Shared Control for Giving Ordinary Drivers Expert Level Drifting Skills

Expanding the shared control paradigm for autonomous vehicles to include the open-loop unstable region can improve safety in extreme vehicle conditions, such as tire saturation from encountering low friction or from emergency lane changes. Whereas the state-of-the-art sacrifices agility for stability by restricting the vehicle domain to operate in the open-loop stable regime, this paper expands existing shared control approaches to ensure safety outside the open-loop stable region. A Nonlinear Model Predictive Control framework balances a cost to follow the driver's commands and costs for safety, such that it does not disturb the driver in safe states and intervenes only when necessary in dangerous states seamlessly. Specifically, a novel cost function is formulated to incorporate driver intent, while ensuring safety through costs on the maximum phase recovery envelope and track bounds. Ensuring sufficient model fidelity for drifting is achieved through a nonlinear bicycle model that incorporates a nonlinear tire model and wheelspeed dynamics. Circular drifting experiments with a full-scale vehicle demonstrate the ability of the controller to follow driver commands in safe states, while augmenting the driver commands to avoid situations of track bound violation and spin-out when drifting a circle.