METHOD FOR EFFECTIVELY PREDICTING TIRE SATURATION FOR EVASIVE STEERING AND ACTIVE SAFETY CONTROL

Abstract

Methods for operating a vehicle (100), comprising: Receiving an initial data stream (604) with respect to a road wheel angle for the vehicle (100); Receiving a second data stream (608) regarding a lateral acceleration for the vehicle (100); Determining a reduced tire model (700) for the vehicle (100) using the first data stream (604) and the second data stream (608); Obtaining a measurement of a current road wheel angle and a measurement of a current lateral acceleration; Determining a current inclination (410) from the current road wheel angle and the current lateral acceleration; Comparing the current inclination (410) with the reduced tire model (700) to predict a saturation level of one of the vehicle's tires (100); and Control of a steering actuator (108) of the vehicle (100) to steer the vehicle (100) on the basis of the saturation level of the tire.

Description

INTRODUCTION

[0001] The present disclosure relates to the operation of a vehicle and in particular to a system and a method for predicting a tire saturation level on the vehicle and adjusting the operation of the vehicle based on the predicted tire saturation level.

[0002] When a vehicle travels around a curve, whether on a road bend or in any terrain, the forces acting on the tire can approach full saturation. Once the tires are saturated, the vehicle operates in a non-linear range. The safety of the vehicle's operation in this non-linear range is critical for the operator, driver, or passenger. Generally, the operator does not need to know the tire's saturation level and is therefore unable to take appropriate action to prevent it. Consequently, it is desirable to develop a system and procedure for predicting a tire's saturation level and controlling the vehicle's trajectory to maintain its operation within a linear range.

[0003] DE 10 2022 122 644 A1 describes a system for adaptive tire force prediction in a motor vehicle.

[0004] DE 10 2019 212 933 A1 describes a control system for a vehicle.

[0005] DE 10 2019 118 831 A1 describes a method for generating torque in a steering system. SUMMARY

[0006] In an exemplary embodiment, a method for operating a vehicle is disclosed. A first data stream is obtained with respect to a road wheel angle for the vehicle. A second data stream is obtained with respect to a lateral acceleration for the vehicle. A reduced tire model is determined for the vehicle using the first and second data streams. A measurement of a current road wheel angle and a measurement of a current lateral acceleration are obtained. A current tilt is determined from the current road wheel angle and the current lateral acceleration. The current tilt is compared with the reduced tire model to predict a saturation level of a tire of the vehicle. A steering actuator of the vehicle is controlled to steer the vehicle based on the tire saturation level.

[0007] In addition to one or more of the features described here, the method further includes shifting the first data stream in time to generate a third data stream with time-shifted road wheel angle data, wherein the third data stream is aligned with the second data stream, and determining the reduced tire model using the third data stream and the second data stream.

[0008] In addition to one or more of the features described here, the reduced tire model includes a normal inclination and a traction limit inclination, and the method further includes comparing the current inclination with the normal inclination and the traction limit inclination to predict the saturation level.

[0009] In addition to one or more of the features described here, the procedure also includes learning a yaw relationship model for the vehicle and setting a model parameter of an adaptive vehicle model based on a comparison of a current yaw tendency with a normal yaw tendency of the yaw relationship model and a yaw limit tendency of the yaw relationship model.

[0010] In addition to one or more of the features described here, the process model parameter includes a front axle tire capacity and / or a rear axle tire capacity.

[0011] In addition to one or more of the features described herein, the method also includes sending a signal to an indicator when either a predicted tire capacity is close to a traction limit or the predicted tire capacity is close to the traction limit and a yaw rate has deviated from a desired yaw rate.

[0012] In addition to one or more of the features described herein, the method further includes adding a safety tolerance above a maximum lateral deviation permitted by the reduced tire model to obtain a target trajectory for the vehicle when a lateral deviation of a reference trajectory exceeds the maximum lateral deviation.

[0013] In another exemplary embodiment, a system for operating a vehicle is disclosed. The system comprises a sensor for receiving a first data stream relating to a road wheel angle for the vehicle and a second data stream relating to a lateral acceleration for the vehicle, and a processor. The processor is configured to determine a reduced tire model for the vehicle using the first and second data streams, to obtain a measurement of a current road wheel angle and a measurement of a current lateral acceleration, to determine a current tilt from the current road wheel angle and the current lateral acceleration, to compare the current tilt with the reduced tire model to predict a saturation level of a tire of the vehicle, and to control a steering actuator of the vehicle to steer the vehicle based on the tire saturation level.

[0014] In addition to one or more of the features described here, the processor is further configured to shift the first data stream in time to generate a third data stream with time-shifted road wheel angle data, with the third data stream aligned to the second data stream, and to determine the reduced tire model using the third data stream and the second data stream.

[0015] In addition to one or more of the features described here, the reduced tire model includes a normal inclination and a traction limit inclination, and the processor is further configured to compare the current inclination with the normal inclination and the traction limit inclination to predict the saturation level.

[0016] In addition to one or more of the features described here, the processor is also configured to learn a yaw relationship model for the vehicle and to set a model parameter of an adaptive vehicle model based on a comparison of a current yaw tendency with a normal yaw tendency of the yaw relationship model and a yaw limit tendency of the yaw relationship model.

[0017] In addition to one or more of the features described here, the model parameter includes a front axle tire capacity and / or a rear axle tire capacity.

[0018] In addition to one or more of the features described here, the processor is further configured to send a signal to a display when either a predicted tire capacity is close to a traction limit or the predicted tire capacity is close to the traction limit and a yaw rate has deviated from a desired yaw rate.

[0019] In addition to one or more of the features described here, the processor is further configured to add a safety tolerance above a maximum lateral deviation allowed by the reduced tire model in order to maintain a target trajectory for the vehicle when a lateral deviation of a reference trajectory exceeds the maximum lateral deviation.

[0020] In yet another exemplary embodiment, a vehicle is disclosed. The vehicle comprises a sensor for receiving a first data stream relating to the vehicle's road wheel angle and a second data stream relating to the vehicle's lateral acceleration, a steering actuator for steering the vehicle, and a processor. The processor is configured to determine a reduced tire model for the vehicle using the first and second data streams, obtain a measurement of the current road wheel angle and a measurement of the current lateral acceleration, determine a current tilt from the current road wheel angle and the current lateral acceleration, compare the current tilt with the reduced tire model to predict a saturation level of one of the vehicle's tires, and control the steering actuator to steer the vehicle based on the tire's saturation level.

[0021] In addition to one or more of the features described here, the processor is further configured to shift the first data stream in time to generate a third data stream with time-shifted road wheel angle data, with the third data stream aligned to the second data stream, and to determine the reduced tire model using the third data stream and the second data stream.

[0022] In addition to one or more of the features described here, the reduced tire model includes a normal inclination and a traction limit inclination, and the processor is further configured to compare the current inclination with the normal inclination and the traction limit inclination to predict the saturation level.

[0023] In addition to one or more of the features described here, the processor is also configured to learn a yaw relationship model for the vehicle and to set a model parameter of an adaptive vehicle model based on a comparison of a current yaw tendency with a normal yaw tendency of the yaw relationship model and a yaw limit tendency of the yaw relationship model.

[0024] In addition to one or more of the features described here, the processor is further configured to send a signal to a display when either a predicted tire capacity is close to a traction limit or the predicted tire capacity is close to the traction limit and a yaw rate has deviated from a desired yaw rate.

[0025] In addition to one or more of the features described here, the processor is further configured to add a safety tolerance above a maximum lateral deviation allowed by the reduced tire model in order to maintain a target trajectory for the vehicle when a lateral deviation of a reference trajectory exceeds the maximum lateral deviation.

[0026] The above features and advantages, and other features and advantages of the disclosure, are readily apparent from the following detailed description in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, advantages, and details are shown only by way of example in the following detailed description, which refers to the drawings; they show: Fig. 1 a vehicle according to an exemplary embodiment; Fig. 2 a top view of the vehicle performing a turning maneuver; Fig. 3 a diagram showing details of the steering control system in an explanatory embodiment; Fig. 4 a diagram illustrating the operation of the controller's modules for predicting current tire capacity, Fig. 5 a diagram of an explanatory operation of the data alignment module; Fig. 6 graphs of a road wheel angle and a lateral acceleration; Fig. 7 a reduced tire model in an explanatory embodiment; Fig. 8 a flowchart illustrating the operation of the tire capacity prediction module; Fig. 9 a yaw relationship model between the yaw angle of the vehicle and the road wheel angle in an explanatory embodiment; Fig. 10 a diagram illustrating a process for generating a steering command based on tire capacity; Fig. 11 a diagram illustrating the operation of an evaluation module to determine a need for a railway hire; and Fig. 12. A lane change scenario for the vehicle for illustrative purposes. DETAILED DESCRIPTION

[0028] The following description is by its very nature merely exemplary and is not intended to limit the present disclosure, its application, or uses. Naturally, throughout the drawings, corresponding reference numerals indicate identical or corresponding parts and features. As used herein, the term "module" refers to a processing circuit arrangement that may include an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or grouped), memory executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components providing the described functionality.

[0029] According to an exemplary embodiment, Fig. 1. A vehicle 100. The vehicle 100 can be an autonomous vehicle, a vehicle with cruise control, a vehicle with power steering, etc. The vehicle 100 includes a steering control system 102, which controls the steering of the vehicle. The steering control system 102 can be part of an autonomous driving system, an advanced driver assistance system (ADAS), or another suitable driver assistance system. The steering control system 102 includes an inertial measurement unit (IMU) 104, a steering angle sensor 106, a steering actuator 108, a display 110, and a controller 112. The inertial measurement unit 104 (IMU) measures a lateral acceleration α. y The vehicle's 100 and the steering angle sensor 106 measure a road wheel angle δ. rwa (or steering angle) of the vehicle. The inertial measurement unit 104 can also measure the yaw rate ω. zThe steering actuator 108 controls various components for steering the vehicle, including the steering column (not shown). The display 110 can show messages, instructions, or explanations to an operator of the vehicle 100 based on the results of the calculations disclosed herein.

[0030] The controller 112 receives measurements from the inertial measurement unit 104 and the steering angle sensor 106 and performs calculations to determine a saturation level for the vehicle's tires and to control the steering actuator 108 to steer the vehicle based on the saturation level results. The controller 112 may include a processing circuit arrangement that may comprise an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or grouped), memory that executes one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.The controller 112 can comprise a non-transitory computer-readable medium that stores instructions which, when processed by one or more processors of the controller 112, implement a method for learning a reduced tire model based on online measurements of lateral acceleration and road wheel angle, comparing a current inclination between the lateral acceleration and the road wheel angle with an inclination of the reduced tire model to predict a saturation level of a tire and / or an approach of the vehicle to full tire saturation where the vehicle operates in a non-linear region, and controlling the steering actuator 108 to steer the vehicle based on the predicted saturation level of the tire, according to one or more embodiments described in detail herein.

[0031] Fig. Figure 2 shows a top view 200 of vehicle 100 performing a turning maneuver. For illustrative purposes, the vehicle travels along a track 201 with a continuously increasing curvature. At point 202, vehicle 100 travels along a straight track. At point 204, the vehicle begins to turn. At point 206, the track has reached a curvature such that the vehicle's tires have become saturated. The method disclosed herein predicts the level of remaining tire capacity at a time (or point on the track) before the tires become saturated, as shown at point 208.

[0032] Fig. Figure 3 is a diagram 300 that shows details of the steering control system 102 in an explanatory embodiment. The diagram 300 shows the inertial measurement unit (IMU) 104, the steering angle sensor 106, and the controller 112, as well as various steering actuators, including, for example, an autonomous cruise control 302, a super cruise control 304, an intelligent system learning system 306, an interactive lane keeping assist 308, an automatic lane change assist 310, and an assisted evasive steering system 312.

[0033] The inertial measurement unit 104 feeds lateral acceleration measurements into the controller 112, and the steering angle sensor 106 feeds road wheel angle measurements into the controller. The controller 112 operates various modules for predicting tire capacity for the vehicle. These modules may include, but are not limited to, a data alignment module 314, a tire capacity curve learning module 316, an operating range learning module 318, and a tire capacity prediction module 320. The data alignment module 314 aligns the road wheel angle measurements with corresponding lateral acceleration measurements. The tire capacity curve learning module 316 learns or determines a reduced tire model based on a relationship between the lateral acceleration measurements and the time-aligned road wheel angle measurements.The operating range learning module 318 learns or determines a current tilt relative to the vehicle's current operation within the reduced tire model. The current tilt corresponds to or is determined using a current lateral acceleration and a time-aligned road wheel angle. The tire capacity prediction module 320 predicts the vehicle's remaining tire capacity (or tire saturation level) by comparing the current tilt to a tilt of the reduced tire model. The predicted remaining tire capacity is output to one or more steering actuators.

[0034] The predicted remaining tire capacity can be used to determine whether the vehicle is operating within a standard (linear) operating range or within a nonlinear operating range. A prediction that the vehicle is operating within a nonlinear operating range can affect the operation of Autonomous Cruise Control 302, Super Cruise Control 304, Intelligent System Learning 306, Interactive Lane Keeping Assist 308, and Automatic Lane Change Assist 310. Any operating range of the tire can be used to control the Assisted Evasive Steering System 312.

[0035] Fig. Figure 4 shows a diagram 400 illustrating the operation of the modules of the controller 112 for predicting a current tire capacity. The procedure involves receiving the road wheel angle data 402 and the lateral acceleration data 404 at the data alignment module 314. The road wheel angle data 402 is a first stream of angle measurements acquired over a certain time period, and the lateral acceleration data 404 is a second stream of acceleration measurements acquired over a certain time period. The data alignment module 314 determines a time delay between the first and second streams and aligns the first stream to the second stream in time, so that a selected road wheel angle is temporally aligned to a corresponding lateral acceleration. The data alignment module 314 outputs a third data stream with time-shifted road wheel angle data 406.

[0036] The tire capacity curve learning module 316 uses the time-shifted road wheel angle data 406 and the lateral acceleration data 404 to determine a reduced tire model. The tire capacity curve learning module 316 can use a selection of the time-shifted road wheel angle data 406 and the lateral acceleration data 404 over a historical time range prior to the current operation of the tire. The operating range learning module 318 uses the time-shifted road wheel angle data 406 and the lateral acceleration data 404 to determine a current inclination 410 for the vehicle. The operating range learning module 418 can use the time-shifted road wheel angle data 406 and the lateral acceleration data 404 over a time range closer to or associated with the current operation of the vehicle.An initial slope 408 of the reduced tire model and a current slope 410, which is associated with the current operation of the vehicle, are input into the tire capacity prediction module 320. The tire capacity prediction module 320 compares the current slope 410 with the initial slope 408 to predict the remaining tire capacity 412 of the tire.

[0037] Fig. Figure 5 shows a diagram 500 illustrating the operation of the data alignment module 314. A first stream of road wheel angle data 402 is received, measured, or monitored. The first stream of data comprises several road wheel angles spaced at intervals. A first sampling device 502 samples the first stream to detect a change in the road wheel angle. A counter 504 comprises a first clock 506 and a second clock 510. The first clock 506 sets a timestamp on each of the road wheel angle measurements after the detected change in the road wheel angle.

[0038] A second stream of lateral acceleration data 404 is received, measured, or monitored. This second stream of data comprises multiple lateral accelerations spaced apart in time. A second scanning device 508 samples the second stream to detect any change in lateral acceleration. The second clock 510 sets a timestamp on each lateral acceleration measurement after the detected change in lateral acceleration.

[0039] The counter 504 indicates a time difference τ time_difference The data alignment module 314 learns a reaction time between the application of a road wheel angle to the vehicle and the resulting lateral acceleration of the vehicle. A moving average module 512 calculates a moving average τ. sync the time differences τ time_difference, which are calculated by the counter 504. A synchronization module 514 shifts the road wheel angle measurements using the moving average τ. sync temporally, in order to align them with the lateral acceleration. The synchronization module 514 outputs the temporally aligned road wheel angle measurements 516.

[0040] Fig. Figure 6 shows graphs of a road wheel angle and lateral acceleration. A first graph 602 shows a first data stream 604 with road wheel angle measurements, and a second graph 606 shows a second data stream 608 with associated lateral acceleration measurements. The second stream 608 has a time delay relative to the first stream 604. The data alignment module 314 shifts the first stream 604 forward in time to form a third stream 610 (time-aligned road wheel angle) that is aligned with the second stream 608.

[0041] Fig. Figure 7 shows a reduced tire model 700 in an illustrative embodiment. The road wheel angle (δ) rwa ) is shown along an abscissa and the lateral acceleration (α y The graph is shown along the ordinate axis. Lateral acceleration data are plotted against road wheel angle data, and a regression analysis is performed to obtain the reduced tire model 700. The reduced tire model 700 (indicated by curve 701) comprises a normal operating range 702, which represents the normal operation of the tire, and a traction limit range 704, which represents a saturated range of tire operation.

[0042] Each area of ​​the reduced tire model 700 has a characteristic slope. The slope is given by Eq. (1): Cveh=ay_veh / δrwa

[0043] The normal operating range 702 is defined by a normal inclination C veh_normThis term indicates that the lateral acceleration generally increases linearly with the road wheel angle in the first quadrant. The traction limit range 704 generally occurs above a selected road wheel angle and a selected lateral acceleration and is defined by a traction limit inclination C. veh_lim The traction limit C is defined as follows: veh_lim may have zero inclination or essentially zero inclination.

[0044] Fig. Figure 8 shows a flowchart 800 illustrating the operation of the tire capacity prediction module 320. Box 802 determines the traction limit inclination of the traction limit range 704 of the reduced tire model 700. Box 804 determines a normal inclination of the normal operating range 702 of the reduced tire model 700. Box 806 determines the current inclination of the tire. Box 808 compares the current inclination with the traction limit inclination to determine whether the current inclination is approaching the traction limit inclination. Box 810 observes the normal inclination to determine whether it is decreasing or reaching a plateau (flattening). Box 812 uses the results from boxes 808 and 810 to predict the remaining tire capacity.

[0045] Fig. Figure 9 shows a yaw relationship model 900 between the yaw angle of the vehicle and the road wheel angle in an explanatory embodiment. The road wheel angle (δ) rwa ) is shown along the abscissa and the yaw rate (ω) z The graph is shown along the ordinate axis. Yaw data are plotted against the road wheel angle data, and a regression analysis is performed to obtain the yaw relationship model 900. The yaw relationship model 900 is represented by curve 901. The yaw relationship model 900 comprises a normal range 902, corresponding to the normal yaw operation of the vehicle, and a yaw limit range 904, representing a saturated operating range of the vehicle.

[0046] Each region along the yaw relationship model 900 is designated by a slope. The slope is as shown in Eq. (2): Rveh=ωz / δrwa

[0047] The normal range 902 is characterized by a normal yaw tendency Rveh_norm This is defined as the area in which the yaw rate generally increases linearly with the road wheel angle in the first quadrant. The yaw limit region 904 occurs over a selected road wheel angle and a selected yaw rate and is defined by a yaw relationship with a yaw limit inclination R. veh_lim designated as those that deviate from this normal yaw tendency R veh_norm deviates. In a scenario where tire saturation occurs on the front axle of the vehicle, curve 901 has a first slope 906 that is generally flat (i.e., the value of the first slope is zero or essentially zero). In a scenario where tire saturation occurs on the rear axle of the vehicle, curve 901 has a second slope 908 that approaches a vertical line (i.e., the value of the second slope approaches infinity).

[0048] The normal inclination C vehLateral dynamics are related to various model parameters of the vehicle, such as front axle tire capacity C. f and rear axle capacity C r A change in the normal inclination C veh is the sum of a change in front axle tire capacity C f and a change in rear axle tire capacity C r , as shown in Eq. (3): ΔCveh=ΔCf+ΔCr

[0049] A ratio of the change in rear axle tire capacity ΔC r to a change in the front axle tire capacity ΔC f is equivalent to a ratio between the yaw limit tendency R veh_lim and the normal yaw tendency R veh_norm , as shown in Eq. (4): ΔCrΔCf=Rveh_limRveh_norm

[0050] These model parameters can therefore be calculated based on the tire capacity model and the yaw model, in particular based on the normal yaw tendency and the yaw limit tendency. Considering the relationship of Eq. (4), the front axle tire capacity C f and the rear axle tire capacity C r will be updated over time, as shown in Eqs. (5) and (6): Cf(t+1)=Cf(t)+ΔCf Cr(t+1)=Cr(t)+ΔCr

[0051] Fig. Figure 10 is a diagram 1000 that represents a process for generating a steering command based on tire capacity. The diagram 1000 comprises one or more perception devices 1002 (e.g., camera, radar, lidar), a path planning module 1004, the tire capacity prediction module 320, an adaptive vehicle model 1006, and a model prediction controller 1008. The one or more perception devices 1002 receive data specifying an environmental environment and any objects within it. Such data can relate to other vehicles, lane markings, traffic signals, etc. The path planning module 1004 generates a reference path for the vehicle based on the data from the one or more perception devices 1002. The reference path is output to the model prediction controller 1008. The model prediction controller 1008 can return information to the path planning module 1004 to assist in future reference path planning.Such information may include, for example, the maximum achievable lateral acceleration and / or the maximum achievable yaw rate for the vehicle.

[0052] The tire capacity prediction module 320 provides the predicted tire capacity (including updated values ​​of model parameters C). f and C r ) to the adaptive vehicle model 1006. The adaptive vehicle model 1006 calculates a dynamic state of the vehicle using the updated values ​​of C. f and C rThe dynamic state is supplied to the model prediction controller 1008. The tire capacity prediction module 320 also issues an adaptation command to the model prediction controller 1008 upon detection of impending tire saturation. Upon receiving the adaptation command, the model prediction controller 1008 sets internal weights and references to modify or control vehicle behavior to adapt it to the tire saturation condition.

[0053] The model prediction controller 1008 performs an optimization process to output a target trajectory and / or a suitable steering command, using the reference trajectory, vehicle condition, and predicted tire capacity. The steering command is fed to the electronic power steering 1010, which moves the vehicle along the target trajectory. The target trajectory provides smoother or improved control of the vehicle.

[0054] Fig. Figure 11 is a diagram 1100 depicting the operation of an evaluation module 1102 to determine the need for a track adjustment. The evaluation module 1102 receives a tire capacity prediction from the tire capacity prediction module 320. A first algorithm 1104 assesses whether the predicted tire capacity is close to a saturation limit for the tire. If the predicted tire capacity is close to the saturation limit, a first warning signal 1106 can be sent (i.e., to the display 110) to alert the operator that the vehicle's operation is approaching a non-linear range.

[0055] A second algorithm 1108 receives a predicted yaw rate and compares it to a desired yaw rate. If the tire capacity is near its saturation limit and the yaw rate deviates from the desired yaw rate, a second warning signal 1110 can be sent (i.e., to the indicator 110) indicating that the vehicle is operating in the non-linear operating range. The first algorithm 1104 is proactive and can detect that the vehicle is approaching the non-linear operating range before the second algorithm 1108 can detect the non-linear range.

[0056] Fig.Figure 12 shows a lane-change scenario 1200 for the vehicle for explanatory purposes. The vehicle 100 is positioned in a first lane 1202, its straight path being obstructed by an object 1204. To avoid the object 1204, the vehicle 100 plans a reference path 1210 to move to a new location 1206 in a second lane 1208 adjacent to the first lane 1202. The reference path 1210 follows outside a predicted tire capacity limit 1212 for the vehicle. The predicted tire capacity limit 1212 is used to estimate a maximum lateral deviation that the controller can track. A target path 1214 is therefore calculated to accommodate the tire capacity limit. The lateral deviation of the target path 1214 is calculated by adding a safety tolerance to the maximum lateral deviation within the predicted tire capacity limit 1212. The destination lane 1214 is used to maneuver the vehicle into the second lane 1208.

[0057] The terms "a" and "an" do not denote a limitation of quantity, but rather indicate the presence of at least one of the cited subject matter. The term "or" means "and / or" unless clearly indicated otherwise by the context. Reference throughout the patent description to "an aspect" means that a specific element (e.g., feature, structure, step, or property) described in connection with that aspect is contained in at least one aspect described therein and may or may not be present in other aspects. Furthermore, it should be understood that the described elements can be combined in any suitable manner across the various aspects.

[0058] When an element such as a layer, film, area, or substrate is described as being "on" another element, it can be directly on top of that element, or intermediate elements may be present. Conversely, when an element is described as being "directly on" another element, no intermediate elements are present.

[0059] Unless otherwise stated herein, all test standards are the most recent applicable standard since the filing date of this application, or, if priority is claimed, the filing date of the earliest priority application in which the test standard appears.

[0060] Unless otherwise defined, technical and scientific terms used herein have the same meanings as they would normally be understood by a person skilled in the art in the field to which this disclosure belongs.

Claims

[1] Method for operating a vehicle (100) comprising: Receiving an initial data stream (604) with respect to a road wheel angle for the vehicle (100); Receiving a second data stream (608) regarding a lateral acceleration for the vehicle (100); Determining a reduced tire model (700) for the vehicle (100) using the first data stream (604) and the second data stream (608); Obtaining a measurement of a current road wheel angle and a measurement of a current lateral acceleration; Determining a current inclination (410) from the current road wheel angle and the current lateral acceleration; Comparing the current inclination (410) with the reduced tire model (700) to predict a saturation level of one of the vehicle's tires (100); and Control of a steering actuator (108) of the vehicle (100) to steer the vehicle (100) on the basis of the saturation level of the tire. [2] The method of claim 1, further comprising the temporal shift of the first data stream (604) to generate a third data stream (610) with temporally shifted road wheel angle data, wherein the third data stream (610) is aligned with the second data stream (608), and the determination of the reduced tire model (700) using the third data stream (610) and the second data stream (608). [3] Method according to claim 1, wherein the reduced tire model (700) comprises a normal inclination and a traction limit inclination, further comprising comparing the current inclination (410) with the normal inclination and the traction limit inclination to predict the saturation level. [4] Method according to claim 1, further comprising learning a yaw relationship model (900) for the vehicle (100) and setting a model parameter of an adaptive vehicle model (1006) based on a comparison of a current yaw tendency with a normal yaw tendency of the yaw relationship model (900) and a yaw limit tendency of the yaw relationship model (900). [5] Method according to claim 1, further comprising adding a safety tolerance above a maximum lateral deviation permitted by the reduced tire model (700) to obtain a target path (1214) for the vehicle (100) when a lateral deviation of a reference path (1210) exceeds the maximum lateral deviation. [6] System for operating a vehicle (100) comprising: a sensor (104, 106) for receiving a first data stream (604) with respect to a road wheel angle for the vehicle (100) and a second data stream (608) with respect to a lateral acceleration for the vehicle (100); a processor configured to do this: to determine a reduced tire model (700) for the vehicle (100) using the first data stream (604) and the second data stream (608); to obtain a measurement of a current road wheel angle and a measurement of a current lateral acceleration; to determine a current inclination (410) from the current road wheel angle and the current lateral acceleration; to compare the current inclination (410) with the reduced tire model (700) to predict a saturation level of one of the vehicle's tires (100); and to control a steering actuator (108) of the vehicle (100) in order to steer the vehicle (100) on the basis of the saturation level of the tire. [7] System according to claim 6, wherein the processor is further configured to shift the first data stream (604) in time to generate a third data stream (610) with time-shifted road wheel angle data, wherein the third data stream (610) is aligned with the second data stream (608), and to determine the reduced tire model (700) using the third data stream (610) and the second data stream (608). [8] System according to claim 6, wherein the reduced tire model (700) comprises a normal inclination and a traction limit inclination and the processor is further configured to compare the current inclination (410) with the normal inclination and the traction limit inclination to predict the saturation level. [9] System according to claim 6, wherein the processor is further configured to learn a yaw relationship model (900) for the vehicle (100) and to set a model parameter of an adaptive vehicle model (1006) based on a comparison of an actual yaw inclination with a normal yaw inclination of the yaw relationship model (900) and a yaw limit inclination of the yaw relationship model (900). [10] System according to claim 6, wherein the processor is further configured to add a safety tolerance above a maximum lateral deviation allowed by the reduced tire model (700) in order to obtain a target path (1214) for the vehicle (100) when a lateral deviation of a reference path (1210) exceeds the maximum lateral deviation.

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