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

TL;DR

This paper introduces a hierarchical motion planning and robust control framework for autonomous vehicles operating in complex, slippery conditions, integrating trajectory generation with offline robust model predictive control to enhance safety and efficiency.

Contribution

It presents a novel offline RMPC approach with augmented system modeling that outperforms existing methods in computational efficiency and input smoothness.

Findings

The proposed method improves computation time over existing RMPC approaches.

It reduces input vibrations, leading to smoother vehicle control.

The approach effectively handles vehicle uncertainties and complex scenarios.

Abstract

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.