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.

翻译：在复杂场景下应对恶劣驾驶条件是自动驾驶车辆（AVs）面临的最大挑战。为解决该问题，本文提出一种基于前主动转向系统的分层运动规划与鲁棒控制策略，适用于不同湿滑路面附着系数且考虑车辆不确定参数的复杂场景。首先对人驾车辆（HVs）行为进行分析，并通过智能驾驶员模型（IDM）将跟车行为建模为汽车跟随模型。随后在上层规划层中，运动规划器采用人工势场（APF）算法对道路标线、边界及静态/动态障碍物等周边环境要素进行建模并生成最优轨迹。为实现对最优轨迹的跟踪，下层控制层采用基于线性矩阵不等式（LMI）优化的约束输出反馈鲁棒模型预测控制（RMPC）方法，该方法针对线性变参数（LPV）系统设计，可有效应对模型参数不确定性。此外，通过系统模型增广技术，本文提出的离线RMPC方法在提升计算效率与减小控制输入振动方面均优于三种现有RMPC方法（如偏移离线RMPC、在线RMPC及无模型增广的离线RMPC（offline RMPC w/o AM））。