appreciated that such modules (109) may be represented as a single module or a combination of different modules.

[0038] Figure 3 shows an exemplary embodiment (300) of the adaptive torque control system (105) for controlling steering system (103) of the moving vehicle (101), in accordance with some embodiments of the present disclosure.

[0039] In an embodiment, the receiving module (207) is configured to continuously receive the plurality of inputs from the one or more sensors (102). The one or more sensors (102) are associated with the moving vehicle (101). In an example scenario, when the driver is driving the vehicle on the road, the one or more sensors (102) may sense the one or more conditions including, for example, at least one of one or more vehicle steering wheel angle conditions, one or more vehicle steering wheel torque conditions, yaw rate, lateral acceleration, pitch angle, one or more tire sizes, one or more tire pressures, vehicle load conditions, road conditions i.e., road friction coefficient and gradient condition, vehicle speed condition and so on. Further, one or more driving patterns may also be monitored in the moving vehicle (101). These one or more conditions and the one or more driving patterns are received as the plurality of inputs by the receiving module (207) of the adaptive torque control system (105) from the one or more sensors (102).

[0040] In an embodiment, the estimating module (208) is used to estimate the correction torque for the steering system (103) of the moving vehicle (101). The correction torque is used to control the steering system (103) in real-time. The correction torque may be estimated by determining at least one variability condition related to the plurality of inputs from the one or more sensors (102). In an embodiment, the at least one variability condition indicates change in the driving pattern, and change in the one or more conditions. In an embodiment, the at least one variability condition may be determined by comparing received driving patterns and the conditions with previous driving patterns and previous conditions related to the moving vehicle (101). One or more techniques, known to a person skilled in the art, may be implemented to determine the at least one variability conditions. Consider an example scenario where a driver is driving the vehicle on a road with different gradient conditions. Thus, variations may be determined in driver’s driving pattern, speed of the vehicle, steering wheel speed and so on. These variations are considered to estimate the correction torque. Thus, based on the determination of the at least one variability condition, the correction torque is estimated. In an embodiment, the correction torque may be of a positive value or a negative value, based on the correction that is to be performed on the EPAS torque. In an embodiment, the estimation module may implement the pre-trained machine learning model to evaluate the plurality of inputs and to output the correction torque.

[0041] In an embodiment, the pre-trained machine learning model may be created by gathering thousands of data related to vehicle. Such data may include, but is not limited to, the historic information related to vehicle load conditions, the road friction coefficient, the gradient conditions, the yaw rate conditions, the lateral acceleration, the pitch angle, the one or more tire sizes, the one or more tire pressures, the vehicle speed conditions, the one or more vehicle steering wheel angle conditions, the one or more vehicle steering wheel torque conditions and the one or more driving patterns, and the corresponding adaptive torque values. The gathered data set is screened based on the requirement. For instance, vehicle load with only two persons may be inefficient since vehicle load is minimal. In an embodiment, such inefficient data may be discarded. In an embodiment, the pre-trained machine learning model may be trained using at least one of a Levenberg M model, Scaled Conjugate model and Bayesian Regularization model. The Levenberg M model or Scaled Conjugate model or Bayesian Regularization model is used to train the model to provide the correction torque. The Levenberg M model or Scaled Conjugate model or Bayesian Regularization model selects data from data set randomly for validating trained data. With training and validation, accuracy and loss parameters are calculated. Once the model is optimized with the given set of data set and parameters, the pre-trained machine learning model may be used to estimate the correction torque based on the one or more sensor data (201). In an embodiment, the pre-trained machine learning model may be any artificial neural network, known to a person skilled in the art.

[0042] In an embodiment, the correction module (209) is configured to correct the EPAS torque from the EPAS torque provide unit (111). The correction is performed using the correction torque to output the adaptive torque. In an embodiment, the correction may be achieved by performing arithmetic operation. In an embodiment, the arithmetic operation may be addition, subtraction, multiplication, division and so on. For example, the value of the correction torque may be added to the EPAS torque to output the adaptive torque. In another example, the value of the correction torque may be subtracted to the EPAS torque to output the adaptive torque.

[0043] In an embodiment, the adaptive torque provide module (210) may be configured to provide the adaptive torque to the steering system (103) of the moving vehicle (101). The adaptive torque provide module (210) of the adaptive torque control system (105) continuously provides the adaptive torque to control the steering system (103) of the moving vehicle (101).

[0044] One or more modules 109 of the present disclosure function to control a torque of the steering system (103) of the moving vehicle (101) in real-time by providing the adaptive torque to the steering system (103). Also, the one or more modules (109) of the present disclosure function to estimate the correction torque based on the received plurality of inputs. The one or more modules (109) perform correction of the EPAS torque using the correction torque to output the adaptive torque.

[0045] In an embodiment, the other modules (211) may be configured to perform various miscellaneous functionalities of the adaptive torque control system (105).

[0046] Figures 4a and 4b show exemplary plots illustrating results using an adaptive torque control system (105) for controlling the steering system (103) of the moving vehicle (101), in accordance with some embodiments of the present disclosure. Referring to graph 4a, x-axis indicates Steering Wheel Angle (SWA) and y- axis indicates Hand Wheel Torque (HWT) of the moving vehicle (101). Two Handle Wheel Torque (HWT) lines shown in figure. 4a, one is depicted as desired HWT and another one may be depicted as without correction HWT line. The HWT line indicates variation of steering wheel torque with respect to steering wheel angle. There is gap between desired torque i.e., desired HWT line and current torque i.e., without correction HWT line. The adaptive torque control system (105) provides the adaptive torque to the steering system (103) of the moving vehicle (101) in moving condition to control the torque of the moving vehicle (101) in real-time. Thus, gap between the desired and the current torque is minimized as shown in the Figure 4b.

[0047] Figure 5 shows a flowchart illustrating method for controlling the steering system (103) of the moving vehicle (101), in accordance with some embodiments of the present disclosure. The method steps are performed using the pre-trained machine learning model. The order in which the method (500) 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.

[0048] At the step 501, the receiving module (207) continuously receives the plurality of inputs from the one or more sensors (102) associated with the moving vehicle (101). The plurality of inputs may include at least one of the one or more vehicle steering wheel angle conditions, the one or more vehicle steering wheel torque conditions, the yaw rate, the lateral acceleration, the pitch angle, the one or more tire sizes, the one or more tire pressures, the vehicle load conditions, the road conditions i.e., road friction coefficient, gradient condition, the vehicle speed condition and the one or more driving patterns.

[0049] At the step 502, the estimating module (208) estimates the correction torque based on the determined at least one variability condition associated with the plurality of inputs using the pre-trained machine learning model. The at least one variability condition depicts the change in value or status of the plurality of inputs. In an embodiment, the at least one variability condition indicates change in driving patterns of driver in the moving vehicle (101), and condition of at least one of the moving vehicle (101) and a road in route of the moving vehicle (101).

[0050] At the step 503, the correction module (209) performs correction of the EPAS torque using the correction torque to output the adaptive torque. The correction to EPAS torque may be achieved by performing arithmetic operation between the EPAS torque and the correction torque. In an embodiment, the arithmetic operation may include, but is not limited to, addition, subtraction, multiplication, division and so on.

[0051] At the step 504, the adaptive torque provide module (210) provides the adaptive torque to the steering system (103) of the moving vehicle (101) to control the steering system (103) in real-time. The adaptive torque indicates the desired torque for the steering system (103) in real-time.

[0052] ADVANTAGES:
The present invention provides an adaptive torque to a steering system of the moving vehicle to control the steering system in real-time. This controls steering wheel torque variations and improves driving experience. The proposed adaptive torque control system provides adaptive torque to the moving vehicle in any environmental conditions. By this, critical maneuver can be handled easily.

COMPUTER SYSTEM
[0053] Figure. 6 shows a general-purpose computer system implementing an adaptive torque control system (105) for controlling steering system (613) of a moving vehicle (612), in accordance with embodiments of the present disclosure. In an embodiment, the computer system (600) may be used to implement the method of providing adaptive torque to control the steering system (613) of the moving vehicle (612). The computer system (600) may comprise a central processing unit (“CPU” or “processor”) (602). The processor (602) may comprise at least one data processor for executing program components for dynamic resource allocation at run time. The processor (602) may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

[0054] The processor (602) may be disposed in communication with one or more input/output (I/O) devices (not shown) via I/O interface (601). The I/O interface (601) may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-(1394), serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.

[0055] Using the I/O interface (601), the computer system (600) may communicate with one or more I/O devices. For example, the input device (610) may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device/source, etc. The output device (511) may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.

[0056] In some embodiments, the computer system (600) is connected to the service operator through a communication network (611). The processor (602) may be disposed in communication with the communication network (611) via a network interface (603). The network interface (603) may communicate with the communication network (611). The network interface (603) may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/Internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network (611) may include, without limitation, a direct interconnection, e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, etc. Using the network interface (603) and the communication network (611), the computer system (600) may communicate with the one or more service operators.

[0057] In some embodiments, the processor (602) may be disposed in communication with a memory (605) (e.g., RAM, ROM, etc. not shown in Figure 5 via a storage interface (604). The storage interface (604) may connect to memory (605) including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

[0058] The memory (605) may store a collection of program or database components, including, without limitation, user interface (606), an operating system (607), web server (608) etc. In some embodiments, computer system (600) may store user/application data (606), such as the data, variables, records, etc. as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.

[0059] The operating system (607) may facilitate resource management and operation of the computer system (700). Examples of operating systems include, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM®OS/2®, MICROSOFT® WINDOWS® (XP®, VISTA®/7/8, 10 etc.), APPLE® IOS®, GOOGLETM ANDROIDTM, BLACKBERRY® OS, or the like.

[0060] In some embodiments, the computer system (600) may implement a web browser (not shown in Figure) stored program component. The web browser may be a hypertext viewing application, such as MICROSOFT® INTERNET EXPLORER®, GOOGLETM CHROMETM, MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers (708) may utilize facilities such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system (600) may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as Active Server Pages (ASP), ACTIVEX®, ANSI® C++/C#, MICROSOFT®, .NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system (700) may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®, MOZILLA® THUNDERBIRD®, etc.

[0061] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory (605) on which information or data readable by a processor (602) may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processors to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access memory (RAM), Read-Only memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

[0062] In an embodiment, the computer system (600) may receive the plurality of inputs from one or more sensors (614) associated with the movable vehicle (612) and EPAS torque form EPAS torque provide unit (615) through the communication network (611).

[0063] 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.
[0064] The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] The illustrated operations of Fig. 5 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.

[0069] 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.

[0070] 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 Exemplary environment
101 Moving vehicle
102 One or more sensors
103 Steering system
104 Communication network
105 Adaptive torque control system
106 Processor
107 I/O interface
108 Memory
109 Modules
110 Data
111 EPAS torque provide unit
201 One or more sensor data
202 Pre-trained data
203 Model data
204 EPAS torque data
205 Adaptive torque data
206 Other data
207 Receiving module
208 Estimating module
209 Correction module
210 Adaptive torque provide module
211 Other modules
600 Computer system
601 I/O interface
602 Processor
603 Network interface
604 Storage interface
605 Memory
606 User interface
607 Operating system
608 Web Server
609 Input Device
610 Output Device
611 Communication network
612 Moving vehicle
613 Steering system
614 One or more sensors
615 EPAS torque provide unit