摘要：AbstractAccurate detection of driver steering interventions is crucial for assuring safe control transitions from the autonomous driving system (ADS) to the human driver as well as for seamless human-automation shared driving. In particular, the estimated human driver steering torque is an important decisive signal for initiating such taking-over transitions. During the past decade, numerous research endeavors were made to study the driver torque estimation problems. Yet, the majority of the existing solutions are based upon the traditional power-assisted steering (PAS) systems, which rely on passive hydraulic, electric, or electrohydraulic power assistance. On the other hand, the maturation of advanced driver-assistance systems (ADAS) and the advancement in autonomous driving have promoted the broad adaption of the next-generation steer-by-wire (SBW) technology. To tackle the new problem of human driver torque estimation in the SBW system setup, this paper proposes a novel model-based estimator. To begin with, a state-space model for the uncertain steering system dynamics is derived. The corresponding observability is subsequently verified. Next, in light of the generalizedH2filtering theory, a multi-objective robust observer is synthesized. Finally, the accuracy and robustness of the proposed driver torque estimation algorithm are validated in simulations.
