SYSTEMS AND METHODS FOR PREDICTING ROAD WHEEL ANGLES

Abstract

A determination is made as to whether a rider torque, road wheel angle (RWA), yaw rate, and heading are in a steady state, whether the difference in speeds between the left and right rear wheels is less than a rear wheel speed threshold, and whether the difference in speeds between the left and right front wheels is less than a front wheel speed threshold. A further determination is made as to whether the rider torque, RWA, yaw rate, and heading are transient, whether the difference in speeds between the left and right rear wheels is greater than the rear wheel speed threshold, and whether the difference in speeds between the left and right front wheels is greater than the front wheel speed threshold.One of a predicted steady-state RWA, a predicted transient RWA, and the measured RWA is selected based on the specifications to determine a quantity of force to be applied to the steering wheel of the vehicle.

Description

INTRODUCTION The technical field generally concerns vehicles and specifically systems and methods for predicting road wheel angles. Electric power steering systems (EPS systems) are commonly used to assist a vehicle driver by providing additional force to the vehicle's steering wheel. When a driver applies torque to the steering wheel, a steering angle sensor (SAS) measures steering wheel angles corresponding to the application of that torque. The EPS system typically receives these measured steering wheel angles from the SAS. The amount of force applied to the steering wheel by the EPS system is based, in part, on these measured steering wheel angles. Accordingly, it is desirable to provide systems and methods for predicting road wheel angles in order to enable an evaluation of the EPS system and the SAS. Other desirable features and characteristics will become apparent from the following detailed description and the attached claims in conjunction with the attached drawings and the preceding technical field and background. DESCRIPTION Method for predicting a road wheel angle (RWA) in a vehicle, comprising making a first determination of whether a measured driver torque τkin is in a steady state, a measured RWA δkin is in a steady state, a measured yaw rate of the vehicle is in a steady state, a measured heading of the vehicle is in a steady state, a difference in the speeds of a left rear wheel and a right rear wheel is less than or equal to a rear wheel speed difference threshold, and a difference in the speeds of a left front wheel and a right front wheel is less than or equal to a front wheel speed difference threshold;Performing a second determination to ascertain whether the measured driver torque τk is transient, the measured RWA δk is transient, the measured yaw rate of the vehicle is transient, the measured heading of the vehicle is transient, the difference in speeds of the left rear wheel and the right rear wheel is greater than the rear wheel speed difference threshold, and the difference in speeds of the left front wheel and the right front wheel is greater than the front wheel speed difference threshold; selecting one of a predicted steady-state RWA δprd.ssk+N, a predicted transient RWA δprd.transk+N, and the measured RWA based on the first and second determinations to determine a quantity of force to be applied to a steering wheel of the vehicle; and applying the determined quantity of force to the steering wheel of the vehicle. In at least one embodiment, the method further comprises: performing a third determination of whether a sensor system of the vehicle has passed diagnostic tests; and selecting one of the predicted steady state RWA δprd.ssk+N', the predicted transient RWA δprd.transk+N and the measured RWA based on the first, second and third determinations to determine the amount of force to be applied to the steering wheel of the vehicle. In at least one embodiment, the method further comprises generating the predicted steady state RWA δprd.ssk+N using a first equation, wherein the first equation is: where: τ is no measured driver torque applied to the steering wheel at an actual time k, kτs is his steady-state driver torque gain, wherein the steady-state driver torque gain is a previously calibrated value, ϕ is no lean angle of the vehicle at the actual time k, kΦs is his steady-state lean angle gain, and kδs is his steady-state road wheel angle gain. In at least one embodiment, the method further comprises generating the predicted transient RWA δprdd.transk+N using a second equation, wherein the second equation is: where δ̇ is not an estimated road wheel angular rate, Δt is a time difference associated with the previous predicted transient RWA δprd.transk-1', and δprd.transk-1 is a previous predicted transient RWA. In at least one embodiment, the method further comprises generating the estimated road wheel angle rate δ̇k using a third equation, wherein the third equation is: where: τk-No is previously measured driver torque, where k is a current time and N is a previous time, kτtrans is an adaptive transient driver torque boost, τ̇k-No is a derivative of the previously measured driver torque, where k is the current time and N is the previous time, is a transient road wheel boost, δprd.transk-1 is a previously generated transient road wheel angle prediction, is a road wheel angle boost, ϕ is no lean angle of the vehicle, and kϕ is a lean angle boost. In at least one embodiment, the adaptive transient driving torque amplification kτtrans is a function of the vehicle's speed, the vehicle's acceleration / deceleration, and the driver's torque rate τ̇. In at least one embodiment, the method further comprises that the measured RWA is based on a measured steering angle of the vehicle's steering wheel, which is received by a steering angle sensor (SAS). The road wheel angle (RWA) prediction system comprises at least one processor and at least one memory that is communicatively coupled to the at least one processor. The at least one memory contains instructions which, when executed by the at least one processor, cause the at least one processor to perform the following: make a first determination as to whether a measured driver torque τkin is in a steady state, whether a measured RWA δkin is in a steady state, whether a measured yaw rate of a vehicle is in a steady state, and whether a measured heading of the vehicle is in a steady state.a difference in the speeds of a left rear wheel and a right rear wheel is less than or equal to a rear wheel speed difference threshold, and a difference in the speeds of a left front wheel and a right front wheel is less than or equal to a front wheel speed difference threshold; performing a second determination of whether the measured driver torque τk is transient, the measured RWA δk is transient, the measured yaw rate of the vehicle is transient, the measured heading of the vehicle is transient, the difference in the speeds of the left rear wheel and the right rear wheel is greater than the rear wheel speed difference threshold, and the difference in the speeds of the left front wheel and the right front wheel is greater than the front wheel speed difference threshold; selecting a steady state RWA δprd.ssk+N' predicted by aa predicted transient RWA δprd.transk+N and the measured RWA based on the first and second determinations to determine a quantity of force to be applied to a steering wheel of the vehicle; and applying the determined quantity of force to the steering wheel of the vehicle. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to: perform a third determination of whether a sensor system of the vehicle has passed diagnostic tests; and select one of the predicted steady state RWA δprd.ssk+N', the predicted transient RWA δprd.transk+N and the measured RWA based on the first, second and third determinations, in order to determine the amount of force to be applied to the steering wheel of the vehicle. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to generate the predicted steady state RWA δprd.ssk+N using a first equation, wherein the first equation is: where: τ is no measured driver torque applied to the steering wheel at a current time k, kτs is his steady-state driver torque gain, wherein the steady-state driver torque gain is a previously calibrated value, ϕ is no lean angle of the vehicle at the current time k, kΦs is his steady-state lean angle gain, and kδs is his steady-state road wheel angle gain. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to generate the predicted transient RWA δprd,transk+N using a second equation, wherein the second equation is: where δ̇̇̇ is not an estimated road wheel angular rate, Δt is a time difference associated with the previous predicted transient RWA δprd.transk-1, and δprd.transk-1 is a previous predicted transient RWA. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to generate the estimated road wheel angle rate δ̇̇̇k using a third equation, wherein the third equation is: where: τ̇k-No is previously measured driver torque, where k is a current time and N is a previous time, kτtrans is an adaptive transient driver torque boost, τ̇k-No is a derivative of the previously measured driver torque, where k is the current time and N is the previous time, is a transient road wheel boost, δprd.transk-1 is a previously generated transient road wheel angle prediction, kδ̇̇ is a road wheel angle boost, ϕ is a lean angle of the vehicle, and kϕ is a lean angle boost. In at least one embodiment, the adaptive transient driving torque amplification kTtrans is a function of the vehicle's speed, the vehicle's acceleration / deceleration, and the driver's torque rate τ̇. In at least one embodiment, the measured RWA is based on a measured steering angle of the vehicle's steering wheel, which is received by a steering angle sensor (SAS). A vehicle comprising a road wheel angle (RWA) prediction system, comprising at least one processor and at least one memory communicatively coupled to the at least one processor. The at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to: perform a first determination of whether a measured driver torque τkin is in a steady state, whether a measured RWA δkin is in a steady state, whether a measured yaw rate of the vehicle is in a steady state, and whether a measured heading of the vehicle is in a steady state.a difference in the speeds of a left rear wheel and a right rear wheel is less than or equal to a rear wheel speed difference threshold, and a difference in the speeds of a left front wheel and a right front wheel is less than or equal to a front wheel speed difference threshold; performing a second determination of whether the measured driver torque τk is transient, the measured RWA δk is transient, the measured yaw rate of the vehicle is transient, the measured heading of the vehicle is transient, the difference in the speeds of the left rear wheel and the right rear wheel is greater than the rear wheel speed difference threshold, and the difference in the speeds of the left front wheel and the right front wheel is greater than the front wheel speed difference threshold; selecting a steady state RWA δprd.ssk+N' predicted by aa predicted transient RWA δprd.cransk+N and the measured RWA based on the first and second determinations, to determine a quantity of force to be applied to a steering wheel of the vehicle; and applying the determined quantity of force to the steering wheel of the vehicle. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to: perform a third determination of whether a sensor system of the vehicle has passed diagnostic tests; and select one of the predicted steady state RWA δprd.ssk+N', the predicted transient RWA δprd.transk+N and the measured RWA based on the first, second and third determinations, in order to determine the amount of force to be applied to the steering wheel of the vehicle. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to generate the predicted steady state RWA δprd.ssk+N using a first equation, wherein the first equation is: where: τ is no measured driver torque applied to the steering wheel at a current time k, kτs is his steady-state driver torque gain, wherein the steady-state driver torque gain is a previously calibrated value, ϕ is no lean angle of the vehicle at the current time k, kΦs is his steady-state lean angle gain, and kδs is his steady-state road wheel angle gain. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to generate the predicted transient RWA δprd,transk+N using a second equation, wherein the second equation is: where δ̇ is not an estimated road wheel angular rate, Δt is a time difference associated with the previous predicted transient RWA δprd.transk-1, and δprd.transk-1 is a previous predicted transient RWA. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to generate the estimated road wheel angle rate δ̇̇k using a third equation, wherein the third equation is: where: τ̇k-No is previously measured driver torque, where k is a current time and N is a previous time, kτtrans is an adaptive transient driver torque boost, τ̇k-No is a derivative of a previously measured driver torque, where k is the current time and N is the previous time, kδ̇trans is a transient road wheel boost, δprd.transk-1 is a previously generated transient road wheel angle prediction, kδ̇̇ is a road wheel angle boost, ϕ is a lean angle of the vehicle, and kϕ is a lean angle boost. In at least one embodiment, the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to: select one of the predicted steady state RWA δprd.ssk+N', the predicted transient RWA δprd.transk+N and the measured RWA based on the first and second determinations for use during at least one of a steering angle sensor (SAS) failure and a silent electric power steering (EPS) failure. BRIEF DESCRIPTION OF THE FIGURES The exemplary embodiments are described below in conjunction with the following drawing figures, where the same reference numerals denote the same elements and where: Fig. 1 is a functional block diagram of a vehicle comprising a road wheel angle (RWA) prediction system according to at least one embodiment; Fig. 2 is a functional block diagram of a controller comprising an RWA prediction system according to at least one embodiment; and Fig. 3 is a flowchart representation of an exemplary method for performing an evaluation of the SAS system using an RWA prediction system according to at least one embodiment. DETAILED DESCRIPTION The following detailed description is merely exemplary and is not intended to limit applications and uses. Furthermore, there is no intention to be bound by any express or implied theory presented in the preceding technical section, background, summary, or the detailed description that follows. As used herein, the term module refers to an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or grouped), and memory executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components providing the described functionality. Embodiments of the present disclosure can be described herein with respect to functional and / or logical block components and various processing steps. It is understood that such block components can be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, or the like, which can perform a variety of functions under the control of one or more microprocessors or other control devices.Furthermore, the person skilled in the art will recognize that embodiments of the present disclosure can be practiced in connection with any number of systems and that the systems described herein are merely exemplary embodiments of the present disclosure. For the sake of brevity, conventional techniques relating to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and / or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in an embodiment of the present disclosure. With reference to Fig. 1, a functional block diagram of a vehicle 10 comprising a road wheel angle (RWA) prediction system 100 according to at least one embodiment is shown. The vehicle 10 generally comprises a chassis 12, a body 14, front wheels 16, and rear wheels 18. In the illustrated embodiment, the vehicle 10 is depicted as a passenger car; however, it is understood that the RWA prediction system 100 can be included and used in any other vehicle, including trucks, SUVs, recreational vehicles (RVs), etc. In various embodiments, the body 14 is arranged on the chassis 12 and essentially encloses components of the vehicle 10. The body 14 and the chassis 12 can together form a frame. The wheels 16-18 are rotatably coupled to the chassis 12 near each respective corner of the body 14. In various embodiments, the vehicle 10 is an autonomous or semi-autonomous vehicle that is automatically controlled to transport passengers and / or cargo from one place to another. For example, in one exemplary embodiment, the vehicle 10 is an automation system of so-called Level Two, Level Three, Level Four, or Level Five. Level Two automation means that the vehicle assists the driver with various driving tasks under driver supervision. Level Three automation means that, under certain circumstances, the vehicle can take over all driving functions. All major functions are automated, including braking, steering, and accelerating. At this level, the driver can disengage completely until the vehicle instructs the driver otherwise.A Level Four system indicates "high automation," which refers to the driving-mode-specific performance of an automated driving system in all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request for intervention. A Level Five system indicates "full automation," which refers to the full-time performance of an automated driving system in all aspects of the dynamic driving task under all road and environmental conditions that can be handled by a human driver. As shown, the vehicle 10 generally comprises a drive system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. The controller 34 is configured to implement an advanced driver assistance system (ADAS). The drive system 20 is configured to generate power to propel the vehicle. The drive system 20 may, in various embodiments, comprise an internal combustion engine, an electric machine such as a traction motor, a fuel cell propulsion system, and / or any other type of drive configuration. The transmission system 22 is configured to transmit power from the drive system 20 to the vehicle wheels 16-18 according to selectable rotational speed ratios.According to various embodiments, the transmission system 22 can comprise a multi-stage automatic transmission, a continuously variable transmission, or another suitable transmission. The braking system 26 is configured to provide braking torque to the vehicle wheels 16-18. In various embodiments, the braking system 26 can comprise friction brakes, brake-by-wire, a regenerative braking system such as an electric motor, and / or other suitable braking systems. The braking system 26 is configured to generate power to propel the vehicle. The steering system 24 is configured to influence the position of the vehicle wheels 16. Although, for illustrative purposes, it is shown in some embodiments considered within the scope of this disclosure as comprising a steering wheel and a steering column, the steering system 24 may not include a steering wheel and / or a steering column. The steering system 24 is configured to propel the vehicle. The steering system 24 comprises a steering column coupled, for example, by a rack and pinion or other mechanism (not shown), to an axle 50 associated with the front wheels 16. Alternatively, the steering system 24 may comprise a steer-by-wire system comprising actuators associated with each of the front wheels 16. The sensor system 28 comprises one or more detection devices 40a-40n that detect observable conditions of the external environment and / or the internal environment of the vehicle 10. The detection devices 40a-40n may include, but are not limited to, radars, lidar, global positioning systems, optical cameras, thermal imaging cameras, ultrasonic sensors and / or other sensors. The vehicle dynamics sensors provide vehicle dynamics data, including longitudinal speed, yaw rate, lateral acceleration, longitudinal acceleration, etc. The vehicle dynamics sensors can include wheel sensors that measure information relating to one or more wheels of the vehicle 10. In one embodiment, the wheel sensors include wheel speed sensors coupled to each of the wheels 16-18 of the vehicle 10. Furthermore, the vehicle dynamics sensors can include one or more accelerometers (provided as part of an inertial measurement unit (IMU)) that measure information relating to the acceleration of the vehicle 10. In various embodiments, the accelerometers measure one or more acceleration values ​​for the vehicle 10, including lateral and longitudinal acceleration and yaw rate. The actuator system 30 comprises one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, the drive system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may further include interior and / or exterior vehicle features, such as, but not limited to, doors, a trunk, and cabin features such as air conditioning, music, lighting, etc. (not numbered). The communication system 36 is configured to wirelessly communicate information to and from other entities 48, such as, but not limited to, other vehicles (vehicle-to-vehicle “V2V” communication), infrastructure (vehicle-to-infrastructure “V21” communication), remote systems, and / or personal devices. In one exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate over a wireless local area network (WLAN) using IEEE 802.11 standards or using cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communication (DSRC) channel, are also considered within the scope of this disclosure.DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards. The data storage device 32 stores data for use in the ADAS of the vehicle 10. In various embodiments, the data storage device 32 stores defined maps of the navigable environment. In various embodiments, the defined maps can be predefined by and received from a remote system. For example, the defined maps can be compiled by the remote system and communicated to the vehicle 10 (wirelessly and / or via a wired connection) and stored in the data storage device 32. As can be seen, the data storage device 32 can be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system. The controller 34 comprises at least one processor 44 and a computer-readable storage device or computer-readable storage medium 46. The processor 44 can be any custom or commercially available processor, central processing unit (CPU), graphics processing unit (GPU), auxiliary processor among several processors associated with the controller 34, a microprocessor-based semiconductor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable storage device or computer-readable storage medium 46 can, for example, include volatile and non-volatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM).KAM is a persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is switched off. The computer-readable memory device or computer-readable storage medium 46 can be implemented using any number of known memory devices, such as PROMs (programmable read-only memory), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or any other electrical, magnetic, optical, or combined memory devices capable of storing data, some of which are executable instructions used by the controller 34 in controlling the vehicle 10. The instructions can comprise one or more separate programs, each containing an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, perform logic, calculations, procedures, and / or algorithms to automatically control the components of the vehicle 10, and generate control signals to the actuator system 30 to automatically control the components of the vehicle 10 based on the logic, calculations, procedures, and / or algorithms. Although in Fig.While only one controller 34 is shown in Figure 1, embodiments of the vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and that interact to process the sensor signals, perform logic, calculations, procedures and / or algorithms, and generate control signals to automatically control features of the vehicle 10. In various embodiments, the controller(s) 34 are configured to implement ADAS. With reference to Fig. 2, a functional block diagram of a controller 34, comprising a smoke and heat exhaust ventilation (SHEV) prediction system 100 according to at least one embodiment, is shown. The controller 34 comprises at least one processor 44 and at least one memory 46. The at least one processor 44 is a programmable device comprising one or more instructions that are stored in or assigned to the at least one memory 46. The at least one memory 46 comprises instructions that the at least one processor 44 is configured to execute. The at least one memory 46 comprises an embodiment of the SHEV prediction system 100. The control unit 34 is configured to communicate with a sensor system 28, wheel speed sensors 200, electric power steering system (EPS) 202, and steering angle sensor (SAS) 204. The EPS system 202 is configured to communicate with the SAS 204. The control unit 34 may include additional components that facilitate the operation of the smoke and heat exhaust ventilation (SHEV) prediction system 100. With reference to Fig. 3, a flowchart representation of an exemplary method 300 for performing an assessment of the SAS system using a smoke and heat exhaust ventilation (SHEV) forecasting system according to at least one embodiment is shown. The method 300 is described with reference to an exemplary implementation of the SHEV forecasting system 100. As can be seen from the disclosure, the order of operation within the method 300 is not limited to sequential execution, as illustrated in Fig. 3, but can be carried out in one or more different sequences as required and in accordance with the present disclosure. At 302, the Road Wheel Angle (RWA) Prediction System 100 determines whether the sensors of a vehicle 10 have passed diagnostic tests. The sensors include, but are not limited to, a sensor system 28 and wheel speed sensors 200. The RWA Prediction System receives diagnostic test results associated with the sensors. Based on the received diagnostic test results, the RWA Prediction System 100 determines whether the sensors have passed the diagnostic tests. The sensor system 28 includes an inertial measurement unit (IMU) and a yaw rate sensor. The IMU is configured to provide a lean angle ϕk of the vehicle 10. The yaw rate sensor is configured to provide a yaw rate of the vehicle 10. The wheel speed sensors 200 are configured to provide wheel speeds of each of the wheels of the vehicle 10. If the RWA prediction system 100 determines that the sensors have failed the diagnostic tests, the RWA prediction system 100 is configured to generate a sensor failure diagnostic notification for display on a display device of the vehicle 10 at 304. When the RWA prediction system 100 determines that the sensors have passed the diagnostic tests, the RWA prediction system 100 is configured to determine whether to generate a predicted steady state RWA δprd.ssk+N, where k represents a current time and k+N represents a future time at 306. The RWA prediction system 100 is configured to determine whether to generate the predicted steady state RWA δprd.ssk+N when the RWA prediction system 100 determines that the following conditions have been met: a measured driver torque τk is in a steady state, a measured RWA δkin is in a steady state, a measured yaw rate of the vehicle 10 is in a steady state, a measured heading of the vehicle 10 is in a steady state, a difference in the speeds of a left rear wheel and a right rear wheel is less than or equal to a rear wheel speed difference threshold, and a difference in the speeds of a left front wheel and a right front wheel is less than or equal to a front wheel speed difference threshold. The measured driver torque τk is considered steady state if any change in the measured driver torque τk over a period of time is less than a driver torque difference threshold. The measured smoke and heat exhaust ventilation (SHEV) δk is considered steady state if any change in the measured SHEV δk over a period of time is less than a SHEV difference threshold. The measured yaw rate is considered steady state if any change in the measured yaw rate over a period of time is less than a yaw rate difference threshold. The measured heading is considered steady state if any change in the measured heading over a period of time is less than a heading difference threshold. If the RWA prediction system 100 determines that it will generate the predicted steady state RWA δprd.ssk+N based on the fulfillment of the conditions, the RWA prediction system 100 generates the predicted steady state RWA δprd.ssk+N at 308 and procedure 300 transitions to 316. The generation of the predicted steady state RWA δprd.ssk+N is described in more detail below. If the RWA prediction system 100 determines not to generate the predicted steady state RWA δprd.ssk+N as a result of one or more of the conditions not being met, the RWA prediction system 100 is configured to determine whether to generate a predicted transient RWA δprd.transk+N, where k represents a current time and k+N represents a future time at 310. The RWA prediction system 100 is configured to determine whether to generate the predicted transient RWA δprd.transk+N when the RWA prediction system 100 determines that the following conditions have been met: a measured driver torque τkist is transient, a measured RWA δ̇̇kist is transient, a measured yaw rate of vehicle 10 is transient, a measured heading of vehicle 10 is transient, a difference in the speeds of a left rear wheel and a right rear wheel is greater than the rear wheel speed difference threshold, and a difference in the speeds of a left front wheel and a right front wheel is greater than the front wheel speed difference threshold. The measured driver torque τk is considered transient if a change in the measured driver torque τk over a period of time is greater than the driver torque difference threshold. The measured RWA δk is considered transient if a change in the measured RWA δk over a period of time is greater than an RWA difference threshold. The measured yaw rate is considered transient if a change in the measured yaw rate over a period of time is greater than a yaw rate difference threshold. The measured heading is considered transient if a change in the measured heading over a period of time is greater than a heading difference threshold. If the RWA prediction system 100 determines not to generate the predicted transient RWA δprd.transk+N as a result of one or more of the conditions not being met, the RWA prediction system 100 is configured to use the measured RWA associated with the measured SAS at 312. The RWA prediction system 100 determines a quantity of force to be applied to a steering wheel of vehicle 10 using the measured RWA associated with the measured SAS and applies the determined quantity of force to the steering wheel of vehicle 10. If the RWA prediction system 100 determines to generate the predicted transient RWA δprd.transk+N based on the fulfillment of the conditions, the RWA prediction system 100 generates the predicted transient RWA δprd.transk+N at 314 and the procedure proceeds to 316. The generation of the transient RWA δprd.transk+N is described in more detail below. At 316, the smoke and heat exhaust ventilation (SHEV) prediction system 100 determines whether the difference between the predicted SHEV and the measured SHEV is less than an SHEV threshold. If the predicted SHEV is the predicted steady-state SHEV δprd.ssk+Nist, the SHEV prediction system 100 determines whether the difference between the predicted steady-state SHEV δprd.ssk+N and the measured SHEV is less than an SHEV threshold. If the predicted SHEV is the predicted transient SHEV δprd.transk+Nist, the SHEV prediction system 100 determines whether the difference between the predicted transient SHEV δprd.transk+N and the measured SHEV is less than the SHEV threshold. When the RWA prediction system 100 determines that the difference between the predicted RWA and the measured RWA is less than an RWA threshold, the RWA prediction system 100 instructs the EPS system 202 to use the measured RWA to determine the additional amount of force to be added to the steering wheel to assist the driver at 318. The measured RWA is based on the measured steering angle. The RWA prediction system 100 determines an amount of force to be applied to the steering wheel of vehicle 10 using the measured RWA and applies the determined amount of force to the steering wheel of vehicle 10. If the RWA prediction system 100 determines that the difference between the predicted RWA and the measured RWA is greater than the RWA threshold, the RWA prediction system 100 instructs the EPS system 202 to use the predicted RWA at 320 to determine the additional amount of force to be added to the steering wheel to assist the driver at 318. If the predicted RWA is the predicted steady-state RWA δprd.ssk+N, the RWA prediction system 100 instructs the EPS system 202 to use the predicted steady-state RWA δprd.ssk+N to determine the additional amount of force to be added to the steering wheel to assist the driver. The RWA prediction system 100 then applies the determined amount of force to the steering wheel of vehicle 10. If the predicted RWA is the predicted transient RWA δprd.transk+N, the RWA prediction system 100 instructs the EPS system 202 to use the predicted transient RWA δprd.cransk+N to determine the additional amount of force to be added to the steering wheel to assist the driver. The RWA prediction system 100 then applies the specified amount of force to the vehicle's steering wheel. The RWA prediction system 100 is configured to generate the predicted steady state RWA δprd.ssk+Nbei 308 of procedure 300. The EPS system 202 includes a torque sensor. The torque sensor is configured to detect a measured driver torque τk applied to a steering wheel by a driver at a current time k. The RWA prediction system 100 is configured to receive the measured driver torque τk applied to the steering wheel. The RWA prediction system 100 is configured to multiply the measured driver torque τk by a steady-state driver torque gain kτss divided by a steady-state road wheel angle gain kδss to generate a first value. The stationary driver torque gain kτss and the stationary road wheel angle gain kδsss are previously calibrated values ​​that are stored in the RWA prediction system 100. The smoke and heat exhaust ventilation (SHEV) prediction system 100 is configured to receive a roll angle ϕk of the vehicle 10 from a sensor system 28. The sensor system 28 includes an inertial measurement unit (IMU). The IMU detects the roll angle ϕk of the vehicle 10. The SHEV prediction system 100 is configured to calculate a sine of the roll angle sin ϕk. The SHEV prediction system 100 is configured to multiply the sine of the roll angle sin ϕk by a stationary roll angle gain kΦss divided by a stationary road wheel angle gain kδss to generate a second value. The stationary roll angle gain kΦss and the stationary road wheel angle gain kδsss are previously calibrated values ​​stored in the SHEV prediction system 100. The RWA prediction system 100 is configured to generate the predicted steady state RWA δprd.ssk+N by subtracting the second value from the first value of a first value. The generation of the predicted steady state RWA δprd.ssk+N using the steps detailed above is represented by the following equation: The RWA prediction system 100 is configured to generate the predicted transient RWA δprd.transk+N at 314 of procedure 300. The RWA prediction system 100 has previously received a previously measured driver torque τ̇k-N from the EPS system 202, where k is the current time and N is a previous time. The RWA prediction system 100 is configured to multiply the previously measured driver torque τk-N by an adaptive transient driver torque gain kτtrans to generate an initial value (τk-N· kτtrans). The adaptive transient driver torque gain kτtrans is a function of the vehicle speed 10, the vehicle acceleration / deceleration 10, and the driver torque rate τ̇. The RWA prediction system 100 is configured to multiply a derivative of the previously received driver torque τ̇k-N by a transient road wheel gain to generate a second value (τ̇k-N· kδtranṡ). The transient road wheel gain is a previously calibrated value stored in the RWA prediction system 100. The RWA prediction system 100 is configured to multiply a previously generated transient road wheel angle prediction δprd.transk-1 with a transient road wheel angle gain kδ̇ to generate a third value (δprd.transk-1· kδ). The transient driving torque gain kτtrans is the previously calibrated value stored in the RWA prediction system 100. The RWA prediction system 100 is configured to receive a tilt angle ϕk from the IMU of the sensor system 28, calculate a sine of the tilt angle sin ϕk and multiply the sine of the tilt angle sin ϕk with a transient angle gain kϕtrans to generate a fourth value (sin ϕk· kϕ). The RWA prediction system 100 is configured to add the third value (δprd.transk-1· kδ) to the fourth value (sin ϕk· kϕ) to generate a fifth value (δprd.transk-1· kδ+ sin ϕk· kϕ). The RWA prediction system 100 is configured to add the first value (τk-N· kτtrans) to the second value (τ̇k-N· kδ̇trans) and to subtract the fifth value (δprd.transk-1· kδ+ sin ϕk· kϕ) to generate a sixth value [τk-N· kτtrans+ τ̇k-N· kδtrans- (δprd.transk-1· kδ+ sin ϕk· kϕ)]. The RWA prediction system 100 is configured to divide the sixth value [τk-N· kτtrans+ τ̇k-N· kδ̇trans- (δprd.transk-1· kδ+ sin ϕk· kϕ)] by a transient road wheel angle gain kδtrans to produce an estimated road wheel angle rate δ̇̇̇k. The generation of the estimated road wheel angle rate δ̇̇k using the steps detailed above is represented by the following equation: The RWA prediction system 100 is configured to calculate the predicted transient RWA δprd.transk+N using the following equation: where δ̇̇k is the estimated road wheel angular rate, Δt is a time difference between the previous predicted transient RWA δprd.transk-1 and the updated predicted transient RWA δprd.transk+N, and δprd.transk-1 is the previous predicted transient RWA. In at least one embodiment, the RWA prediction system 100 is configured to select one of the predicted steady state RWA δprd.ssk+N', the predicted transient RWA δprd.transk+N and the measured RWA based on the first and second determinations for use during at least one of a steering angle sensor (SAS) failure and a silent electric power steering (EPS) failure. While at least one exemplary embodiment has been presented in the preceding detailed description, it is understood that a large number of variations exist. It is also understood that the exemplary embodiment or embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the preceding detailed description provides the person skilled in the art with a suitable roadmap for implementing the exemplary embodiment or embodiments. It is understood that various modifications to the function and arrangement of elements can be made without deviating from the scope of the disclosure as set forth in the appended claims and their legal equivalents.

Claims

Method for predicting a road wheel angle (RWA) in a vehicle, comprising: making a first determination of whether a measured driver torque τkin is in a steady state, a measured RWA δkin is in a steady state, a measured yaw rate of the vehicle is in a steady state, a measured heading of the vehicle is in a steady state, a difference in the speeds of a left rear wheel and a right rear wheel is less than or equal to a rear wheel speed difference threshold, and a difference in the speeds of a left front wheel and a right front wheel is less than or equal to a front wheel speed difference threshold;Performing a second determination to ascertain whether the measured driver torque τk is transient, the measured RWA δ̇̇k is transient, the measured yaw rate of the vehicle is transient, the measured heading of the vehicle is transient, the difference in speeds of the left rear wheel and the right rear wheel is greater than the rear wheel speed difference threshold, and the difference in speeds of the left front wheel and the right front wheel is greater than the front wheel speed difference threshold; selecting one of a predicted steady-state RWA δprd.ssk+N', a predicted transient RWA δprd.transk+N, and the measured RWA based on the first and second determinations to determine a quantity of force to be applied to a steering wheel of the vehicle; and applying the determined quantity of force to the steering wheel of the vehicle. The method of claim 1, further comprising: performing a third determination of whether a sensor system of the vehicle has passed diagnostic tests; and selecting one of the predicted steady state RWA δprd.ssk+N', the predicted transient RWA δprd.transk+N and the measured RWA based on the first, second and third determinations to determine the amount of force to be applied to the steering wheel of the vehicle. The method of claim 1, further comprising: generating the predicted steady state RWA δprd.ssk+N using a first equation, wherein the first equation is: δprd.ssk+N = τk ⋅ kτssk δss − sin ϕk ⋅ kϕssk δss where: τ k a measured driver torque that is applied to the steering wheel at a current time k, k τss a stationary driver torque gain, where the stationary driver torque gain is a previously calibrated value, ϕ k a tilt angle of the vehicle at the current time k is, k ss a stationary tilt angle amplification, and k δss a stationary road wheel angle enhancement. The method of claim 1, further comprising: generating the predicted transient RWA δprd,transk+N using a second equation, wherein the second equation is: δprd .trans k + N = δ˙ k Δ t + δprd . trans k − 1 where δ̇̇ k an estimated road wheel angle rate is, Δt is a time difference that corresponds to the previously predicted transient RWA δ prd.transk-1 , is associated, and δ prd.transk-1 a previously predicted transient RWA. The method of claim 4, further comprising: generating the estimated road wheel angular rate δ̇̇k using a third equation, wherein the third equation is: δ ˙ k = τ k − N ⋅ k τ trans + τ ˙ k − N ⋅ k δ ˙ trans − ( δ prd .trans k − 1 ⋅ k δ + sin ϕ k ⋅ k ϕ ) k δ ˙ where: τ kN a previously measured driver torque, where k is a current time and N is a previous time, k τtrans an adaptive transient driver torque boost is, τ̇ kN a derivative of the previously measured driver torque, where k is the current time and N is the previous time, k δtrans a transient road wheel reinforcement is, δ prd.transk-1 a previously generated transient road wheel angle prediction is, k δ̇̇ a road wheel angle enhancement is, ϕ k a vehicle's tilt angle, and k ϕ a tilt angle increase. Method according to claim 5, wherein the adaptive transient driving torque amplification kτtrans is a function of a vehicle speed, a vehicle acceleration / deceleration and a driver torque rate τ̇. Method according to claim 1, wherein the measured RWA is based on a measured steering angle of the vehicle's steering wheel received by a steering angle sensor (SAS). Road wheel angle (RWA) prediction system, comprising: at least one processor;and at least one memory that is communicatively coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to: perform a first determination of whether a measured driver torque τkin is in a steady state, a measured RWA δkin is in a steady state, a measured yaw rate of a vehicle is in a steady state, a measured course of the vehicle is in a steady state, a difference in the speeds of a left rear wheel and a right rear wheel is less than or equal to a rear wheel speed difference threshold, and a difference in the speeds of a left front wheel and a right front wheel is less than or equal to a front wheel speed difference threshold;Performing a second determination to ascertain whether the measured driver torque τk is transient, the measured RWA δ̇̇k is transient, the measured yaw rate of the vehicle is transient, the measured heading of the vehicle is transient, the difference in speeds of the left rear wheel and the right rear wheel is greater than the rear wheel speed difference threshold, and the difference in speeds of the left front wheel and the right front wheel is greater than the front wheel speed difference threshold; selecting one of a predicted steady-state RWA δprd.ssk+N', a predicted transient RWA δprd.transk+N, and the measured RWA based on the first and second determinations to determine a quantity of force to be applied to a steering wheel of the vehicle; and applying the determined quantity of force to the steering wheel of the vehicle. System according to claim 8, wherein the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to: perform a third determination as to whether a sensor system of the vehicle has passed diagnostic tests; and select one of the predicted steady state RWA δprd.ssk+N', the predicted transient RWA δprd,transk+N and the measured RWA based on the first, second and third determinations to determine the amount of force to be applied to the steering wheel of the vehicle. System according to claim 8, wherein the at least one memory further comprises instructions which, when executed by the at least one processor, cause the at least one processor to do the following: Generate the predicted steady state RWA δprd.ssk+N using a first equation, wherein the first equation is: δprd.ssk+N = τk ⋅ kτssk δss − sin ϕk ⋅ kϕssk δss where: τ k a measured driver torque that is applied to the steering wheel at a current time k, k τss a stationary driver torque gain, where the stationary driver torque gain is a previously calibrated value, ϕ k a tilt angle of the vehicle at the current time k is, k Φss a stationary tilt angle amplification, and k δss a stationary road wheel angle enhancement.

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