Tire State Stiffness Prediction for Improving Path Tracking Control During Emergency Collision Avoidance

Shaosong Li,Bangcheng Zhang,Guodong Wang,Guoying Chen,Hong Chen

doi:10.1109/access.2019.2959043

Shaosong Li, Bangcheng Zhang + Show 3 more

Open Access

https://doi.org/10.1109/access.2019.2959043

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 12	License type: CC BY 4.0

Affiliation: Jilin University, Changchun University of Technology

Abstract

In this study, a model predictive path tracking control method based on the prediction of tire state stiffness is proposed to improve the path tracking performance at the limit of vehicle dynamics. Considering the influence of the nonlinear properties of tire force on vehicle dynamics, a nonlinear UniTire model is established, based on which a state stiffness 3D look-up table is designed to linearize the nonlinear tire model. The tire state stiffness in the prediction horizon is predicted by the vehicle motion model using the reference path information. A new linear time-varying path tracking control model in the prediction horizon is designed based on the predicted tire state stiffness. A nonlinear model predictive controller and a traditional linear time-varying model predictive controller are also designed and compared with the proposed method to verify the effectiveness and advantage of the latter. Results clearly show an improved control performance of the proposed method compared with the traditional method under the limit condition. Moreover, the calculation speed of the proposed method is faster than that of the nonlinear model predictive control method.

Highlights

Human errors are identified as the primary reason for fatal driving accidents, with approximately 75 % of traffic accidents related to driver failures [1]
For LTI-model predictive control (MPC) and linear time-varying MPC (LTV-MPC), the precision of tire model affects the accuracy of linearization and the final control effect
This study proposes a new LTV model predictive path tracking controller that considers the nonlinear trend of tire force in the prediction horizon

Summary

Introduction

Human errors are identified as the primary reason for fatal driving accidents, with approximately 75 % of traffic accidents related to driver failures [1]. Existing driver assistance systems primarily avoid collisions by emergency braking, but in some situations Of pedestrians and obstacles, high-speed driving conditions, etc.), collisions cannot be avoided even at full brake. Active steering intervention provides a new solution for emergency collision avoidance control in limit conditions. The main task of the former is to assist the driver to steering better and maintain the stability of the vehicle. The task of the latter is to plan a reasonable collision avoidance path and track the path well. Typical path planning methods for steering-based obstacle avoidance include clothoid and sigmoid functions [5], [6]. The traditional control methods of path tracking control (robust control, fuzzy control, and sliding mode control [7]–[9]) depend on the current

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Results

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