A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

Hanbing Wei,Xing Chen,Jin Xu,Yanhong Wu

doi:10.3390/app10072626

Abstract

For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs.

Highlights

Significant progress has been conducted in the field of perception, decision making, path planning and control authority for autonomous vehicles (AVs) in the past few decades
Using the aforementioned withoriginal the experimental results, the key parameters of three subjects are identification identified in algorithm
Indicate greater muscle applied In byparticular, the driver.higher stiffness and damping coefficients indicate greater muscle force applied by the driver

Summary

Introduction

Significant progress has been conducted in the field of perception, decision making, path planning and control authority for autonomous vehicles (AVs) in the past few decades. It is still far away from widespread market penetration because of its unmatured technology. The dynamic model is of great significance for the understanding of the intrinsic and developing control authority of HVSC. In this way, many existing scholarly works have demonstrated the contributions associated with the dynamic model in the HVSC domain. By investigating the influence of haptic aids on the pilot’s NMS response, an online estimator of the time-varying NMS dynamic method based on Recursive Least

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Results

Conclusion