Multi-mode switching-based model predictive control approach for longitudinal autonomous driving with acceleration estimation

Abstract

This study proposes a multi-mode switching longitudinal autonomous driving system based on model predictive control (MPC) with acceleration estimation of proceeding vehicle. A hierarchical control framework composed of three layers is utilised. In the first layer, five longitudinal driving scenarios are defined based on emergency degree. In the second layer, the MPC for longitudinal autonomous driving is designed and serving as the upper controller. Among which a non-linear tracking differentiator is used for acceleration estimation of preceding vehicle. In the third layer, the inverse longitudinal vehicle system dynamic model with strong non-linearity is considered in the lower controller. Proportional-integral-derivative feedback and feedforward control are combined to track the desired acceleration. Simulation and hardware-in-loop test results show that the multi-mode switching longitudinal autonomous driving system is feasible and effective, and has important value for engineering application.