  Driving vehicles in complex scenarios under harsh conditions is the biggest
challenge for autonomous vehicles (AVs). To address this issue, we propose
hierarchical motion planning and robust control strategy using the front-active
steering system in complex scenarios with various slippery road adhesion
coefficients while considering vehicle uncertain parameters. Behaviors of human
vehicles (HVs) are considered and modeled in the form of a car-following model
via the Intelligent Driver Model (IDM). Then, in the upper layer, the motion
planner first generates an optimal trajectory by using the artificial potential
field (APF) algorithm to formulate any surrounding objects, e.g., road marks,
boundaries, and static/dynamic obstacles. To track the generated optimal
trajectory, in the lower layer, an offline-constrained output feedback robust
model predictive control (RMPC) is employed for the linear parameter varying
(LPV) system by applying linear matrix inequality (LMI) optimization method
that ensures the robustness against the model parameter uncertainties.
Furthermore, by augmenting the system model, our proposed approach, called
offline RMPC, achieves outstanding efficiency compared to three existing RMPC
approaches, e.g., offset-offline RMPC, online RMPC, and offline RMPC without an
augmented model (offline RMPC w/o AM), in both improving computing time and
reducing input vibrations.
