Hierarchical Motion Planning and Offline Robust Model Predictive Control for Autonomous Vehicles

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.