REDUCE OF TRACTION STEERING DISTURBANCES BY DRIVER-CONNECTED TORQUE IN VEHICLES WITH ELECTRONIC STEERING

Abstract

Method (200) comprising: Obtaining sensor data (204) from one or more sensors (131, 132, 133, 134, 135, 136, 137, 138, 139) of a vehicle (100) with a steering system (104); Determining (206), via the one or more processors (142) of the vehicle (100), a probability of the occurrence of a traction steering fault for the vehicle (100);and selectively adjusting (264) the resistance for a steering wheel (116) of the steering system (104) in accordance with instructions based on the sensor data provided via the one or more processors (142) and executed via a motor (120) coupled to the steering wheel (116), wherein the step of determining (206) the probability of a traction steering disturbance comprises: generating, via the one or more processors (142), scalar values ​​for each of a plurality of parameter values ​​from the sensor data, each of the scalar values ​​representing a corresponding probability that a corresponding of the plurality of parameter values ​​is likely to contribute to a traction steering disturbance; aggregating (248) the scalar values ​​for each of the plurality of parameter values ​​via the one or more processors (142);and calculate, via the one or more processors (142), an aggregated measure of the probability of a traction steering disturbance occurring for the vehicle (100), based on the aggregation of the scalar values, using the sensor data (204) to determine the probability of a traction steering disturbance occurring, and adjusting the resistance for the steering wheel (116) accordingly to mitigate the traction steering disturbances for the steering wheel (116) as experienced by the driver of the vehicle (100).

Description

The technical field of the present invention relates to methods and systems for mitigating traction steering disturbances.Many vehicles today are steer-by-wire vehicles in which the vehicle's steering wheel is not physically connected to the vehicle's wheels. In such vehicles, resistance may be provided to the driver via the steering wheel. However, under certain circumstances, the resistance may be affected by disturbances in the traction steering due to the driver's feedback torque.DE 10 2006 044 088 A1 discloses a method for compensating for drive influences of a drive train of a motor vehicle on its steering system, which has an electric power steering system. A permanently activated simulation model of the drive train integrated into the motor vehicle determines disturbance variables from a drive behavior, so that a compensation torque for the power steering system is generated, which counteracts the disturbance variables.DE 10 2014 218 509 A1 discloses a method for compensating drive influences of a drive train of a vehicle. The vehicle includes a traction motor and a power steering system including a servo motor configured to provide torque to a vehicle steering rack. The method includes actuating the servo motor to apply a compensation torque to the vehicle steering rack. The compensation torque is applied in response to a predictive drive influence caused by a regenerative braking torque.DE 10 2008 042 666 A1 discloses a method for compensating disturbance variables which act on a vehicle with an assist-force-assisted steering system. The method includes estimating an actual rack force using an observer model of the steering, estimating an artificial target rack force using an observer model of the vehicle, subtracting the estimated actual rack force from the estimated artificial target rack force, such that a total steering force error is generated. In a decision block, at least a first fractional factor of at least signals known from vehicle system is determined, which is superimposed on the total steering force error, so that a rack compensation force is generated, which is superimposed on the auxiliary force.DE 10 2020 108 132 A1 discloses a method for controlling a power steering system. The method includes generating a motor command as a function of a hand wheel speed, modifying the motor command based on a traction torque signal, and applying the motor command to an actuator of the power steering system.Accordingly, it is desirable to provide improved methods and systems for controlling traction steering disturbances in vehicles, such as steer-by-wire vehicles. The solution of the above-mentioned object is achieved by a method with the features of claim 1 and by a system with the features of claim 7.A method is disclosed that includes obtaining sensor data from one or more sensors of a vehicle having a steering system; determining a likelihood of occurrence of a traction steering fault for the vehicle via one or more processors of the vehicle; and selectively adjusting resistance for a steering wheel of the steering system according to instructions based on the sensor data, provided via the one or more processors, and executed via a motor coupled to the steering wheel. The sensor data is used to determine the probability of a traction steering disturbance occurring and the resistance to the steering wheel is adjusted accordingly to mitigate the traction steering disturbance for the steering wheel as experienced by the driver of the vehicle. The step of selectively adjusting the resistance includes performing the adjustment via the motor coupled to the steering wheel in accordance with the instructions provided via the one or more processors and based on the sensor data.The step of determining the probability of occurrence of a traction steering fault comprises generating, via the one or more processors, scalar values for each of a plurality of parameter values from the sensor data, each of the scalar values representing a respective probability that a respective one of the plurality of parameter values will likely contribute to a traction steering fault; aggregating, via the one or more processors, the scalar values for each of the plurality of parameter values; and calculating, via the one or more processors, an aggregated measure of the probability of occurrence of a traction steering fault for the vehicle based on the aggregating of the scalar values.In an exemplary embodiment, each of the scalar values has a value between zero and one.In an exemplary embodiment, the step of aggregating the scalar values also includes multiplying the scalar values for each of the plurality of parameter values via the one or more processors.In an exemplary embodiment, the method further comprises obtaining a measured rack load for the steering system from the sensor data; determining, via the one or more processors, a corrected rack load attributable to the disturbance of the traction steering; and determining, via the one or more processors, an adjustment of the resistance based on the corrected rack load; wherein the step of selectively adjusting the resistance comprises implementing the adjustment via the motor coupled to the steering wheel in accordance with the instructions provided via the one or more processors.In an exemplary embodiment, the method further comprises determining, via the one or more processors, an estimate of the traction steering induced rack force based on the measured rack load and the likelihood of a traction steering disturbance occurring for the vehicle; wherein the determining of the corrected rack load is performed using the estimate of the traction steering induced rack force.In an exemplary embodiment, the method further comprises applying, via the one or more processors, filtering based on a frequency map using the measured rack load and the likelihood of a traction steering disturbance occurring for the vehicle; wherein the determination of the corrected rack load is made using both the traction steering induced rack force estimation and the filtering based on the frequency map.Further provided is a system comprising one or more sensors and one or more processors of a vehicle having a steering system. The one or more sensors are configured to obtain sensor data about the vehicle. The one or more processors are coupled to the one or more sensors and are configured to at least enable determination of the probability of occurrence of a traction steering fault for the vehicle and selectively adjust the resistance to a steering wheel of the steering system according to instructions provided via the one or more processors and executed via a motor coupled to the steering wheel.The one or more processors are configured to at least enable generating scalar values for each of a plurality of parameter values from the sensor data, each of the scalar values representing a respective probability that a respective one of the plurality of parameter values is likely to contribute to a traction steering disturbance; aggregate the scalar values for each of the plurality of parameter values; and calculate an aggregated measure of the probability of occurrence of a traction steering disturbance for the vehicle based on the aggregating of the scalar values.In an exemplary embodiment, each of the scalar values has a value between zero and one.Also in an example embodiment, the one or more processors are configured to enable at least the aggregation of the scalar values by multiplying the scalar values for each of the plurality of parameter values.Also in an exemplary embodiment, the one or more processors are configured to at least enable obtaining from the sensor data a measured rack load for the steering system; determine a corrected rack load attributable to the disturbance of the traction steering; determine a resistance adjustment based on the corrected rack load; and selectively adjust the resistance by implementing the adjustment via the motor coupled to the steering wheel in accordance with the instructions provided via the one or more processors.Also in an exemplary embodiment, the one or more processors are configured to at least enable determination of a traction steering induced rack force estimate based on the measured rack load and the likelihood of a traction steering disturbance occurring for the vehicle; and determination of the corrected rack load using the traction steering induced rack force estimate.In an exemplary embodiment, the one or more processors are further configured to enable at least application of filtering based on a frequency map using the measured rack load and the likelihood of a traction steering disturbance occurring for the vehicle; and determine the corrected rack load using both the traction steering induced rack force estimation and the filtering based on the frequency map.Further provided is a vehicle that includes a steering system, one or more sensors, and one or more processors. The steering system has a steering wheel and a motor coupled thereto. The one or more sensors are configured to acquire sensor data about the vehicle. The one or more processors are coupled to the one or more sensors and are configured to at least enable determination of the likelihood of occurrence of a fault in the traction steering of the vehicle and selectively adjust the resistance of the steering wheel according to the instructions based on the sensor data and provided via the one or more processors and executed via the motor coupled to the steering wheel.The one or more processors are configured to at least enable generating scalar values for each of a plurality of parameter values from the sensor data, each of the scalar values representing a respective probability that a respective one of the plurality of parameter values is likely to contribute to a traction steering disturbance; aggregate the scalar values for each of the plurality of parameter values; and calculate an aggregated measure of the probability of occurrence of a traction steering disturbance for the vehicle based on the aggregating of the scalar values. The sensor data is used to determine the probability of a traction steering disturbance occurring and the resistance to the steering wheel is adjusted accordingly to mitigate the traction steering disturbance for the steering wheel as experienced by the driver of the vehicle.Also in an example embodiment, each of the scalar values has a value that is between zero and one; and the one or more processors are configured to enable at least aggregating the scalar values by multiplying the scalar values for each of the plurality of parameter values.In an exemplary embodiment, the steering system further includes a rack and the one or more processors are configured to at least enable obtaining a measured rack load for the rack from the sensor data; determine a corrected rack load attributable to the disturbance of the traction steering; determine a setting of the resistance based on the corrected rack load; and selectively set the resistance by implementing the setting via the motor coupled to the steering wheel in accordance with the instructions provided via the one or more processors.Also in an exemplary embodiment, the one or more processors are configured to at least enable determination of a traction steering induced rack force estimate based on the measured rack load and the likelihood of a traction steering disturbance occurring for the vehicle; and determination of the corrected rack load using the traction steering induced rack force estimate.In an exemplary embodiment, the one or more processors are further configured to enable at least application of filtering based on a frequency map using the measured rack load and the likelihood of a traction steering disturbance occurring for the vehicle; and determine the corrected rack load using both the traction steering induced rack force estimation and the filtering based on the frequency map.The present description will be described below in conjunction with the following figures, wherein like numerals designate like elements: FIG. 1 is a functional block diagram of a vehicle including a steering system and a control system for controlling the steering system and mitigating traction steering disturbances in the vehicle, according to an example embodiment; and FIG. 2 is a flow diagram of a method for controlling mitigation of traction steering disturbances in vehicles that may be implemented in connection with the vehicle of FIG. 1, including the steering system and control system of FIG. 1 and components thereof, in accordance with example embodiments.FIG. 1 illustrates a vehicle 100 according to an example embodiment. As described in more detail below, the vehicle 100 includes a steering system 104, wherein the vehicle 100 also includes a control system 102 for controlling mitigation of traction steering disturbances in the steering system 104 resulting from driver feedback during operation of the vehicle 100, as described in more detail below in connection with the vehicle 100 of FIG. 1 and the method 200 of FIG. 2.In various embodiments, the vehicle 100 includes an automobile. The vehicle 100 may be any type of automobile, such as a sedan, wagon, truck, or sport utility vehicle (SUV), and may include two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), four-wheel drive (4WD), or all-wheel drive (AWD), and / or various other types of vehicles in certain embodiments. In certain embodiments, the vehicle 100 may also include a motorcycle or other vehicle, such as an aircraft, spacecraft, watercraft, etc., and / or one or more other types of mobile platforms (e.g., a robot and / or other mobile platform). In various embodiments, the vehicle 100 includes a steer-by-wire vehicle.The vehicle 100 includes a body 105 disposed on a chassis 108. The body 105 substantially encloses other components of the vehicle 100. The body 105 and the chassis 108 may collectively form a frame. The vehicle 100 also includes a plurality of wheels 110. The wheels 110 are each rotatably connected to the chassis 108 near a corner of the body 105 to enable movement of the vehicle 100. In one embodiment, the vehicle 100 includes four wheels 110, although in other embodiments (e.g., for trucks and certain other vehicles), this may vary.A propulsion system 106 is mounted on the chassis 108 and drives the wheels 110, for example, via axles 114. In various embodiments, the propulsion system 106 includes a propulsion system that includes a motor 122 (e.g., an internal combustion engine and / or an electric motor / generator coupled to a transmission). In certain embodiments, the propulsion system 106 includes or is coupled to an accelerator pedal that receives input from a driver of the vehicle 100. In the illustrated embodiment, axles 114 include a front axle 114( 1) and a rear axle 114( 2).In various embodiments, the steering system 104 provides for steering of the vehicle 100. As shown in FIG. 1, in various embodiments, the steering system 104 includes a steering wheel 116, a rack and pinion system 118, and a motor 120. In various embodiments, a driver of the vehicle 100 controls steering via the steering wheel 116. In various embodiments, the driver inputs to the steering wheel 116 from the rack 118 are used to turn the wheels 110 of the vehicle 100 to enable steering. In certain embodiments, the rack and pinion system 118 includes a rack and pinion steering. Moreover, as mentioned above, in various embodiments, the steering system 104 includes a steer-by-wire system in which the steering wheel 116 is not physically connected to the wheels 110.As shown in FIG. 1, in various embodiments, the steering system 104 also includes a motor 120 coupled to the steering wheel 116. In various embodiments, the motor 120 is used to provide and adjust resistance to the steering wheel 116, for example, such that the steering wheel 116 behaves and feels as expected to the driver. In various embodiments, the motor 120 is used to control mitigation of traction steering disturbances at the steering wheel 116 due to driver feedback in accordance with instructions provided by the controller 102 (as described below).With continued reference to FIG. 1, in various embodiments, the controller 102 controls operation of the steering system 104. In particular, in various embodiments, the control system 102 controls operation of the rack and pinion system 118, including the implemented user inputs provided via the steering wheel 116 to steer the vehicle 100. In various embodiments, as mentioned above, the controller 102 also controls mitigation of traction steering disturbances at the steering wheel 116 due to driver feedback, as described in more detail below in connection with the method 200 of FIG. 2.As shown in FIG. 1, in various embodiments, the control system 102 includes a sensor assembly 130 and a controller 140, as described in more detail below, in accordance with an example embodiment.In various embodiments, the sensor assembly 130 collects data relating to the vehicle 100 and its components, including sensing conditions relating to the likelihood that traction steering disturbances may occur with respect to the steering wheel 116. In various embodiments, the sensor data is provided from the sensor assembly 130 to the controller 140 for use in mitigating traction steering disturbances, for example as described in more detail below.In various embodiments, the sensor assembly 130 includes one or more rack load sensors 131, torque sensors 132, rotation sensors 133, suspension sensors 134, speed sensors 135, throttle sensors 136, steering wheel sensors 137, and friction sensors 138. In certain embodiments, the sensor assembly 130 may also include one or more input sensors 139.In various embodiments, the shelf load sensors 131 measure a load on the shelf system 118 of FIG. 1.In various embodiments, the torque sensors 132 also measure one or more torque values for the vehicle 100, including a drive torque for the front axle 114( 1) of the vehicle 100, in certain embodiments.In various embodiments, the rotation sensors 133 measure the rotational speed of one or more wheels 110 of the vehicle 100, including one or more front wheels 110.In various embodiments, the suspension sensors 134 include one or more suspension and / or pitch angle sensors that obtain sensor data about a pitch angle of the vehicle 100 and / or one or more other parameters related to a suspension system of the vehicle 100.Moreover, in various embodiments, the speed sensors 135 measure a speed of the vehicle 100 and / or information used to determine the vehicle speed (e.g., in certain embodiments, the speed sensors 135 may include wheel speed sensors 135 that measure the wheel speed of the vehicle 100).In various embodiments, the throttle sensors 136 measure or detect a position of a throttle of the vehicle 100 (e.g., the propulsion system 106).In various embodiments, the steering wheel sensors 137 obtain sensor data about the steering wheel 116 of the vehicle 100, including an angle and a speed of the steering wheel 116.In certain embodiments, the friction sensors 138 include one or more sensors that measure or acquire sensor data relating to a road friction coefficient for a road or path along which the vehicle 100 is travelling and / or can be used to calculate such a coefficient.In various embodiments, the input sensors 139 include one or more sensors that measure or detect input provided by a driver of the vehicle 100. In various embodiments, the input sensors 139 are connected to a steering wheel, an accelerator pedal, a brake pedal, and other instruments of the vehicle 100 and configured to measure or detect driver intervention.In various embodiments, the controller 140 is coupled to and receives sensor data from the sensor assembly 130. In various embodiments, the controller 140 is further coupled to the steering system 104, and in certain embodiments, to one or more of the following systems (and in certain embodiments, to the propulsion system 106 and / or other systems of the vehicle 100). In various embodiments, the controller 140 controls the steering system 104 (including mitigation of traction steering disturbances) based on sensor data, among other things, as described further below in connection with the method 200 of FIG. 2.In various embodiments, the control unit 140 includes a computer system (also referred to herein as computer system 140). In various embodiments, the control unit 140 (and in certain embodiments the control system 102 itself) is disposed within the body 105 of the vehicle 100. In one embodiment, the controller 102 is mounted on the chassis 108. In certain embodiments, the controller 140 and / or the control system 102 and / or one or more components thereof may be located outside the body 105, for example, on a remote server, in the cloud, or the like.It will be appreciated that the control unit 140 may be different from the embodiment shown in FIG. 1. For example, the controller 140 may be coupled to or otherwise utilize one or more remote computing systems and / or other control systems, for example, as part of one or more of the above-mentioned devices and systems of the vehicle 100.In the illustrated embodiment, the computer system of the controller 140 includes a processor 142, a memory 144, an interface 146, a storage device 148, and a bus 150. Processor 142 performs the calculation and control functions of controller 140, and may include any type of processor or multiple processors, single integrated circuits such as a microprocessor, or any number of integrated devices and / or circuit boards that cooperate to perform the functions of a processing unit. During operation, the processor 142 executes one or more programs 152 included in the memory 144, and as such, controls the general operation of the controller 140 and the computer system of the controller 140, generally in executing the processes described herein, such as the process 200 of FIG. 2 described further below in connection therewith.The memory 144 may be any type of suitable memory, including various types of non-transitory computer readable storage media. In certain examples, the memory 144 is on and / or located on the same computer chip as the processor 142. In the illustrated embodiment, the memory 144 stores the aforementioned program 152 along with stored values 154 (e.g., look-up tables, thresholds, and / or other values related to traction steering disturbances and their relationship to the sensor data).The interface 146 enables communication with the computer system of the controller 140, e.g., from a system driver and / or other computer system, and may be implemented using any suitable method and apparatus. In one embodiment, the interface 146 receives the various data from the sensor assembly 130, among other possible data sources. The interface 146 may include one or more network interfaces to communicate with other systems or components. The interface 146 may also include one or more network interfaces for communication with technicians and / or one or more memory interfaces for connection to memory devices such as the device 148.The storage device 148 may be any suitable type of storage device, including various types of random access memories and / or other storage devices. In an exemplary embodiment, the apparatus 148 includes a program product from which the memory 144 may receive a program 152 that executes one or more embodiments of one or more processes of the present description, such as the steps of the process 200 of FIG. 2 described further below in connection therewith. In another exemplary embodiment, the program product may be directly stored in and / or otherwise accessed by the memory 144 and / or a disk (e.g., disk 156) as described below.Bus 150 is used to transfer programs, data, status and other information or signals between the various components of the computer system of controller 140. Bus 150 may be any suitable physical or logical means for interconnecting computer systems and components. These include, but are not limited to, direct hardwired connections, fiber optic technology, infrared and wireless bus technologies. During operation, the program 152 is stored in the memory 144 and executed by the processor 142.While this exemplary embodiment will be described in the context of a fully functional computer system, those skilled in the art will recognize that the mechanisms of the present description may be distributed as a program product with one or more types of non-transitory computer readable signal bearing media used to store the program and its instructions and to execute its distribution, such as a non-transitory computer readable medium bearing the program and including computer instructions stored therein to cause a computer processor (such as processor 142) to execute and execute the program.FIG. 2 is a flow diagram of the process 200 for controlling mitigation of traction steering disturbances from driver feedback, according to example embodiments. The method 200 may be implemented in connection with the vehicle 100 of FIG. 1, including the steering system 104 and the control system 102 of FIG. 1 and components thereof, in accordance with example embodiments.As shown in FIG. 2, process 200 begins in step 202. In one embodiment, the process 200 begins when the vehicle 100 is or was operating, for example, during or after a current vehicle trip. In one embodiment, the steps of process 200 are continuously performed once process 200 begins.The sensor data is acquired in step 204. In various embodiments, sensor data relating to the vehicle is obtained via each of the sensors of the sensor assembly 130 of FIG. 1. In certain embodiments, the sensor data of step 204 includes the following, among other possible types of sensor data:(i) rack load sensor data from one or more rack load sensors 131 for measuring a load on the rack system 118 of FIG. 1 ; (ii) torque sensor data from one or more torque sensors 132 including a drive torque for the front axle 114( 1) of the vehicle 100; (iii) rotation sensor data from one or more rotation sensors 133 including a rotational speed of one or more front wheels 110 of the vehicle 100;(iv) suspension data from one or more suspension sensors 134 relating to an inclination angle of the vehicle 100 and / or one or more other parameters relating to a suspension system of the vehicle 100; (v) speed sensor data from one or more speed sensors 135 to measure a speed of the vehicle 100 and / or information used to determine the vehicle speed (e.g., wheel speed);(vi) Throttle sensor data from one or more throttle sensors 136 regarding a position of a throttle of the vehicle 100 (e.g., the propulsion system 106); (vii) Steering wheel angle sensor data from one or more steering wheel sensors 137 regarding an angle or angular position of the steering wheel 116; (viii) Steering wheel speed sensor data from one or more steering wheel sensors 137 regarding a speed of the steering wheel 116; and (ix) Friction sensor data from one or more friction sensors 138, including regarding a road friction coefficient for a road or path the vehicle 100 is travelling. In certain embodiments, additional sensor data may also be obtained via one or more input sensors 139 and may relate, for example, to inputs provided by a driver of the vehicle 100, such as via a steering wheel, accelerator pedal, brake pedal, and other instruments of the vehicle 100.In various embodiments, the sensor data from step 204 is converted (e.g., as referred to in FIG. 2 as combined step 206). In particular, in various embodiments, during the combined step 206, each of the determined types of sensor data (e.g., as reflected in each respective type of sensor data signals) is translated into respective scalar values, as an indication of how likely each type of sensor data (or associated parameters) is to contribute to a traction steering state for the steering wheel 116. In various embodiments, this is performed by a processor (such as processor 142 in FIG. 1 ).In particular, as part of the combined step 206, in various embodiments: (i) a rack load 208 from the rack load sensor data is translated into a rack load scaler 210 (also referred to as K 10 in FIG. 2 ); (ii) a front axle drive torque 212 from the torque sensor data is translated into a drive torque scaler 214 (also referred to as K 1 in FIG. 2 ); (iii) a front axle torque rate of change 216 is calculated from the torque sensor data and translated into a drive torque rate of change 214. 22); (iii) a front axle torque rate of change 216 is calculated from the front axle drive torque 212 and translated into a drive torque rate of change scalar 218 (also referred to as K 4 in FIG. 2 ); (iv) a front wheel torque (or change) 220 from the rotation sensor data is translated into a rotation delta scalar 222 (also referred to as K 3 in FIG. 2 ); (v) a vehicle suspension pitch angle 224 from the suspension data is translated into a pitch angle scalar 226 (also referred to as K in FIG. 2 ); (vi) a vehicle suspension pitch angle 224 from the suspension data is translated into a pitch angle scalar 226 (in FIG. 2 ); (vi) a vehicle speed 228 from the speed sensor data is translated into a vehicle speed scalar 230 (also referred to as K 5 in FIG. 2 ); (vii) a throttle position 232 from the throttle sensor data is translated into a throttle scalar 234 (also referred to as K 6 in FIG. 2 ); (viii) a steering wheel angle 236 from the steering wheel angle sensor data is translated into a steering wheel angle scalar 238 (also referred to as K 7 in FIG. 2 ); (ix) a steering wheel angle 236 from the steering wheel angle sensor data is translated into a steering wheel angle scalar 238. 2); (ix) a steering wheel speed 240 from the steering wheel speed sensor data is translated into a steering wheel speed scalar 242 (also referred to as K 8 in FIG. 2 ); and (x) a road friction coefficient 244 from the road friction sensor data is translated into a road friction coefficient scalar 246 (also referred to as K 9 in FIG. 2 ).In various embodiments, each of the respective scalar values 210, 214, 218, 222, 226, 230, 234, 238, 242, and 246 is based on the current respective corresponding parameter values 208, 212, 216, 220, 224, 228, 232, 236, 240, and 244 in combination with past or historical data relating to such parameter values relating to traction steering events (e.g., in certain embodiments relating to the same vehicle 100 and / or to other vehicles, such as in or from vehicle manufacturer data, published data, shared data between different vehicles, and so forth). Also in certain embodiments, the past or historical data is stored in the memory 144 of FIG. 1 as stored values 154, which may include, for example, functions, lookup tables, and / or other data representations.In various embodiments, each of the scalar values 210, 214, 218, 222, 226, 230, 234, 238, 242, and 246 has a corresponding value between zero and one. The closer the scalar value for a particular parameter is to zero ("0"), the less likely the particular parameter will contribute to a disturbance of the traction steering (e.g., a value of zero means that it is safe or nearly safe that a current value of the particular parameter will not cause a disturbance of the traction steering). Conversely, in various embodiments, the closer the scalar value is to one ("1"), the greater the probability that a particular parameter contributes to a traction steering disturbance (e.g., a value of one means that a current value of the particular parameter will certainly or nearly certainly cause a traction steering disturbance).In various embodiments, the respective scalar values are aggregated (step 248). In particular, in various embodiments, the scalar values 210, 214, 218, 222, 226, 230, 234, 238, 242, and 246 are multiplied together, thereby producing a product 249. In various embodiments, the product 249 may also be referred to as a TSLI (Traction Steer Likelihood Indication) 249.As shown in FIG. 2, in various embodiments, an estimate of the rack force caused by the traction steering is determined (step 250). In various embodiments, the traction steering induced rack force during step 250 represents an amount of rack force attributed to a traction steering disturbance. In various embodiments, at step 250, the traction steering induced rack force is determined by a processor (such as processor 142 in FIG. 1 ) based on the TSLI 249 along with the rack load 208.In various embodiments, the rack force caused by the traction steering of step 250 also corresponds to a rack force offset 257 (e.g., which may be used to correct or mitigate the traction steering in certain embodiments). In various embodiments, the rack force offset 257 corresponds to a torque that can be applied by the motor 120 to the steering wheel 116 that is equal to and opposite to a drag torque that comes from the drive torque forces that generate the rack force, thereby decreasing the drive steering behavior of the steering wheel 116.Moreover, in various embodiments, the TSLI 249 of step 248 is also used for the assignment of the cutoff frequency (step 252). In particular, in various embodiments, a cut-off frequency is selected for filtering the sensor data (in particular the data of the rack load sensor) depending on the probability of whether a traction steering occurs or is imminent if the occurrence of a traction steering is deemed probable. In various embodiments, this is performed via a processor, such as processor 142 of FIG. 1. Also in various embodiments, step 252 results in the selection of a particular optimized cutoff frequency "f c " 253 (as shown in FIG. 2 ) that depends on the TSLI 249.In various embodiments, filtering is performed (step 254). In particular, in various embodiments, in step 254, filtering is applied to the rack load 208 measurements based on the optimized cutoff frequency "f c " 253 of step 252. In various embodiments, the filtering is performed such that the sensor data (in particular, the rack load sensor data) is filtered sufficiently to remove any portion of the rack load data due to the force due to the drive torque, thereby also mitigating the disturbance of the traction steering. In various embodiments, this is performed by a processor, e.g., processor 142 in FIG. 1. In certain embodiments, the variable digital filter includes a first order low pass filter. In an exemplary embodiment, the variable digital filter includes a three hertz (3 Hz) low pass filter; however, this may vary in other embodiments. In various embodiments, the filtering of step 254 provides as output a filtered rack load 256.In various embodiments, the rack force offset 257 (from step 250) and the filtered rack load 256 (from step 254) are aggregated at step 258. In particular, in various embodiments, at step 258, the rack force offset 257 and the filtered rack load 256 are added. In various embodiments, this is also performed via a processor, such as processor 142 of FIG. 1.In various embodiments, the summation of step 258 yields a traction steering corrected rack load 260 as a calculated result. In various embodiments, the traction steering corrected rack load 260 represents a modified or updated value of the rack load after subtracting from this force value all forces due to traction steering disturbances (e.g., due to the drive torque and / or associated feedback). In various embodiments, this is performed via one or more processors (such as processor 142 in FIG. 1 ).In various embodiments, a resistance adjustment is determined (step 262). More specifically, in various embodiments, during step 262, the traction steering corrected rack load is used to determine an appropriate setting for the resistance provided to the steering wheel 116 of FIG. 1 via the motor 120 of FIG. 1. In various embodiments, the setting is determined via a processor (such as processor 142 of FIG. 1 and / or one or more separate processors, for example motor 120 and / or steering system 104 of FIG. 1 ). In various embodiments, the setting is also determined such that the resulting setting of the resistance at the steering wheel 116 corresponds to the resistance at the steering wheel 116 and / or appears to the driver as being equal to the resistance at the steering wheel 116 that would originally have been present in the absence of the traction steering disturbance.In various embodiments, an adjusted resistor is also provided (step 264). In various embodiments, the adjusted resistance is provided to the steering wheel 116 in implementing the determined adjustment of step 264. In various embodiments, the adjusted resistance is automatically implemented via the motor 120 in adjusting the resistance of the steering wheel 116 in accordance with and executing instructions provided to the motor 120 via one or more processors (such as the processor 142 of FIG. 1 ). Accordingly, in various embodiments, the driver experiences an appropriate resistance of the steering wheel 116 based on the current operating conditions and parameters of the vehicle 100 and the roadway, but without the undesirable disturbance of the traction steering. In other words, in various embodiments, the steering will feel better than the driver is expecting.In various embodiments, the process 200 is then terminated in step 265.Accordingly, methods, systems, and vehicles are provided for controlling mitigation of traction steering disturbances for the steering wheel of vehicles. In various embodiments, sensor data is used to determine a likelihood that a traction steering disturbance occurs or is about to occur for the steering wheel, and the resistance for the steering wheel is adjusted accordingly to mitigate the traction steering disturbance for the steering wheel as experienced by the driver of the vehicle using a motor coupled to the steering wheel and controlled in accordance with instructions provided by a processor of a control system and based on the sensor data.It will be appreciated that the systems, vehicles, and methods may vary from those illustrated in the figures and described herein. For example, the vehicle 100 of FIG. 1, its control system 102, and its steering system 104, and / or its components of FIG. 1, may vary in various embodiments. It will also be appreciated that the steps of the method 200 may be different from that shown in FIG. 2 and / or that different steps of the method 200 may be performed simultaneously and / or in a different order than that shown in FIG. 2.

Claims

A method (200) comprising: obtaining sensor data (204) from one or more sensors (131, 132, 133, 134, 135, 136, 137, 138, 139) of a vehicle (100) having a steering system (104); determining (206), via the one or more processors (142) of the vehicle (100), a likelihood of occurrence of a traction steering fault for the vehicle (100); and selectively adjusting (264) the resistance for a steering wheel (116) of the steering system (104) in accordance with instructions based on the sensor data, provided via the one or more processors (142), and executed via a motor (120) coupled to the steering wheel (116), wherein the step of determining (206) the probability of occurrence of a traction steering fault comprises: generating, via the one or more processors (142), scalar values for each of a plurality of parameter values from the sensor data, each of the scalar values representing a corresponding probability that a corresponding one of the plurality of parameter values is likely to contribute to a traction steering fault; aggregating (248) the scalar values for each of the plurality of parameter values via the one or more processors (142); Calculating, via the one or more processors (142), an aggregated measure of the probability of occurrence of a traction steering disturbance for the vehicle (100) based on the aggregation of the scalar values, wherein the sensor data (204) is used to determine the probability of occurrence of a traction steering disturbance and the resistance for the steering wheel (116) is adjusted accordingly to mitigate the traction steering disturbance for the steering wheel (116) as experienced by the driver of the vehicle (100).The method (200) of claim 1, characterized in that each of the scalar values has a value between zero and one.The method (200) of claim 1, characterized in that the step of aggregating (248) the scalar values comprises multiplying the scalar values for each of the plurality of parameter values via the one or more processors (142).The method (200) of claim 1, characterized in that the method (200) further comprises: determining, from the sensor data, a measured rack load (208) for the steering system (104); determining, via the one or more processors (142), a corrected rack load (260) attributable to the disturbance of the traction steering; and determining (262) an adjustment in resistance via the one or more processors (142) based on the corrected rack load (260).The method (200) of claim 4, characterized in that the method (200) further comprises: determining (250), via the one or more processors (142), an estimate of the traction steering induced rack force based on the measured rack load (208) and the probability of a traction steering disturbance occurring for the vehicle (100); wherein the determining of the corrected rack load (260) is performed using the estimate of the traction steering induced rack force.The method (200) of claim 5, characterized in that the method (200) further comprises: applying (254), via the one or more processors (142), filtering based on a frequency map using the measured rack load (208) and the probability of occurrence of a traction steering disturbance for the vehicle (100); wherein determining the corrected rack load (260) is performed using both the traction steering induced rack force estimate and the frequency map based filtering.A system comprising: one or more sensors (131, 132, 133, 134, 135, 136, 137, 138, 139) of a vehicle (100) having a steering system (104), the one or more sensors (131, 132, 133, 134, 135, 136, 137, 138, 139) being configured to obtain sensor data about the vehicle (100); and one or more processors (142) of the vehicle (100) coupled to the one or more sensors (131, 132, 133, 134, 135, 136, 137, 138, 139) and configured to at least enable: determining (206) a probability of occurrence of a traction steering fault for the vehicle (100); and selectively adjusting (264) the resistance for a steering wheel (116) of the steering system (104) in accordance with instructions based on the sensor data and provided via the one or more processors (142) and executed via a motor (120) coupled to the steering wheel (116); generating scalar values for each of a plurality of parameter values from the sensor data, each of the scalar values representing a respective probability that a respective one of the plurality of parameter values is likely to contribute to a traction steering disturbance; aggregating (248) the scalar values for each of the plurality of parameter values; calculating an aggregated measure of the probability of occurrence of a traction steering disturbance for the vehicle (100) based on the aggregation of the scalar values, wherein the sensor data (204) is used to determine the probability of occurrence of a traction steering disturbance and the resistance for the steering wheel (116) is adjusted accordingly to mitigate the traction steering disturbance for the steering wheel (116) as experienced by the driver of the vehicle (100).The system of claim 7, characterized in that the one or more processors (142) are configured to enable at least: determining a measured rack load for the steering system (104) from the sensor data; determining (258) a corrected rack load (260) due to the disturbance of the traction steering; determining (262) an adjustment of the resistance based on the corrected rack load (260); and selectively adjusting (264) the resistance by performing the adjustment via the motor (120) coupled to the steering wheel (116) in accordance with the instructions provided via the one or more processors (142).

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