System and method for mitigating brake traction and / or torque steering

Abstract

A method for mitigating undesired steering torque includes determining whether a vehicle is subject to a braking pull in response to determining that the vehicle is braking; determining whether the vehicle is moving according to driver intent in response to determining that the vehicle is subject to a braking pull; learning functional coefficients of rack force impacts due to braking pulls in response to determining that the vehicle is moving according to driver intent; predicting a rack load effect due to a braking pull in response to determining that the learned maturity is achieved; and generating a handwheel actuator command configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. Provisional Patent Application No. 63 / 594,727 filed on October 31, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to steering systems and, more particularly, to systems and methods for mitigating adverse driver feel due to brake pull and / or torque steer. Background Art

[0004] Vehicles such as automobiles, trucks, SUVs, crossovers, minivans, boats, airplanes, all-terrain vehicles, recreational vehicles, or other suitable forms of transportation typically include a steering system, such as an electronic power steering (EPS) system, a steer-by-wire (SbW) system, a hydraulic steering system, or other suitable steering system. The steering system of such a vehicle typically controls various aspects of the vehicle's steering, including providing steering assistance to the operator of the vehicle, controlling the steering wheel of the vehicle, etc. Summary of the invention

[0005] The present disclosure relates generally to vehicle steering systems.

[0006] One aspect of the disclosed embodiment includes a system for mitigating undesirable steering torque. The system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: in response to determining that the vehicle is braking, determine whether the vehicle is subject to brake tug; in response to determining that the vehicle is subject to brake tug, determine whether the vehicle is moving according to driver intent; in response to determining that the vehicle is moving according to driver intent, learn a function coefficient of rack force impact due to brake tug; in response to determining that the learning maturity is achieved, predict a rack load effect due to brake tug; and generate a handwheel actuator command, the handwheel actuator command being configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

[0007] Another aspect of the disclosed embodiment includes a method for mitigating undesired steering torque. The method includes: in response to determining that the vehicle is braking, determining whether the vehicle is experiencing brake pull; in response to determining that the vehicle is experiencing brake pull, determining whether the vehicle is moving according to driver intent; in response to determining that the vehicle is moving according to driver intent, learning a function coefficient of a rack force impact due to brake pull; in response to determining that a learning maturity is achieved, predicting a rack load effect due to brake pull; and generating a handwheel actuator command, the handwheel actuator command being configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

[0008] Another aspect of the disclosed embodiment includes an apparatus for mitigating undesirable steering torque. The apparatus includes a controller configured to: determine whether the vehicle is moving according to driver intent in response to determining that the vehicle is experiencing brake pull; learn a function coefficient of a rack force impact due to brake pull in response to determining that the vehicle is moving according to driver intent; predict a rack load effect due to brake pull; and generate a handwheel actuator command based on the predicted rack load effect.

[0009] These and other aspects of the present disclosure are disclosed in the following detailed description of the embodiments, the appended claims and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The present disclosure is best understood when the following detailed description is read in conjunction with the accompanying drawings. It should be emphasized that, according to common practice, the various features of the drawings are not drawn to scale. Instead, the sizes of the various features are arbitrarily enlarged or reduced for clarity.

[0011] Figure 1 A vehicle in accordance with the principles of the present disclosure is generally shown.

[0012] Figure 2A A controller according to the principles of the present disclosure is generally shown.

[0013] Figure 2B A functional block diagram of brake pull and / or torque steer mitigation according to the principles of the present disclosure is generally shown.

[0014] Figure 3A A brake pull event according to the principles of the present disclosure is generally illustrated.

[0015] Figure 3B A torque steer event according to the principles of the present disclosure is generally illustrated.

[0016] Figure 3C Induced torque steer according to the principles of the present disclosure is generally shown.

[0017] Figure 4 is a flow chart generally illustrating a method for a brake pull and / or torque steer event according to the principles of the present disclosure.

[0018] Figure 5 is a flow chart generally illustrating a brake pull relief method according to the principles of the present disclosure.

[0019] Figure 6 is a flow chart generally illustrating a torque steer mitigation method according to the principles of the present disclosure.

[0020] Figure 7is a flow chart generally illustrating a method according to the principles of the present disclosure. DETAILED DESCRIPTION

[0021] The following discussion is directed to various embodiments of the present disclosure. Although one or more of these embodiments may be preferred, the disclosed embodiments should not be interpreted or otherwise used as limiting the scope of the present disclosure, including the claims. In addition, it will be understood by those skilled in the art that the following description has a wide range of applications, and the discussion of any embodiment is merely an example of that embodiment, and is not intended to imply that the scope of the present disclosure, including the claims, is limited to that embodiment.

[0022] As described above, vehicles such as automobiles, trucks, SUVs, crossovers, minivans, boats, airplanes, all-terrain vehicles, recreational vehicles, or other suitable forms of transportation typically include a steering system, such as an electronic power steering (EPS) system, a steer-by-wire (SbW) system, a hydraulic steering system, or other suitable steering systems. The steering system of such a vehicle typically controls various aspects of the vehicle's steering, including providing steering assistance to the vehicle's operator, controlling the vehicle's steering wheels, etc.

[0023] In such steering systems, brake pull and / or torque steer events may result in an undesirable steering feel (e.g., from the perspective of the vehicle driver). Efforts have been made to mitigate brake pull events by manipulating brake torque in the connected steering system. Additionally, efforts have been made to mitigate torque steer events including approaches involving suspension geometry or vehicle configuration.

[0024] refer to FIG. 3A to FIG. 3C , generally showing such a brake-pull event and torque-steer event. The asymmetrical application of brake / propulsion torque between the left and right sides of the vehicle causes an undesirable moment and corresponding yaw rate of the vehicle, which is referred to as brake-pull / torque-steer. Brake-pull (e.g., Figure 3A ) can be caused by various faults in the brake system, while torque steer (e.g. Figure 3B ) events can be caused by inherent vehicle construction and vehicle suspension geometry. This moment results in unnecessary rack forces, which ultimately affect the steering wheel torque feedback to the driver.

[0025] Figure 3C Figure 1 shows uneven torque distribution on the axle between the left and right wheels. This can cause speed differences at the wheels, which can cause the vehicle to steer unexpectedly. This affects the steering wheel torque feedback felt by the driver.

[0026] Therefore, systems and methods configured to mitigate effect events (e.g., which may include brake pull events and / or torque steer events), such as those described herein, may be desirable. In some embodiments, the systems and methods described herein may be configured to mitigate undesirable steering wheel torque due to brake pull or torque steer on a SbW steering system (e.g., or any other suitable steering system, including but not limited to a rear-steering system). Additionally or alternatively, the systems and methods described herein may be configured to mitigate undesirable steering torque associated with uneven road surfaces, banked curves, etc.

[0027] In some embodiments, the systems and methods described herein may be configured not to prevent the occurrence of brake pull or torque steer events.The systems and methods described herein may be configured to mitigate unwanted steering wheel torque feedback to the driver.

[0028] The systems and methods described herein can be configured to overcome undesirable hand wheel torque by learning the rack load effect due to brake pull or torque steer. For example, for brake pull, the systems and methods described herein can be configured to learn the rack load pattern when the vehicle is traveling straight and the driver intends to go straight and brakes. The mathematical model of the effect of brake pull on rack load can be defined according to the following formula:

[0029]

[0030]

[0031] v x - longitudinal vehicle speed;

[0032] a x - longitudinal vehicle acceleration;

[0033] Bp - applied master cylinder pressure or total brake torque;

[0034] a0, a1, a2, a3, a4, a5 - real scalar coefficients ∈ R;

[0035] a′0, a′1, ′a2, a′3, a′4, a′5 - currently learned scalar coefficients ∈ R based on variable data.

[0036] For torque steer, the systems and methods described herein may be configured to learn rack load patterns when the vehicle is traveling in a straight line and the driver intends to go straight and accelerate.

[0037] The mathematical model for the effect of torque steer on rack load can be defined as follows, where AxleTrq corresponds to the propulsion torque on the axle:

[0038]

[0039]

[0040] fn(Force, v x )-function for mapping rack force to generate force component of handwheel reference torque;

[0041]

[0042]

[0043]

[0044] The coefficients are actively learned using randomized linear regression. The conditions for enabling learning are as follows: rack acceleration magnitude is less than a threshold; rack velocity magnitude is less than a threshold; steering column velocity magnitude is less than a threshold; steering column acceleration magnitude is less than a threshold; steering column position amplitude is less than a threshold: longitudinal velocity is greater than a threshold; the driver accelerator pedal is pressed; the driver brake is not pressed; and external controllers (such as ABS, TSC, ESC) are not enabled.

[0045] The effect of brake pull on rack force can be continuously predicted using the above model, and the predicted brake pull component of the rack force can be used to compensate the final rack force as a reference for the command of the handwheel torque for driver feedback according to the following equation:

[0046] F (Rack-HW-Effort) =F Rack_obs -K*F rack(TS Component)-Pred

[0047] The parameter K is a calibratable scalar gain that is a function of vehicle speed, vehicle yaw rate, and lateral acceleration. To detect whether the vehicle is experiencing brake pull and / or torque steer, yaw rate and lateral acceleration checks may be performed by the systems and methods described herein during straight-line maneuvers where the driver intends to brake or accelerate. To detect maturity in learning, the systems and methods described herein may be configured to compare predicted rack force to actual rack force during the learning phase and use a condition based on a threshold of the difference between the predicted rack force and the actual rack force.

[0048] In some embodiments, the systems and methods described herein can be configured to use a mathematical model that uses rack force as the primary source of information to mitigate disturbances. The systems and methods described herein can be configured to actively learn this function, which gives a deeper understanding of what is actually happening in the system.

[0049] In some embodiments, the systems and methods described herein can be configured to maintain the handwheel in a 0 degree position while reducing the driver's own effort once the effects of torque steer have been mitigated via a handwheel torque command, thereby driving a rack-controlled reference position that commands the wheel actuator to maintain position control to move the vehicle in the intended direction.

[0050] Torque steer can be caused by the difference in wheel speed between the left and right wheels. Therefore, it is proportional to the speed difference between the wheels. Torque steer can also depend on longitudinal vehicle speed and acceleration, and / or be affected by the direction of axle torque, which can be displayed as:

[0051] F rack(TS Component) =fn(v x , a x ,sign(T axle ))

[0052]

[0053] K = fn(Yaw rate, ay)-tunable

[0054] T_ref_Final=T_ref+K*F_Rack_TS

[0055] The two sets of the above expressions can be used for two different directions of axle torque (e.g., positive and negative). Therefore, two sets of coefficients can be learned. The coefficients can be actively learned using a total least squares fit, giving more weight to the most recent set of data points. Initially, the system can be loaded with the coefficients learned through testing, and as the vehicle is driven, these estimates can be updated. The learning phase may be limited to when the driver intends to go straight to avoid noise and other nonlinearities in the system. Once F is learned, rack(TS Component)-Pred and F rack ( BP Component)-Pred , the systems and methods described herein can be configured to mitigate by eliminating this term in T_ref at any time to reduce the driver's own force and maintain the intended direction when the driver's own force is reduced.

[0056] In some embodiments, the systems and methods described herein may be configured to determine whether the vehicle is being subjected to brake tug in response to determining that the vehicle is braking. The systems and methods described herein may be configured to determine whether the vehicle is being moved in accordance with driver intent in response to determining that the vehicle is being subjected to brake tug.

[0057] The systems and methods described herein may be configured to learn the function coefficients of the rack force effect due to brake pull in response to determining that the vehicle is moving according to the driver's intent. In some embodiments, the vehicle may be traveling substantially straight, and the driver's intent indicates the vehicle's intent to travel substantially straight.

[0058] The systems and methods described herein can be configured to predict a rack load effect due to brake pull in response to determining that a learned maturity has been achieved. The systems and methods described herein can be configured to generate a handwheel actuator command configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

[0059] Figure 1 A vehicle 10 in accordance with the principles of the present disclosure is generally shown. The vehicle 10 may include any suitable vehicle, such as a car, a truck, a sport utility vehicle, a minivan, a crossover, any other passenger vehicle, any suitable commercial vehicle, or any other suitable vehicle. Although the vehicle 10 is shown as a passenger vehicle having wheels and used on a road, the principles of the present disclosure may be applied to other vehicles, such as airplanes, boats, trains, drones, or other suitable vehicles.

[0060] The vehicle 10 includes a body 12 and a hood 14. A passenger compartment 18 is at least partially defined by the body 12. Another portion of the body 12 defines an engine compartment 20. The hood 14 may be movably attached to a portion of the body 12 such that when the hood 14 is in a first position or an open position, the hood 14 provides access to the engine compartment 20, and when the hood 14 is in a second position or a closed position, the hood 14 covers the engine compartment 20. In some embodiments, the engine compartment 20 may be disposed at the rear of the vehicle 10, rather than in the position generally shown.

[0061] The passenger compartment 18 may be disposed rearward of the engine compartment 20, but in embodiments where the engine compartment 20 is disposed at the rear of the vehicle 10, the passenger compartment 18 may be disposed forward of the engine compartment 20. The vehicle 10 may include any suitable propulsion system, including an internal combustion engine, one or more electric motors (e.g., an electric vehicle), one or more fuel cells, a hybrid propulsion system including a combination of an internal combustion engine, one or more electric motors (e.g., a hybrid vehicle), and / or any other suitable propulsion system.

[0062] In some embodiments, the vehicle 10 may include a gasoline or gasoline fueled engine, such as a spark ignition engine. In some embodiments, the vehicle 10 may include a diesel engine, such as a compression ignition engine. The engine compartment 20 houses and / or encapsulates at least some components of the propulsion system of the vehicle 10. Additionally or alternatively, propulsion control devices, such as accelerator actuators (e.g., accelerator pedals), brake actuators (e.g., brake pedals), hand wheels, and other such components are disposed in the passenger compartment 18 of the vehicle 10. The propulsion control devices may be actuated or controlled by an operator of the vehicle 10 and may be directly connected to corresponding components of the propulsion system, such as throttles, brakes, axles, vehicle transmissions, etc. In some embodiments, the propulsion control devices may transmit signals to a vehicle computer (e.g., drive-by-wire), which in turn may control corresponding propulsion components of the propulsion system. Thus, in some embodiments, the vehicle 10 may be an autonomous vehicle.

[0063] In some embodiments, the vehicle 10 includes a transmission that is in communication with the crankshaft via a flywheel or a clutch or a fluid coupling. In some embodiments, the transmission includes a manual transmission. In some embodiments, the transmission includes an automatic transmission. In the case of an internal combustion engine or a hybrid vehicle, the vehicle 10 may include one or more pistons that cooperate with the crankshaft to generate force that is transmitted through the transmission to one or more axles that rotate the wheels 22. When the vehicle 10 includes one or more electric motors, the vehicle battery and / or fuel cell provides energy to the electric motors to rotate the wheels 22.

[0064] The vehicle 10 may include an automatic vehicle propulsion system, such as cruise control, adaptive cruise control, automatic braking control, other automatic vehicle propulsion systems, or combinations thereof. The vehicle 10 may be an autonomous or semi-autonomous vehicle, or other suitable type of vehicle. The vehicle 10 may include more or fewer features than those generally shown and / or disclosed herein.

[0065] In some embodiments, the vehicle 10 may include an Ethernet component 24, a controller area network (CAN) bus 26, a media oriented system transport component (MOST) 28, a FlexRay component 30 (e.g., a wire control brake system, etc.), and a local interconnect network component (LIN) 32. The vehicle 10 may use the CAN bus 26, the MOST 28, the FlexRay component 30, the LIN 32, other suitable networks or communication systems, or combinations thereof to transmit various information from sensors, such as those inside or outside the vehicle, to various processors or controllers, such as those inside or outside the vehicle. The vehicle 10 may include more or fewer features than those generally shown and / or disclosed herein.

[0066] In some embodiments, the vehicle 10 may include a steering system, such as an EPS system, a steer-by-wire system (e.g., may include or communicate with one or more controllers that control components of the steering system without using a mechanical connection between a hand wheel and the wheel 22 of the vehicle 10), a hydraulic steering system (e.g., may include a magnetic actuator incorporated into a valve assembly of the hydraulic steering system), or other suitable steering systems.

[0067] The steering system may include an open-loop feedback control system or mechanism, a closed-loop feedback control system or mechanism, or a combination thereof. The steering system may be configured to receive various inputs, including but not limited to hand wheel position, input torque, one or more wheel positions, other suitable inputs or information, or a combination thereof.

[0068] Additionally or alternatively, the input may include a hand wheel torque, a hand wheel angle, a motor speed, a vehicle speed, an estimated motor torque command, other suitable inputs, or a combination thereof. The steering system may be configured to provide steering functionality and / or control to the vehicle 10. For example, the steering system may generate an assist torque based on various inputs. The steering system may be configured to selectively use the assist torque to control a motor of the steering system to provide steering assistance to an operator of the vehicle 10.

[0069] In some embodiments, the vehicle 10 may include a controller, such as controller 100, such as Figure 2A . The controller 100 may include any suitable controller, such as an electronic control unit or other suitable controller. The controller 100 may be configured to control various functions of, for example, a steering system and / or various functions of the vehicle 10. The controller 100 may include a processor 102 and a memory 104. The processor 102 may include any suitable processor, such as those described herein. Additionally or alternatively, in addition to or different from the processor 102, the controller 100 may include any suitable number of processors. The memory 104 may include a single disk or multiple disks (such as a hard disk drive), and include a storage management module that manages one or more partitions within the memory 104. In some embodiments, the memory 104 may include flash memory, semiconductor (solid-state) memory, etc. The memory 104 may include a random access memory (RAM), a read-only memory (ROM), or a combination thereof. The memory 104 may include instructions that, when executed by the processor 102, cause the processor 102 to control at least various aspects of the vehicle 10. Additionally or alternatively, the memory 104 may include instructions that, when executed by the processor 102 , cause the processor 102 to at least control various functions of the steering system and / or perform any other suitable functions, including the functions of the systems and methods described herein.

[0070] The controller 100 may receive one or more signals from various measuring devices or sensors 106 that indicate characteristics sensed or measured about the vehicle 10. The sensors 106 may include any suitable sensors, measuring devices, and / or other suitable mechanisms. For example, the sensors 106 may include one or more torque sensors or devices, one or more handwheel position sensors or devices, one or more motor position sensors or devices, one or more position sensors or devices, other suitable sensors or devices, or combinations thereof. The one or more signals may indicate handwheel torque, handwheel angle, motor speed, vehicle speed, other suitable information, or combinations thereof.

[0071] In some embodiments, the controller 100 may be configured to mitigate the effects of events that may include a brake pull event and / or a torque steer event. For example, the controller 100 may include a wheel actuator (RWA) and / or a hand wheel actuator (HWA) and / or may communicate with the wheel actuator (RWA) and / or the hand wheel actuator (HWA), such as Figure 2A Controller 100 can relatively quickly and automatically learn and / or adapt to changes in vehicle dynamics within a predetermined number (eg, 10 or other suitable number) of acceleration events (eg, longitudinal acceleration events = ≥ 0.25 g > 1 second).

[0072] Once the learning cycle is complete (e.g., maturity has been achieved), the controller 100 may limit peak steering wheel torque disturbances due to front axle propulsion torque changes (e.g., increases or decreases) to <0.50 Nm during acceleration and / or deceleration events under the following conditions:

[0073] Acceleration uphill slope (nominal 7.2% slope)

[0074] Lateral acceleration + / - 0.10g

[0075] Any potential vehicle loading condition up to GVW rating

[0076] Any valid potential weight distribution (front / rear and left / right)

[0077] Axle torque causes rack load at the calibrated limit (Tmax)

[0078] like Figure 2BAs generally shown in , the controller 100 can receive various inputs (e.g., including but not limited to brake pressure, handwheel angle, handwheel speed, rack position, rack speed, longitudinal speed, and accelerator pedal position or output) and provide activation and condition checking functions. The activation condition checker function provides torque steering learning when the following conditions are met: the vehicle 10 is moving straight forward, and the driver intends to drive the vehicle straight or relatively straight, and the accelerator pedal is pressed (e.g., actuated), and the vehicle 10 is accelerating. The activation condition checker function activates torque steering offset when the following conditions are met: the accelerator pedal is pressed, and the vehicle 10 is moving forward.

[0079] Additionally or alternatively, the activation condition checker function provides brake pull learning when the following conditions are met: the vehicle 10 is moving straight ahead, and the driver intends to drive the vehicle straight or relatively straight, and the brake pedal is pressed (e.g., actuated), and the vehicle 10 is decelerating. The activation condition checker function activates brake pull biasing when the following conditions are met: the brake pedal is pressed, and the vehicle 10 is moving forward.

[0080] The controller 100 sets a learning activation flag, an offset activation flag, and provides function learning and prediction functions: learning the effect of torque steer on rack force by stochastic gradient descent; predicting necessary rack force offset; and learning vehicle behavior and determining the limit of offset.

[0081] The controller 100 can determine the rack force offset value and provide torque steering using the steering column torque reference. The steering column torque reference: converts the rack force offset into a HW torque similar to the force calculation; converts the calculated torque as a function of vehicle speed and yaw rate; and applies the converted torque as an offset to the total steering column torque reference.

[0082] In some embodiments, the controller 100 may determine whether the vehicle 10 is being braked in response to determining that the vehicle 10 is braking. The controller 100 may determine whether the vehicle 10 is moving according to the driver's intention in response to determining that the vehicle 10 is being braked.

[0083] The controller 100 may learn the function coefficients of the rack force impact due to brake pull in response to determining that the vehicle 10 is moving according to the driver's intent. The vehicle 10 may be moving substantially straight, and the driver's intent indicates an intent of the vehicle to move substantially straight.

[0084] The controller 100 may predict the rack load effect due to brake pull in response to determining that the learned maturity is achieved. The controller 100 may generate a handwheel actuator command. The handwheel actuator command may be configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

[0085] In some embodiments, the controller 100 may determine whether the vehicle is moving according to the driver's intention in response to determining that the vehicle 10 is being subjected to brake tug. The controller 100 may learn a function coefficient of a rack force impact due to brake tug in response to determining that the vehicle 10 is moving according to the driver's intention. The controller 100 may predict a rack load effect due to brake tug. The controller 100 may generate a handwheel actuator command based on the predicted rack load effect.

[0086] In some embodiments, the controller 100 may determine whether the vehicle 10 is moving according to the driver's intention in response to determining that the vehicle 10 is being subjected to torque steer. The controller 100 may learn a function coefficient of a rack force impact due to torque steer in response to determining that the vehicle 10 is moving according to the driver's intention. The controller 100 may predict a rack load effect due to torque steer. The controller 100 may generate a handwheel actuator command based on the predicted rack load effect.

[0087] In some embodiments, the controller 100 can perform the methods described herein. However, the fact that the methods described herein are performed by the controller 100 is not meant to be limiting, and any type of software executed on a controller or processor can perform the methods described herein without departing from the scope of the present disclosure. For example, a controller (e.g., a processor executing software within a computing device) can perform the methods described herein.

[0088] Figure 4 is a flow chart generally illustrating a method 400 for a brake pull and / or torque steer event in accordance with the principles of the present disclosure. At 402 , method 400 determines an asymmetric brake propulsion torque.

[0089] At 404 , method 400 induces a moment about a vertical axis.

[0090] At 406 , method 400 rotates the vehicle about a vertical axis / yaw angle.

[0091] At 408 , method 400 causes additional rack loading.

[0092] At 410 , method 400 applies additional undesired torque on a steering wheel (eg, or hand wheel).

[0093] Figure 5is a flow chart generally illustrating a brake pull mitigation method 500 in accordance with the principles of the present disclosure. At 502 , method 500 detects rack load due to brake pull.

[0094] At 504 , method 500 determines whether the vehicle is braking. If method 500 determines that the vehicle is braking, method 500 continues at 506 . Alternatively, the method continues at 518 .

[0095] At 506 , method 500 determines whether the vehicle is experiencing brake tug. If method 500 determines that the vehicle is experiencing brake tug, method 500 continues at 508 . Alternatively, method 500 continues at 518 .

[0096] At 508, method 500 determines whether the vehicle is moving straight and whether the driver intends to move the vehicle straight. If method 500 determines that the vehicle is moving straight and the driver intends to move the vehicle straight, method 500 continues at 510. Alternatively, if method 500 determines that the vehicle is not moving straight or the driver does not intend to move the vehicle straight, method 500 continues at 512.

[0097] At 510 , method 500 uses stochastic gradient descent to learn function coefficients for rack force impact due to brake pull.

[0098] At 512 , the method 500 determines whether the maturity of learning is achieved. If the method 500 determines that the maturity of learning is achieved, the method 500 continues at 514 . Alternatively, the method 500 continues at 518 .

[0099] At 514 , method 500 predicts rack load effects due to brake pull.

[0100] At 516 , method 500 generates a command to the handwheel actuator that counteracts the effect of brake pull on steering wheel (eg, handwheel) torque.

[0101] At 518 , method 500 takes no action.

[0102] Figure 6 is a flow chart generally illustrating a torque steer mitigation method 600 in accordance with principles of the present disclosure. At 602 , method 600 detects rack load due to torque steer.

[0103] At 604 , method 600 determines whether the vehicle is accelerating due to propulsion torque. If method 600 determines that the vehicle is accelerating due to propulsion torque, method 600 continues at 606 . Optionally, method 600 continues at 618 .

[0104] At 606 , method 600 determines whether the vehicle is experiencing torque steer. If method 600 determines that the vehicle is experiencing torque steer, method 600 continues at 608 . Optionally, method 600 continues at 618 .

[0105] At 608, method 600 determines whether the vehicle is moving straight and whether the driver intends to move the vehicle straight. If method 600 determines that the vehicle is moving straight and the driver intends to move the vehicle straight, method 600 continues at 610. Alternatively, if method 600 determines that the vehicle is not moving straight or the driver does not intend to move the vehicle straight, method 600 continues at 612.

[0106] At 610 , method 600 learns function coefficients of rack force jerk due to torque steer using stochastic gradient descent.

[0107] At 612, the method 600 determines whether the maturity of learning is achieved. If the method 600 determines that the maturity of learning is achieved, the method 600 continues at 614. Optionally, the method 600 continues at 618.

[0108] At 614 , method 600 predicts rack load effects due to torque steer.

[0109] At 616 , method 600 generates a command to a handwheel actuator that counteracts the effect of torque steer on steering wheel (eg, handwheel) torque.

[0110] At 618 , method 600 takes no action.

[0111] Figure 7 is a flow chart generally illustrating a method 700 in accordance with principles of the present disclosure. At 702 , method 700 determines whether a vehicle is experiencing brake pull in response to determining that the vehicle is braking.

[0112] At 704 , method 700 determines if the vehicle is moving according to driver intent in response to determining that the vehicle is experiencing brake pull.

[0113] At 706 , method 700 learns function coefficients of rack force impact due to brake pull in response to determining the vehicle is moving according to driver intent.

[0114] At 708 , method 700 predicts rack load effects due to brake pull in response to determining that learned maturity is achieved.

[0115] At 710 , method 700 generates a handwheel actuator command configured to cause the handwheel actuator to counteract the effects of the rack load when the command is applied to the handwheel actuator.

[0116] In some embodiments, a system for mitigating undesirable steering torque includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: determine whether the vehicle is being subjected to brake pull in response to determining that the vehicle is braking; determine whether the vehicle is moving according to driver intent in response to determining that the vehicle is being subjected to brake pull; learn a function coefficient of rack force impact due to brake pull in response to determining that the vehicle is moving according to driver intent; predict a rack load effect due to brake pull in response to determining that the learning maturity is achieved; and generate a handwheel actuator command, the handwheel actuator command being configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

[0117] In some embodiments, the vehicle is moving substantially straight and the driver intends to indicate an intention for the vehicle to move substantially straight. In some embodiments, the hand wheel actuator is associated with a steering system of the vehicle. In some embodiments, the steering system comprises a steer-by-wire system.

[0118] In some embodiments, a method for mitigating undesirable steering torque includes: determining whether the vehicle is subject to brake pull in response to determining that the vehicle is braking; determining whether the vehicle is moving according to driver intention in response to determining that the vehicle is subject to brake pull; learning a function coefficient of a rack force impact due to brake pull in response to determining that the vehicle is moving according to driver intention; predicting a rack load effect due to brake pull in response to determining that a maturity of learning has been achieved; and generating a handwheel actuator command, the handwheel actuator command being configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

[0119] In some embodiments, the vehicle is moving substantially straight, and the driver intends to indicate an intention for the vehicle to move substantially straight. In some embodiments, the hand wheel actuator is associated with a steering system of the vehicle. In some embodiments, the steering system comprises a steer-by-wire system.

[0120] In some embodiments, a system for mitigating undesirable steering torque includes a processor and a memory including instructions that, when executed by the processor, cause the processor to: determine whether a vehicle is subject to brake traction in response to determining that the vehicle is braking; determine whether the vehicle is moving in accordance with driver intent in response to determining that the vehicle is subject to brake traction; learn a function coefficient of a rack force impact due to brake traction in response to determining that the vehicle is moving in accordance with driver intent; predict a rack load effect due to brake traction; and generate a handwheel actuator command based on the predicted rack load effect.

[0121] In some embodiments, the instructions further cause the processor to determine whether a maturity of learning has been achieved. In some embodiments, the maturity of learning is achieved after a predetermined number of learning cycles. In some embodiments, the predetermined number of cycles is 10. In some embodiments, in response to determining that the maturity of learning has been achieved, the instructions further cause the processor to predict a rack load effect due to brake pull. In some embodiments, the vehicle is moving substantially straight and the driver intends to indicate an intention of the vehicle to move substantially straight. In some embodiments, the handwheel actuator is associated with a steering system of the vehicle. In some embodiments, the steering system includes a steer-by-wire system. In some embodiments, the handwheel actuator command is configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator. In some embodiments, the instructions further cause the processor to apply the handwheel actuator command to the handwheel actuator to selectively control at least one aspect of the vehicle's steering.

[0122] In some embodiments, a method for mitigating undesirable steering torque includes: determining whether the vehicle is subject to brake pull in response to determining that the vehicle is braking; determining whether the vehicle is moving according to driver intention in response to determining that the vehicle is subject to brake pull; learning a function coefficient of a rack force impact due to brake pull in response to determining that the vehicle is moving according to driver intention; predicting a rack load effect due to brake pull; and generating a handwheel actuator command based on the predicted rack load effect.

[0123] In some embodiments, the method further includes determining whether a maturity of learning has been achieved. In some embodiments, the maturity of learning is achieved after a predetermined number of learning cycles. In some embodiments, the predetermined number of cycles is 10. In some embodiments, the method further includes predicting a rack load effect due to brake pull in response to determining that the maturity of learning has been achieved. In some embodiments, the vehicle is moving substantially straight and the driver intends to indicate an intention for the vehicle to move substantially straight. In some embodiments, the handwheel actuator is associated with a steering system of the vehicle. In some embodiments, the steering system includes a steer-by-wire system. In some embodiments, the handwheel actuator command is configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

[0124] In some embodiments, a device for mitigating undesirable steering torque includes a controller configured to: determine whether a vehicle is moving according to driver intent in response to determining that the vehicle is subject to brake pull; learn a function coefficient of a rack force impact due to brake pull in response to determining that the vehicle is moving according to driver intent; predict a rack load effect due to brake pull; and generate a handwheel actuator command based on the predicted rack load effect.

[0125] The above discussion is intended to illustrate the principles and various embodiments of the present disclosure. Once the above disclosure is fully understood, many changes and modifications will become apparent to those skilled in the art. It is intended that the following claims be interpreted as covering all such changes and modifications.

[0126] The word "example" is used herein to mean used as an example, instance or illustration. Any aspect or design described herein as an "example" is not necessarily to be interpreted as being more preferred or advantageous than other aspects or designs. On the contrary, the use of the word "example" is intended to present the concept in a specific way. As used in this application, the term "or" is intended to represent an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clearly seen from the context, "X includes A or B" is intended to represent any natural inclusive arrangement. That is, if X includes A; X includes B; or X includes both A and B, then "X includes A or B" is satisfied in any of the foregoing cases. In addition, the article "a / an" used in this application and the appended claims should generally be interpreted as meaning "one or more", unless otherwise specified or clearly pointed to the singular form from the context. In addition, the use of the term "embodiment" or "one embodiment" throughout the text is not intended to represent the same embodiment or embodiment, unless so described.

[0127] The embodiments of the systems, algorithms, methods, instructions, etc. described herein may be implemented in hardware, software, or any combination thereof. The hardware may include, for example, a computer, an intellectual property (IP) core, an application specific integrated circuit (ASIC), a programmable logic array, an optical processor, a programmable logic controller, microcode, a microcontroller, a server, a microprocessor, a digital signal processor, or any other suitable circuit. In the claims, the term "processor" shall be understood to include any of the foregoing hardware, either alone or in combination. The terms "signal" and "data" are used interchangeably.

[0128] As used herein, the term module may include a packaged functional hardware unit designed to be used with other components, a set of instructions executable by a controller (e.g., a processor executing software or firmware), a processing circuit configured to perform a specific function, and an independent hardware or software component that interfaces with a larger system. For example, a module may include an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a circuit, a digital logic circuit, an analog circuit, a combination of discrete circuits, a gate circuit, and other types of hardware, or a combination thereof. In other embodiments, a module may include a memory that stores instructions that can be executed by a controller to implement the features of the module.

[0129] Furthermore, in one aspect, for example, the systems described herein may be implemented using a general purpose computer or general purpose processor with a computer program that, when executed, performs any corresponding method, algorithm and / or instruction described herein. Additionally or alternatively, for example, a special purpose computer / processor may be utilized that may include other hardware for executing any method, algorithm or instruction described herein.

[0130] In addition, all or part of the implementation of the present disclosure may take the form of a computer program product accessible from, for example, a computer usable or computer readable medium. A computer usable or computer readable medium may be, for example, any device that can tangibly contain, store, communicate, or transmit a program for use by or in conjunction with any processor. The medium may be, for example, an electronic, magnetic, optical, electromagnetic device, or a semiconductor device. Other suitable media may also be used.

[0131] The above embodiments, implementations and aspects have been described to allow easy understanding of the present disclosure and not to limit the present disclosure. On the contrary, the present disclosure is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, and the scope of the appended claims should be given the broadest interpretation to cover all such modifications and equivalent structures permitted by law.

[0132] Although the present disclosure has been described in detail in conjunction with only a limited number of embodiments, it should be readily understood that the present disclosure is not limited to these disclosed embodiments. On the contrary, the present disclosure may be modified to encompass any number of variations, alterations, substitutions, or equivalent arrangements that have not been described heretofore but are commensurate with the spirit and scope of the present disclosure. In addition, although various embodiments of the present disclosure have been described, it should be understood that the aspects of the present disclosure may include only some of the described embodiments. Therefore, the present disclosure should not be considered to be limited by the foregoing description.

Claims

1. A system for mitigating undesirable steering torque, the system comprising: processor; as well as a memory comprising instructions that, when executed by the processor, cause the processor to: determining whether the vehicle is experiencing brake pull in response to determining that the vehicle is braking; determining whether the vehicle is moving according to driver intent in response to determining that the vehicle is experiencing brake pull; as well as learning a function coefficient of a rack force impact due to the brake pull in response to determining that the vehicle is moving according to driver intent; predicting rack load effects due to the brake pull; and Generate handwheel actuator commands based on predicted rack load effects.

2. The system according to claim 1, wherein: The instructions also cause the processor to determine whether a maturity of learning has been achieved.

3. The system according to claim 2, wherein: After a predetermined number of learning cycles, maturity of the learning is achieved.

4. The system according to claim 3, wherein: The predetermined number of cycles is ten.

5. The system according to claim 2, wherein: The instructions further cause the processor to predict a rack load effect due to the brake pull in response to determining that maturity of the learning is achieved.

6. The system according to claim 1, wherein: The vehicle is moving generally straight, and the driver intends to indicate an intention for the vehicle to move generally straight.

7. The system according to claim 1, wherein: The hand wheel actuator is associated with a steering system of the vehicle.

8. The system according to claim 7, wherein: The steering system includes a steer-by-wire system.

9. The system according to claim 1, wherein: The handwheel actuator command is configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

10. The system according to claim 1, wherein: The instructions further cause the processor to apply the hand-wheel actuator command to a hand-wheel actuator to selectively control at least one aspect of vehicle steering.

11. A method for mitigating undesirable steering torque, the method comprising: determining whether the vehicle is experiencing brake pull in response to determining that the vehicle is braking; determining whether the vehicle is moving according to driver intent in response to determining that the vehicle is experiencing brake pull; as well as learning a function coefficient of a rack force impact due to the brake pull in response to determining that the vehicle is moving according to driver intent; predicting rack load effects due to the brake pull; and Generate handwheel actuator commands based on predicted rack load effects.

12. The method according to claim 11, further comprising: Determine whether learning maturity has been achieved.

13. The method according to claim 12, wherein: After a predetermined number of learning cycles, maturity of the learning is achieved.

14. The method according to claim 13, wherein: The predetermined number of cycles is ten.

15. The method according to claim 12, further comprising: A rack load effect due to the brake pull is predicted in response to determining that maturity of the learning is achieved.

16. The method according to claim 11, wherein: The vehicle is moving generally straight, and the driver intends to indicate an intention for the vehicle to move generally straight.

17. The method according to claim 11, wherein: The hand wheel actuator is associated with a steering system of the vehicle.

18. The method according to claim 17, wherein: The steering system includes a steer-by-wire system.

19. The method according to claim 11, wherein: The handwheel actuator command is configured to cause the handwheel actuator to counteract the rack load effect when the command is applied to the handwheel actuator.

20. A device for mitigating undesirable steering torque, the device comprising: The controller is configured as: In response to determining that the vehicle is experiencing brake pull, the following operations are performed: determining whether the vehicle is moving according to driver intent; learning a function coefficient of a rack force impact due to brake pull in response to determining that the vehicle is moving according to driver intent; predicting rack load effects due to the brake pull; as well as generating handwheel actuator commands based on predicted rack load effects; as well as In response to determining that the vehicle is experiencing torque steer, performing the following actions: determining whether the vehicle is moving according to driver intent; learning a function coefficient of a rack force impulse due to torque steer in response to determining that the vehicle is moving according to driver intent; predicting rack load effects due to the torque steer; and Generate handwheel actuator commands based on predicted rack load effects.

Images