SYSTEM FOR REAL-TIME ESTIMATION OF THE ANGLE, SPEED AND ACCELERATION CAPACITY ACHIEVEABLE BY A STEERING ACTUATOR

Abstract

System (100), comprising: a processor and a memory in which instructions are stored which, when executed by the processor, configure the processor to: estimate a rack force on a steering system (104) of a vehicle based on the current driving and environmental conditions; estimate the state of an actuator (130) of the steering system (104) of the vehicle; estimate the maximum achievable steering wheel angle, the maximum achievable steering wheel angle gradient, and the maximum achievable steering wheel angle acceleration that the steering system (104) can provide based on the estimated rack force and the estimated state of the actuator (130); and provide the steering system (104) with a planned path for the vehicle based on the estimated maximum achievable steering wheel angle, the maximum achievable steering wheel angle gradient, and the maximum achievable steering wheel angle acceleration;characterized in that the instructions configure the processor to: obtain an initial value for the rack force from a lookup table based on reference driving and environmental conditions, obtain an adjustment factor based on the current driving and environmental conditions from a model, and estimate the rack force by multiplying the initial value for the rack force by the adjustment factor.

Description

INITIATIONThis section contains information that serves to generally illustrate the context of the disclosure. Work of the present inventors, insofar as described in this introduction, as well as aspects of the description that may not otherwise be considered prior art at the time of filing, is neither expressly nor silently admitted as prior art against the present disclosure.The present disclosure relates to a system for real-time estimation of the angular, speed and acceleration capacity achievable by a steering actuator according to the preamble of claim 1, as can be seen in the broadest sense for example from DE 10 2017 119 215 A1.An object of the present invention is at least to provide an alternative variant thereto. This object is achieved by the subject matter of independent claim 1.The society of automotive engineers (SAE) defines six levels (levels 0-5) of autonomous driving. In SAE level 2-5 autonomous vehicles, the driving systems perform path planning, i.e., plan a path that a vehicle is to follow. An electric power steering (EPS) system in autonomous vehicles is controlled based on the path planning. For example, the autonomous driving system of a first vehicle following a second vehicle may plan a path for the first vehicle that allows the first vehicle to pass the second vehicle. The first vehicle autonomous driving system displays a predicted path opportunity for the first vehicle, which is then used to control the first vehicle EPS system to perform the scheduled passing operation. However, if the path is overpredicted, the actual path of the first vehicle may cause the first vehicle to collide with the second vehicle. Conversely, if the path is underpredicted, the planned path of the first vehicle may be too conservative and cause a maneuver problem or erroneously restrict the path so that the first vehicle cannot pass the second vehicle even if it is capable of doing so.OVERVIEWAn overview of different features is given below, which can serve to define and further develop the invention claimed in the present case.A system includes a processor and a memory storing instructions that, when executed by the processor, configure the processor to estimate a rack force for a steering system of a vehicle based on current driving and environmental conditions and estimate a state of an actuator of the steering system of the vehicle. The instructions configure the processor to estimate the maximum achievable angle, velocity, and acceleration for the steering system actuator based on the estimated rack force and the estimated state of the actuator. The instructions configure the processor to provide a path planned for the vehicle to the steering system based on the estimated maximum reachable angle, the estimated maximum reachable speed, and the estimated maximum reachable acceleration for the actuator of the steering system.The instructions may configure the processor to provide the path to the steering system when an angle, a velocity, and an acceleration for executing the path are within the estimated maximum reachable angle, the estimated maximum reachable velocity, and the estimated maximum reachable acceleration.The instructions may configure the processor to cause the steering system to execute the path when an angle, a velocity, and an acceleration for executing the path are within the estimated maximum reachable angle, the estimated maximum reachable velocity, and the estimated maximum reachable acceleration.The instructions configure the processor to, before providing the path to the steering system, modify the path to bring an angle, a velocity, and an acceleration for the actuator in accordance with the estimated maximum reachable angle, the estimated maximum reachable velocity, and the estimated maximum reachable acceleration.The instructions may configure the processor to calculate the rack force based on the current driving and environmental conditions used to estimate the rack force and to verify the accuracy of the estimated rack force by comparing the estimated rack force to the calculated rack force.The instructions may configure the processor to provide an indication for checking one or more sensors based on the accuracy.The instructions may configure the processor to provide an indication for updating a model used to estimate rack force based on the accuracy.The instructions configure the processor to obtain a first rack force value from a look-up table based on the reference driving and environmental conditions, obtain an adjustment factor from a model based on the current driving and environmental conditions, and estimate the rack force by multiplying the first rack force value by the adjustment factor, according to the invention.The instructions may configure the processor to calculate a first set of rack forces at a first set of vehicle speeds and a first set of steering data, estimate a second set of rack forces for a second set of vehicle speeds and a second set of steering data by interpolating the first set of rack forces, and generate the lookup table including the first and second sets of vehicle speeds.The instructions may configure the processor to train the model to calculate rack forces at different driving and environmental conditions and generate the adjustment factor by comparing a first rack force calculated at the current driving and environmental conditions with a second rack force calculated at reference driving and environmental conditions.A corresponding method includes estimating a rack force for a steering system of a vehicle based on current driving and environmental conditions and estimating a state of an actuator of the steering system of the vehicle. The method further includes estimating the maximum reachable angle, the maximum reachable speed, and the maximum reachable acceleration for the actuator of the steering system based on the estimated rack force and the estimated state of the actuator. The method further includes providing a path planned for the vehicle to the steering system based on the estimated maximum reachable angle, the maximum reachable speed, and the maximum reachable acceleration to the actuator of the steering system.The method may further include providing the path to the steering system when an angle, a speed, and an acceleration for executing the path are within the estimated maximum achievable angle, the estimated maximum achievable speed, and the estimated maximum achievable acceleration.The method may further include executing the path using the steering system when an angle, a speed, and an acceleration for executing the path are within the estimated maximum reachable angle, the estimated maximum reachable speed, and the estimated maximum reachable acceleration.The method may further include, before the path is provided to the steering system, modifying the path to bring an angle, a velocity, and an acceleration for the actuator in accordance with the estimated maximum reachable angle, the estimated maximum reachable velocity, and the estimated maximum reachable acceleration.The method may further include calculating the rack force based on the current driving and environmental conditions used to estimate the rack force, and checking the accuracy of the estimated rack force by comparing the estimated rack force to the calculated rack force.The method may further include providing an indication based on the accuracy for checking one or more sensors.The method may further include providing an indication based on the accuracy to update a model used to estimate the rack force.The method may further include obtaining a first value for the rack force from a look-up table based on the reference driving and environmental conditions, obtaining an adjustment factor from a model based on the current driving and environmental conditions, and estimating the rack force by multiplying the first value for the rack force by the adjustment factor.The method may further include calculating a first set of rack forces at a first set of vehicle speeds and a first set of steering data, estimating a second set of rack forces for a second set of vehicle speeds and a second set of steering data by interpolating the first set of rack forces, and generating the look-up table including the first and second sets of vehicle speeds.The method may further include training the model to calculate rack forces at different driving and environmental conditions and generating the adjustment factor by comparing a first rack force calculated at the current driving and environmental conditions with a second rack force calculated at reference driving and environmental conditions.Further areas of applicability of the present disclosure will become apparent from the detailed description, claims and drawings. The detailed description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the disclosure.BRIEF DESCRIPTION OF THE DRAWINGSThe present disclosure will become more fully understood from the detailed description and the accompanying drawings, wherein: FIG. 1 shows a functional block diagram of a system for real-time estimation of steering actuator achievable angular, velocity, and acceleration capacity according to the present disclosure, FIG. 2 shows a flow diagram of a method for real-time estimation of steering actuator achievable angular, velocity, and acceleration capacity according to the present disclosure, FIG. 3 shows a flowchart of a method for real-time estimating rack force using a look-up table and a trained model in accordance with the present disclosure, FIG. 4 shows a flow chart of a method for creating the look-up table by calculating a rack force under normal driving conditions, according to the present disclosure, FIG. 5 illustrates a method for training the model to provide an adjustment factor that can be applied to the rack force calculated under normal conditions to estimate the rack force in real time, in accordance with the present disclosure, FIG. 6 shows a flowchart of a method for calculating a maximum reachable steering wheel angle (SWA) in real time according to the present disclosure, FIG. 7 shows a flowchart of a method for calculating a maximum achievable steering wheel angular acceleration (SWAA) in real time according to the present disclosure, FIG. 8 shows a flowchart of a method for calculating a maximum achievable steering wheel angle gradient (SWG) in real time according to the present disclosure, FIG. 9 shows a flowchart of a method for checking a planned path trajectory in real time according to the present disclosure; and FIG. 10 shows a flow chart of a method for checking the accuracy of the rack force estimation according to the present disclosure.In the drawings, reference numerals may be reused to identify similar and / or identical elements.DETAILED DESCRIPTIONThe present disclosure provides a method for estimating the angular / velocity / acceleration capacity of a steering wheel for an autonomous driving system of a vehicle in real time. In the present disclosure, the terms rack force and torque are used by referring to a rack and pinion-based steering system and using an electric power steering (EPS) system used with the rack and pinion-based steering system. However, the present disclosure is not limited thereto. Rather, the rack and pinion steering system and the EPS system are used only as illustrative examples. The teachings of the present disclosure are also applicable to other types of steering systems and steering control systems. For example, the teachings may be applied to a steering system that includes two independent steering actuators that are each used to drive the two front (or rear) wheels without a rack being mounted therebetween. Unlike a rotary actuator used in the rack and pinion steering system, the independent steering actuators may be linear actuators. Accordingly, the term torque used with reference to the actuator in the present disclosure may be replaced with the term force.The method of the present disclosure uses a modeled, state-based estimate of steering actuator capacity and steering rack force under a variety of vehicle operating conditions. The maximum steering wheel angle, speed, and acceleration that a steering system can provide depends on various factors, such as the operating conditions of the vehicle, the environmental conditions, and the state of the steering actuator. Currently, path planning in autonomous vehicles does not take into account current driving conditions of the vehicle, such as temperature, vehicle load, tire pressure, etc. The method of the present disclosure obtains real-time feedback about current driving conditions of the vehicle, so that the autonomous driving system can optimize and revised the planned path to adapt it to steering system capacity in real-time.As discussed in detail below, the method considers these real-time parameters to calculate the steering wheel angular / velocity / acceleration capacity, rather than using a conservative / physical model. Therefore, the method can increase the accuracy of the path planning trajectory and reduce errors. The method uses a trained model, such as a neural network and a look-up table, to account for external factors in the operating environment of the vehicle, such as ambient temperature, tire pressure, vehicle mass, surface coefficient of friction, etc.More specifically, the method estimates the capacity for the steering wheel angle (SWA), the steering wheel angle gradient (SWG), and the steering wheel angular acceleration (SWAA) in real time by considering the state-based torque capacity of the steering actuator and the estimates of the rack force. The method uses the calculated results to verify or revision the planned path / trajectory. The method integrates data-controlled models (e.g., a look-up table and / or a neural network) to estimate the rack force in real-time, taking into account various vehicle conditions. The method also checks the accuracy of the estimated rack force and outputs alerts when the accuracy begins to decrease.The prior art does not take into account real-time vehicle conditions to produce the achievable steering wheel angle, steering wheel angle gradient, and steering wheel angular acceleration capacity. The prior art also does not consider the torque capacity of the steering actuator (or the force of a linear actuator) to generate the achievable steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration to allow path plan trajectories to be checked and planned. In contrast, by considering these factors, the method of the present disclosure improves the technical field of autonomous driving in general and the technical field of path planning and path execution by steering systems in autonomous vehicles in particular. These and other features of the present disclosure will be described in more detail below.The present disclosure is structured as follows. A system for real-time estimation of steering actuator achievable angular, velocity, and acceleration capacity according to the present disclosure is shown and described with reference to FIG. 1. A method for real-time estimation of steering actuator achievable angular, velocity, and acceleration capacity according to the present disclosure is shown and described with reference to FIG. 2. Various specific features of the method are shown and described below with reference to FIGS. 3-9.For example, a method for estimating a rack force in real time using a look-up table and a trained model is shown and described with reference to FIG. 3. A method of creating the look-up table by calculating a rack force under normal driving conditions will be shown and described with reference to FIG. 4. A method of training a model (e.g., a neural network) to provide an adjustment factor that can be applied to the rack force calculated under normal conditions to estimate the rack force in real time is shown and described with reference to FIG. 5.Next, methods for calculating the maximum reachable steering wheel angle, the maximum reachable steering wheel angular acceleration, and the maximum reachable steering wheel angle gradient will be shown and described with reference to FIGS. 6-8. Next, a method of checking a planned path / trajectory in real time will be shown and described with reference to FIG. 9. Finally, a method for checking the accuracy of the rack force estimation will be shown and described with reference to FIG. 10.FIG. 1 illustrates a system 100 for real-time estimation of steering actuator achievable angular, velocity, and acceleration capacity in accordance with the present disclosure. The system 100 is implemented in an autonomous vehicle or a SAE level 2-5 vehicle and includes a vehicle controller 102 and a steering system 104. In some examples, the vehicle controller 102 and the steering system 104 are part of a driving control system (e.g., an autonomous driving system) of the vehicle. In some examples, the vehicle controller 102 may be implemented at least partially in a cloud and communicate with the in-vehicle steering system 104 via a distributed communication system, such as the Internet, a cellular network, etc.The vehicle controller 102 includes a path planning module 110, a rack force estimator 112, and an actuator state estimator 114. In some examples, the actuator state estimator 114 may instead be incorporated into the steering system 104, as discussed below. The system 100 further includes a plurality of autonomous driving sensors (hereinafter, the first sensors) 120 and a plurality of vehicle sensors (hereinafter, the second sensors) 122. The vehicle controller 102 receives data from the first and second sensors 120, 122.The first sensors 120 may include, for example, radar, lidar, a laser scanner, one or more cameras, a global positioning system (GPS) receiver, an inertial navigation system (INS), an inertial measurement unit (IMU), etc. The second sensors 122 may sense a variety of vehicle and environmental parameters. The second sensors 122 may include, for example, suspension sensors, tire pressure sensors, axle load sensors, vehicle speed sensors, ambient temperature sensors, traction sensors, etc.The steering system 104 includes an electric power steering (EPS) system, and includes an actuator 130 and an actuator controller 132. Accordingly, the steering system 104 may also be referred to as an EPS system 104. The actuator 130 is fixed to a shaft 140 of a steering wheel 142. The shaft 140 of the steering wheel 142 is connected to a rack shaft via a pinion at a distal end of the shaft 140. In some examples, the actuator 130 may also be directly attached to the rack. The rack and pinion gear is a universal component of steering systems and is therefore neither shown nor described for the sake of brevity.Although not shown, in some examples, the steering system 104 may include two independent steering actuators that are each used to drive the two front (or rear) wheels without a rack disposed therebetween. Instead of the actuator 130 used in the rack and pinion steering system, the independent steering actuators may be linear actuators controlled and operated by the actuator controller 132.The path planning module 110 plans the path to be followed by the steering system 104 based on the inputs from the first sensors 120. The actuator controller 132 receives the scheduled path from the path planning module 110. The actuator controller 132 controls the actuator 130 that drives the shaft 140 of the steering wheel 142 according to the scheduled path received from the path planning module 110. In some examples, the actuator state estimator 114 may be included in the actuator controller 132 instead of the vehicle controller 102.The rack force estimator 112 estimates the rack force which is a net force generated by tire movements on a steering rack of the vehicle. The rack force is determined based on various factors such as the angle between tire and road, the surface profile of the road, and the speed of the vehicle. The rack force estimator 112 estimates the rack force as described below with reference to FIGS. 3-5. The rack force estimator 112 provides the rack force estimates to the path planning module 110 in real time.The actuator state estimator 114 estimates the state of the actuator 130 based on the data acquired from the actuator controller 132. For example, the torque capacity of the actuator 130 may change over time. Further, the torque capacity of the actuator 130 varies with temperature. The actuator state estimator 114 estimates the state of the actuator 130 by monitoring a displacement in the actuator capacity data (e.g., an actuator capacity curve) provided by an actuator manufacturer. The actuator state estimator 114 provides the path planning module 110 with the real-time estimation of the state of the actuator 130. The state estimation is performed in the form of a torque capacity curve of the actuator 130. For example, the actuator state estimator 114 outputs a graph shown in FIG. 6 that shows the rack force capacity of the actuator 130 in terms of actuator torque capacity (output) as a function of the steering wheel angle gradient (input).The path planning module 110 plans the path based on the rack force estimate received from the rack force estimator 112 and the actuator state estimate 130 received from the actuator state estimator 114, as discussed in detail below with reference to FIG. 2. For example, the path planning module 110 checks in real-time whether the planned path is within the conditions generated based on the rack force and actuator state estimate. The check prevents the planned path from being over- or underpredicted.The path planning module 110 provides the planned path to the actuator controller 132 for execution when the planned path is within the conditions. The path planning module 110 may also re-operate (e.g., adjust or fine tune) the planned path in real time based on the rack force and actuator state estimate. The path planning module 110 may also use the rack force and actuator state estimate for planning future paths and trajectories of the vehicle.The path planning module 110 estimates the maximum reachable steering wheel angle (SWA), steering wheel angle gradient (SWG), and steering wheel angular acceleration (SWAA) capacity of the steering system 104 in real-time based on the rack force and actuator state estimate. The actuator controller 132 executes the path scheduled by the path planning module 110, and the actuator 130 controls the steering wheel 142 according to the scheduled path executed by the actuator controller 132. Moreover, although not shown, many other subsystems of the vehicle 106 coordinate with the steering system 104 to execute the planned path. Non-limiting examples of the subsystems include the brake subsystem, the drive subsystem, etc.The operation of the system 100 will now be described in more detail with reference to FIGS. 2-10. Various methods will be described below with reference to FIGS. 2-10. Throughout the following disclosure, the term "controller" denotes the vehicle controller 102 and / or the actuator controller 132 or a plurality thereof depending on the described functionality. In some examples, the vehicle controller 102 and the actuator controller 132 may be implemented with a single controller.FIG. 2 shows a method 200 for real-time estimation of steering actuator achievable angular, velocity, and acceleration capacity in accordance with the present disclosure. For example, method 200 is carried out partially by vehicle control unit 102 and partially by actuator control unit 132. The method 200 may be repeated in a predetermined calibrated time period in order to check and / or plan the path / trajectory in an immediate time horizon. The method 200 can also be used to check a relatively longer-term planned path or a relatively longer-term planned trajectory on the basis of its profile and to revised (i.e. change or adapt) the longer-term planned path or the longer-term planned trajectory if required.At 202, the controller (e.g., vehicle controller 102) detects or receives the driving conditions (e.g., vehicle and environmental parameters) and the EPS state conditions (e.g., rack and actuator parameters). For example, the EPS state conditions may indicate the state of the entire EPS system (i.e., the entire steering system 104, which in some examples may also include the actuator state estimator 114 as described above). The vehicle and environmental parameters may include, for example, data received from the second sensors 122. The data received from the second sensors 122 may include, for example, (but is not limited to) tire pressure, ambient temperature, front axle load, road surface μ (coefficient of friction), etc. The rack and actuator parameters may include, for example, data received from the actuator controller 132. The data received from the actuator controller 132 may include (but is not limited to) the internal friction of the rack, the state (e.g., torque capacity) of the actuator 130, the supply voltage of the actuator controller 132 and the actuator 130, etc., for example.At 204, the controller (e.g., the vehicle controller 102) estimates the rack force and state based actuator capacity 130 based on the received data. The rack force estimation will be described in more detail with reference to FIG. 3. The state-based actuator capacity estimate includes, for example, estimating a displacement or deviation of the actuator torque capacity.At 206, the controller (e.g., the vehicle controller 102) calculates the maximum achievable steering wheel angle (SWA), the maximum achievable steering wheel angle gradient (SWG), and the maximum achievable steering wheel angular acceleration (SWAA) based on the estimated rack force and actuator capacity. These calculations will be described in more detail with reference to Figs. 6-8.At 208, the controller (e.g., the vehicle controller 102) determines whether one of the calculated maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration is inconsistent, as described in detail with reference to FIGS. 6-8. If no inconsistencies are detected in the computations, the controller checks at 210 whether the planned path / trajectories (planned based on the inputs from the first sensors 120) are within the conditions established by the calculated maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration.At 212, the controller (e.g., actuator controller 132) executes the planned path / trajectories when the planned path / trajectories are within the conditions established by the calculated maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration. If the planned path / trajectories are within the conditions, the controller (e.g., the vehicle controller 102) uses the conditions to plan a future path / trajectories for the vehicle at 214. Control returns to 202.If the planned path / trajectories are not within the conditions set by the calculated maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration, the controller (e.g., actuator controller 132) determines 215 whether to re-operate the planned path / trajectories. If the controller does not decide to override the planned path / trajectories, the controller ends at 216 and sends a warning to the user as explained below. However, if the controller decides to re-operate the planned path / trajectories, at 218, the controller re-operates the planned path / trajectories and control returns to 202.If the controller (e.g., the vehicle controller 102) determines at 208 that one of the calculated maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration is inconsistent, as described in detail with reference to FIGS. 6-8, then the controller ends at 216 and sends a warning to the user. The warning may be used, for example, to check whether the second sensors 122 are operating properly, whether the torque curve of the actuator 130 is correct, or to update the model used to estimate the rack force (as explained below with reference to FIG. 3 ).FIG. 3 illustrates a method 300 for estimating rack force in real time using a look-up table and a trained model (e.g., neural network) in accordance with the present disclosure. Method 300 is carried out, for example, by vehicle control unit 102. Although a data-driven approach to estimating rack force is described, other approaches combining physical models and data-based models may also be used.At 302, the controller (e.g., vehicle controller 102) uses an offline prepared lookup table (discussed below with reference to FIG. 4 ) to obtain the rack force under reference driving conditions. For example, the controller uses the current vehicle speed, the current steering wheel angle, and the current steering wheel angle gradient to determine the rack force in the look-up table. For example, the controller receives the current vehicle speed from one of the second sensors 122, and the current steering wheel angle and steering wheel angle gradient from the actuator controller 132.At 304, the controller (e.g., the vehicle controller 102) uses a trained model, such as a neural network (discussed below with reference to FIG. 5 ), to obtain an adjustment factor that adjusts or matches the value of the rack force under reference driving conditions to the current specific driving conditions. As discussed in detail below with reference to FIG. 5, the model may be trained to provide the adaptation factor based on a variety of factors. The factors may include, for example, parameters related to driving conditions, such as tire pressure, ambient temperature, front axle load, road surface μ (friction coefficient), among others, received from the second sensors 122. Additionally, the factors may include the current vehicle speed received from one of the second sensors 122, and the current steering wheel angle and steering wheel angle gradient received from the actuator controller 132.At 306, the controller (e.g., the vehicle controller 102) applies the adjustment factor to the value of the rack force under reference driving conditions to estimate the rack force under the current specific driving conditions. This rack force estimate is related to 204 in FIG. 2, and this estimated rack force is used at 206 in FIG. 2.FIG. 4 illustrates a method for creating the lookup table to which 302 in FIG. 3 relates. The method creates the lookup table by measuring a rack force during test drives with a vehicle and performing various steering maneuvers at different speeds under normal driving conditions. For example, the method measures a first set of rack forces at a first set of vehicle speeds and a first set of steering data (i.e., steering wheel angle and steering wheel angle gradient values). The method estimates a second set of rack forces for a second set of vehicle speeds and a second set of steering data (i.e., steering wheel angle and steering wheel angle gradient values) by interpolating the first set of rack forces. The method generates the look-up table with the first and second sets of rack forces generated for the first and second sets of vehicle speeds and the first and second sets of steering data (i.e., steering wheel angle and steering wheel angle gradient values).In use (i.e., at runtime to which 302 in FIG. 3 refers) the look-up table may provide the rack force under reference driving conditions based on the current vehicle speed and current steering data (i.e., current steering wheel angle and steering wheel angle gradient values) in real-time. The method is carried out, for example, by the vehicle control device 102.At 320, a test ride with the vehicle is under reference ride conditions and various steering maneuvers are performed at different vehicle speeds. At 322, the controller (e.g., vehicle controller 102) calculates the rack force at different vehicle speeds while the vehicle is being steered at different steering wheel angles and steering wheel angle gradients. For example, the controller receives the vehicle speed from one of the second sensors 122, and the current steering wheel angle and steering wheel angle gradient from the actuator controller 132.At 324, the controller (e.g., the vehicle controller 102) interpolates the rack force data and calculates the rack force at various additional vehicle speeds and various other steering data (i.e., steering wheel angle and steering wheel angle gradient values) that are not achieved during sample travel but may occur during actual operation (i.e., under actual driving conditions). At 326, the controller (e.g., the vehicle controller 102) creates a rack force lookup table for various steering wheel angle and steering wheel angle gradient values at different vehicle speeds under reference driving conditions. In use (i.e., at runtime, to which 302 in FIG. 3 refers), the current vehicle speed and current values of the steering data (e.g., steering wheel angle and steering wheel angle gradient) are input to the look-up table. The look-up table outputs a corresponding value for the rack force under reference driving conditions.FIG. 5 illustrates a method for training a model, such as a neural network, to provide an adaptation factor in accordance with the present disclosure. The adjustment factor may be applied to the rack force calculated under normal conditions to estimate the rack force in real time under the present specific driving conditions. The model may include a neural network, a machine learning model, a look-up table, a physical model, or another model. The model is trained to learn how the rack force changes when the driving conditions deviate from the reference driving conditions.In use (i.e., at runtime, to which 304 in FIG. 3 refers) the trained model may receive data about the current specific driving conditions of the vehicle and provide a multiplier (i.e., an adjustment factor) for the current specific driving conditions. The data may include, for example, tire pressure, ambient temperature, front axle load, road surface μ (coefficient of friction), etc. received from the second sensors 122. Additionally, the data may include the current vehicle speed received from one of the second sensors 122, and the current steering wheel angle and steering wheel angle gradient received from the actuator controller 132. The multiplier may be used (e.g., at 306 in FIG. 3 ) to adjust the rack force under reference driving conditions (e.g., obtained from the look-up table at 302) and provide the rack force under the current specific driving conditions of the vehicle. The method is carried out, for example, by the vehicle control device 102.At 340, the controller (e.g., the vehicle controller 102) monitors how the rack force changes as the reference driving conditions vary. For example, the controller monitors how the rack force changes by various factors that may change the rack force in use. The factors may include, for example, (but are not limited to) front axle load, ambient temperature, etc.At 342, the controller (e.g., vehicle controller 102) trains a model to calculate a steady state rack force for different driving conditions (e.g., for different driving and environmental parameters) at different steering wheel angles and steering wheel angle gradients. The driving and environmental parameters may include, for example, (but are not limited to) tire pressure, ambient temperature, front axle load, road surface μ (coefficient of friction), etc.At 344, the controller (e.g., the vehicle controller 102) trains the model to calculate an adjustment factor (e.g., a multiplier) by comparing the stationary rack force calculated under current driving conditions to the stationary rack force under reference driving conditions. In use (e.g., at 306 in FIG. 3 ), the current driving and environmental parameters as well as the current values of the steering data (e.g., steering wheel angle and steering wheel angle gradient) are input to the trained model. The trained model outputs a corresponding adjustment factor that can be used to multiply the rack force under reference driving conditions (e.g., obtained from the look-up table at 302) to provide the value of the rack force under the current specific driving conditions. The thus determined value of rack force under the current specific driving conditions is then used as the estimated rack force at 206.FIGS. 6-8 show and describe methods for calculating the maximum reachable steering wheel angle (SWA), the maximum reachable steering wheel angle gradient (SWG), and the maximum reachable steering wheel angle acceleration (SWAA), respectively, in real-time based on the estimated rack force and state-based actuator capacity. These figures also show and describe determining whether one of the calculated maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration is inconsistent (i.e., not consistent with the corresponding requirements of the planned path that include current values for steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration). The maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration are calculated to determine the maneuverability available to the vehicle control device 102 during path planning. That is, the vehicle controller 102 may determine whether a planned path from the steering system 104 may be performed based on the calculated maximum achievable factors steering wheel angle, steering wheel angle gradient, and steering wheel angular acceleration.FIG. 6 illustrates a method 400 for calculating a maximum achievable steering wheel angle (SWA) in real-time based on the estimated rack force and state-based actuator capacity, in accordance with the present disclosure. For example, the vehicle controller 102 performs the method 400.FIG. 6 also shows a graph in which the rack force on the Y-axis 390 is plotted against the pinion velocity or the steering wheel angle gradient on the X-axis 392. The graph output by the actuator state estimator 114 shows the rack force capacity of the actuator 130 in terms of actuator torque capacity (output) as a function of the steering wheel angle gradient (input). The graph shows, for example, a shift in rack force capacity based on temperature. Other factors, such as aging (weakening of the magnetic field) of actuator 130, may also cause a shift in rack force capacity. The graph shows a first dotted line curve 394 that shows the change in rack force relative to the steering wheel angle gradient at a relatively low temperature. The graph shows a second solid line curve 396 showing the change in rack force relative to the steering wheel angle gradient at a relatively high temperature. This diagram is referred to in the following description of Figs. 6-8.At 402, the controller (e.g., vehicle controller 102) uses the current steering wheel angle gradient to determine the rack force capacity of the EPS system from the graph. At 404, control sets a count n to zero (i.e., n=0) and sets the steering wheel angle (SWA) to the current steering wheel angle (i.e., SWA=current SWA). To calculate the maximum achievable steering wheel angle (to determine whether a larger steering wheel angle may be achieved for greater maneuverability), the controller (e.g., the vehicle controller 102) increments the steering wheel angle as follows until the rack force is greater than or equal to the rack force capacity of the EPS system.At 406, the controller (e.g., vehicle controller 102) increments count n by 1 (i.e., n=n+1). The controller calculates the rack force based on driving conditions, steering wheel angle gradient, vehicle speed, and steering wheel angle value. At 408, the controller (e.g., the vehicle controller 102) determines whether the rack force is greater than or equal to the rack force capacity of the EPS system from the graph. If the rack force is less than the rack force capacity of the EPS system, the controller (e.g., vehicle controller 102) records (i.e., stores) the steering wheel angle at 410. At 412, control (e.g., vehicle controller 102) increments the steering wheel angle by a predetermined small value Δ (e.g., SWA=SWA+Δ) and control returns to 406. Control repeats loop 406, 408, 410, 412 until the rack force is greater than or equal to (i.e., corresponds to) the rack force capacity of the EPS system.If the rack force is greater than or equal to the rack force capacity of the EPS system, the controller (e.g., vehicle controller 102) checks whether n=1 at 414. If n is not 1, the controller (e.g., vehicle controller 102) determines at 416 that the maximum achievable steering wheel angle is the most recently recorded steering wheel angle, and control ends. If n=1, meaning that the calculated rack force cannot maintain the current steering wheel angle (representing an inconsistency), the controller (e.g., vehicle controller 102) sends a warning to the user at 418. The warning may be used, for example, to check whether the second sensors 122 are operating properly, whether the torque curve of the actuator 130 is correct, or to update the model used to estimate the rack force (as discussed above with reference to FIG. 3 ), and control ends.FIG. 7 shows a method 440 for calculating a maximum achievable steering wheel angular acceleration (SWAA) in real time based on the estimated rack force and state-based actuator capacity, in accordance with the present disclosure. For example, the vehicle controller 102 performs the method 440.At 442, the controller (e.g., vehicle controller 102) uses the current steering wheel angle gradient to determine the rack force capacity of the EPS system from the graph shown in FIG. 6. To calculate the maximum steering wheel angular acceleration attainable, the controller (e.g., the vehicle controller 102) determines at 444 whether the rack force capacity of the EPS system from the graph is greater than or equal to the current rack force. If the rack force capacity of the EPS system is greater than or equal to the current rack force, the controller (e.g., the vehicle controller 102) calculates a difference between the rack force capacity of the EPS system and the current rack force at 446. Control divides the difference by the inertia of the steering system 104 to determine the maximum achievable steering wheel angular acceleration, and control ends.If the rack force capacity of the EPS system is less than the current rack force, the controller (e.g., the vehicle controller 102) detects the deviation at 448 and sends a warning to the user. The warning may be used, for example, to check whether the second sensors 122 are operating properly, whether the torque curve of the actuator 130 is correct, or to update the model used to estimate the rack force (as discussed above with reference to FIG. 3 ), and control ends.FIG. 8 shows a method 480 for calculating a maximum achievable steering wheel angle gradient (SWG) in real time based on the estimated rack force and state based actuator capacity, in accordance with the present disclosure. For example, vehicle controller 102 executes method 480.Since the rack force capacity is a function of the steering wheel angle gradient, the controller (e.g., the vehicle controller 102) uses the current rack force at 482 to determine the steering wheel angle gradient from the actuator capacity curve (see example intersection shown in FIG. 6 ). To calculate the maximum steering wheel angle gradient attainable, the controller (e.g., the vehicle controller 102) determines whether the current steering wheel angle gradient is greater than the determined steering wheel angle gradient at 484. If the current steering wheel angle gradient is less than or equal to the determined steering wheel angle gradient, the controller (e.g., the vehicle controller 102) determines at 486 that the determined steering wheel angle gradient is the maximum achievable steering wheel angle gradient, and the controller ends.If the current steering wheel angle gradient is greater than the determined steering wheel angle gradient, the controller (e.g., vehicle controller 102) detects the deviation at 488 and sends a warning to the user. The warning may be used, for example, to check whether the second sensors 122 are operating properly, whether the torque curve of the actuator 130 is correct, or to update the model used to estimate the rack force (as discussed above with reference to FIG. 3 ), and control ends.FIG. 9 illustrates a method 500 for checking (i.e., checking feasibility) a planned path trajectory in real-time, in accordance with the present disclosure. For example, vehicle controller 102 and actuator controller 132 perform method 500. At 502, the controller (e.g., the vehicle controller 102) determines whether the planned path satisfies the conditions (i.e., whether the steering wheel angle, steering wheel angle gradient, and steering wheel angle acceleration for the planned path are less than or equal to the calculated maximum reachable steering wheel angle, calculated maximum reachable steering wheel angle gradient, and calculated maximum reachable steering wheel angle acceleration). If the scheduled path satisfies the conditions, the controller (e.g., actuator controller 132) executes the scheduled path at 504, and control returns to 502. If the planned path does not meet the conditions, the controller (e.g., the vehicle controller 102) re-operates (i.e., adjusts) the planned path at 506, and control continues to 504.FIG. 10 illustrates a method 550 for post-verifying the accuracy of the rack force estimate in accordance with the present disclosure. For example, the vehicle controller 102 performs the method 550 to determine how accurate the rack force estimate was (e.g., at 204 in FIG. 2 ) and whether the model needs to be further trained based on the accuracy determination. For example, the vehicle controller 102 performs the method 550.At 552, the controller (e.g., vehicle controller 102) sets counts N=0 and N F= 0. At 554, control increments N (i.e., N=N+1). The controller calculates the rack force R 1 from the running conditions used to estimate the rack force. The controller calculates the rack force R 2 from the actually measured signals using the equation R 2=nT-Jα, where n is a ratio that converts the torque output of the EPS system into a force applied to the rack, T is a torque output of the steering system 104, J is the inertia of the steering system 104, and α is the steering wheel angular acceleration determined by processing the steering wheel angular gradient (e.g., by deriving the steering wheel angular gradient).At 556, control determines whether the difference between R 1 and R 2 is greater than or equal to a first threshold T 1. Control continues to 560 if the difference between R1and R2is less than the first threshold T1. If the difference between R1 and R2 is greater than or equal to a first predetermined threshold T1, control increments N F( i.e., N F= N F+1), at 558 and control continues to 560.At 560, control determines whether N is greater than or equal to a second predetermined threshold T2 and a ratio N F: N is greater than or equal to a third predetermined threshold T3. Control returns to 554 when N is less than the second predetermined threshold T2 and the ratio N F: N is less than the third predetermined threshold T3. If N is greater than or equal to the second predetermined threshold T2 and a ratio N F: N is greater than or equal to the third predetermined threshold T3, the controller (e.g., the vehicle controller 102) sends a warning to the user at 562. The warning may be used, for example, to check whether the second sensors 122 are operating properly, whether the torque curve of the actuator 130 is correct, or to update the model used to estimate the rack force (as discussed above with reference to FIG. 3 ), and control ends. The first, second, and third predetermined thresholds T 1, T 2, and T 3 may be calibrated.The above description is merely illustrative in nature and is not intended to limit the disclosure, its application, or its use. The broad teachings of the disclosure may be practiced in a wide variety of forms. Therefore, while this disclosure includes particular examples, the true scope of the disclosure should not be limited to the same as other changes will become apparent after studying the drawings, the specification, and the following claims. It should be appreciated that one or more steps within a method may be performed in different order (or simultaneously) without altering the principles of the present disclosure. Further, although the embodiments are described above as each having particular features, any one or more of those features described with respect to one embodiment of the disclosure may be implemented with and / or combined with features of any of the other embodiments, even if that combination is not expressly described. In other words, the described embodiments are not mutually exclusive, and permutations of one or more embodiments with one another remain within the scope of this disclosure.Spatial and functional relationships between elements (e.g., between modules, circuit elements, semiconductor layers, etc.) are described with various terms including "connected," "engaged," "coupled," "adjacent," "next to," "top on," "over," "under," and "arranged.". Where a relationship between first and second elements is not expressly described as "direct" in the above disclosure, this relationship may be a direct relationship in which no other intervening elements are present between the first and second elements, but also an indirect relationship in which one or more intervening elements are present (either spatially or functionally) between the first and second elements. As used herein, the term "A, B, and / or C" should be construed as being logical (A ORed with B ORed with C) using a non-exclusive logical OR operation and should not be understood as "at least one of A, at least one of B, and at least one of C.".In the figures, the direction of an arrow as indicated by the arrow head generally illustrates the flow of information (e.g., data or instructions) of interest for the illustration. For example, if element A and element B exchange a variety of information, but the information transmitted from element A to element B is relevant for illustration, the arrow may point from element A to element B. This unidirectional arrow does not mean that no other information is transmitted from element B to element A. Further, for information sent from element A to element B, element B may send requests or acknowledgements for the information to element A.In this application, which includes the definitions below, the term "module" or the term "controller" may be replaced with the term "circuit" The term "module" may refer to, be part of, or include an application specific integrated circuit (ASIC), a digital, analog, or mixed analog / digital discrete circuit, a digital, analog, or mixed analog / digital integrated circuit, a combinational logic circuit, a field programmable gate array (FPGA), a processor circuit (shared, dedicated, or group) that executes code, a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit, other suitable hardware components that provide the described functionality, or a combination of some or all of the above components, For example, in a system on chip.The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules connected via interface circuits. For example, multiple modules may allow load balancing. In another example, a server module (also referred to as a remote or cloud module) may perform some functions on behalf of a client module.The term "code" as used above may include software, firmware, and / or microcode, and may refer to programs, routines, functions, classes, data structures, and / or objects. The term "common processor circuit" includes a single processor circuit that executes code from multiple modules in part or in whole. The term "group processor circuit" includes a processor circuit that, in combination with additional processor circuits, executes code from one or more modules in part or in whole. References to multiple processor circuits include multiple processor circuits on discrete chips, multiple processor circuits on a single chip, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or a combination thereof. The term "common memory circuit" includes a single memory circuit that stores code from multiple modules in part or in whole. The term "group memory circuit" includes a memory circuit that, in combination with additional memories, stores code from one or more modules in part or in whole.The term "memory circuit" is a subset of the term computer readable medium. The term "computer-readable medium" as used herein does not include transitory electrical or electromagnetic signals propagating through a medium (e.g., on a carrier wave); the term "computer-readable medium" may therefore be considered tangible and non-transitory. Non-limiting examples of a non-transitory, tangible computer readable medium include non-transitory memory circuits (e.g., a flash memory circuit, an erasable programmable read only memory circuit, or a mask read only memory circuit), volatile memory circuits (e.g., a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (e.g., an analog or digital magnetic tape or a hard disk drive), and optical storage media (e.g., a CD, a DVD, or a Blu-ray Disc).The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general purpose computer to perform one or more particular functions included in computer programs. The above-described function blocks, flowchart components, and other elements serve as software specifications that can be translated into the computer programs by the routine work of an skilled technician or programmer.The computer programs include processor-executable instructions stored on at least one non-transitory, tangible computer-readable medium. The computer programs may also include or be based on stored data. The computer programs may include a basic input / output system (BIOS) that interacts with the hardware of the special purpose computer, device drivers that interact with certain devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc.The computer programs may include: (i) descriptive text to be parse, e.g., hypertext markup language (HTML), extensible markup language (XML), or javascript object notation (JSON), (ii) assembler code, (iii) object code generated from the source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. For example only, source code may be written with the syntax of languages that include C, C++, C#, objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript® HTML5 (Hypertext Markup Language 5th version), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Precursor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash® Visual Basic® Lua, MATLAB, SIMULINK, and Python®.

Claims

A system (100) comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the processor to: estimate a rack force on a steering system (104) of a vehicle based on current driving and environmental conditions, estimate a state of an actuator (130) of the steering system (104) of the vehicle, estimate the maximum achievable steering wheel angle, the maximum achievable steering wheel angle gradient, and the maximum achievable steering wheel angle acceleration that the steering system (104) can provide based on the estimated rack force and the estimated state of the actuator (130), and provide a path planned for the vehicle to the steering system (104) based on the estimated maximum achievable steering wheel angle, the maximum achievable steering wheel angle gradient, and the maximum achievable steering wheel angle acceleration; characterized in that, the instructions configure the processor to: obtain a first value for the rack force from a look-up table based on reference driving and environmental conditions, obtain an adjustment factor based on the current driving and environmental conditions from a model, and estimate the rack force by multiplying the first value for the rack force by the adjustment factor.The system (100) of claim 1, characterized in that the instructions configure the processor to communicate the path to the steering system (104) when a steering wheel angle, a steering wheel angle gradient, and a steering wheel angle acceleration for executing the path are within the estimated maximum reachable steering wheel angle, the estimated maximum reachable steering wheel angle gradient, and the estimated maximum reachable steering wheel angle acceleration.The system (100) of claim 1, characterized in that the instructions configure the processor to cause the steering system (104) to execute the path when a steering wheel angle, a steering wheel angle gradient, and a steering wheel angle acceleration for executing the path are within the estimated maximum reachable steering wheel angle, the estimated maximum reachable steering wheel angle gradient, and the estimated maximum reachable steering wheel angle acceleration.The system (100) of claim 1, characterized in that the instructions configure the processor to, before providing the path to the steering system (104), modify the path to bring a steering wheel angle, a steering wheel gradient, and a steering wheel acceleration for the actuator (130) in accordance with the estimated maximum reachable steering wheel angle, the estimated maximum reachable steering wheel angle gradient, and the estimated maximum reachable steering wheel angle acceleration.The system (100) of claim 1, characterized in that the instructions configure the processor to: calculate the rack force from the current driving and environmental conditions used to estimate the rack force, and verify the accuracy of the estimated rack force by comparing the estimated rack force to the calculated rack force.The system (100) of claim 5, characterized in that the instructions configure the processor to issue a request to verify one or more sensors (122) based on the obtained accuracy.The system (100) of claim 5, characterized in that the instructions configure the processor to provide a request to update a model used to estimate the rack force based on the obtained accuracy.The system (100) of claim 1, characterized in that the instructions configure the processor to: calculate a first set of rack forces from a first set of vehicle speeds and a first set of steering data, estimate a second set of rack forces from a second set of vehicle speeds and a second set of steering data by interpolating the first set of rack forces, and generate the look-up table with the first and second sets of vehicle speeds.The system (100) of claim 1, characterized in that the instructions configure the processor to train the model to: calculate rack forces at different driving and environmental conditions; and generate the adjustment factor by comparing a first rack force calculated under the current driving and environmental conditions with a second rack force calculated under the reference driving and environmental conditions.

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