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DE-102024126806-B4 - METHOD FOR EFFECTIVELY PREDICTING TIRE SATURATION FOR EVASIVE STEERING AND ACTIVE SAFETY CONTROL

DE102024126806B4DE 102024126806 B4DE102024126806 B4DE 102024126806B4DE-102024126806-B4

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.

Inventors

  • Puneet Bagga
  • Ami Woo
  • REZA ZARRINGHALAM
  • Mohammadali Shahriari
  • Hassan Askari
  • Abed Shammaa
  • Kin Man Michael Wong

Assignees

  • GM Global Technology Operations LLC

Dates

Publication Date
20260513
Application Date
20240917
Priority Date
20240729

Claims (10)

  1. A method for operating a vehicle (100) comprising: Obtaining a first data stream (604) relating to a road wheel angle for the vehicle (100); Obtaining a second data stream (608) relating to 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 a tire of the vehicle (100); and controlling a steering actuator (108) of the vehicle (100) to steer the vehicle (100) on the basis of the saturation level of the tire.
  2. Procedure according to Claim 1 , which further includes the temporal shifting 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. Procedure according to Claim 1 , wherein the reduced tire model (700) includes a normal inclination and a traction limit inclination, which further includes comparing the current inclination (410) with the normal inclination and the traction limit inclination to predict the saturation level.
  4. Procedure according to Claim 1 , which further includes 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. Procedure according to Claim 1 , which further includes 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) relating to a road wheel angle for the vehicle (100) and a second data stream (608) relating to a lateral acceleration for the vehicle (100); a processor configured to: determine a reduced tire model (700) for the vehicle (100) using the first data stream (604) and the second data stream (608); obtain a measurement of a current road wheel angle and a measurement of a current lateral acceleration; determine a current inclination (410) from the current road wheel angle and the current lateral acceleration; compare the current inclination (410) with the reduced tire model (700) to predict a saturation level of a tire of the vehicle (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) includes 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 tendency with a normal yaw tendency of the yaw relationship model (900) and a yaw limit tendency of the yaw relationship model (900).
  10. System according to Claim 6 , the processor is further configured to perform a security stole to add the lateral deviation 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.

Description

INTRODUCTION 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. 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. DE 10 2022 122 644 A1 describes a system for adaptive tire force prediction in a motor vehicle. DE 10 2019 212 933 A1 describes a control system for a vehicle. DE 10 2019 118 831 A1 describes a method for generating torque in a steering system. SUMMARY 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. 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. 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. 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. 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. 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. 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. In another exemplary embodiment, a system for operating a vehicle is disclosed. The system comprises a sensor for obtaining a first data stream relating to a road wheel angle for the vehicle and a second data stream relating to a transverse angle. The system consists of acceleration data 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, 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 a steering actuator of the vehicle to steer the vehicle based on the tire's saturation level. 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. In addition t