A Novel Predictive Haptic Control Interface for Automation-to-Human Takeover of Automated Vehicles

Abstract

Human driver's intervention is still needed before achieving fully autonomous driving, and this poses a great challenge for ensuring safety and smoothness during control transition from automation to human driver. This paper addresses this challenge by proposing a novel predictive haptic takeover control method. First, a new human-machine interaction model considering two phases, namely machine dominance and human dominance, is developed to describe the interactive behaviors between human driver and automation system during takeover. Based on the model built, a novel haptic takeover controller is designed by using model predictive control approach in order to achieve a safe and smooth handover transition. The optimal steering input is derived by considering the expected paths of both human driver and automation system. The takeover controller generates predictive haptic steering torque based on driver's states, guiding and assisting human driver to gradually resume manual control. Simulation is conducted under a typical driving condition with proposed method. Simulation results indicate that the automation-to-human control transfer is completed safely and smoothly with the proposed haptic takeover controller, and meanwhile the vehicle performance and stability are also guaranteed.