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

Hung Duy Nguyen, Minh Nhat Vu, Nguyen Ngoc Nam, Kyoungseok Han

TL;DR

This work tackles autonomous driving in low-adhesion, complex scenarios by a two-layer framework: an APF-based upper-layer motion planner that generates collision-avoiding trajectories and an offline constrained RMPC lower-layer that robustly tracks these trajectories under an LPV model with uncertain tire stiffness. The IDM-based behavior of human vehicles and front-active steering are integrated into the modeling to reflect realistic traffic interactions, while the RMPC uses LMI-based robustness and a look-up table to yield real-time gains. Key contributions include a traffic-behavioral obstacle formulation, an augmented LPV RMPC design with Lyapunov stability guarantees, and demonstrated efficiency advantages over online and non-augmented baselines, including reduced input vibrations. The approach offers improved safety and stability for AVs operating under varying road adhesion, with practical implications for real-time control in harsh conditions.

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.

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

TL;DR

This work tackles autonomous driving in low-adhesion, complex scenarios by a two-layer framework: an APF-based upper-layer motion planner that generates collision-avoiding trajectories and an offline constrained RMPC lower-layer that robustly tracks these trajectories under an LPV model with uncertain tire stiffness. The IDM-based behavior of human vehicles and front-active steering are integrated into the modeling to reflect realistic traffic interactions, while the RMPC uses LMI-based robustness and a look-up table to yield real-time gains. Key contributions include a traffic-behavioral obstacle formulation, an augmented LPV RMPC design with Lyapunov stability guarantees, and demonstrated efficiency advantages over online and non-augmented baselines, including reduced input vibrations. The approach offers improved safety and stability for AVs operating under varying road adhesion, with practical implications for real-time control in harsh conditions.

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.
Paper Structure (19 sections, 25 equations, 9 figures, 1 table)

This paper contains 19 sections, 25 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Modeling
  3. Traffic Environment Model
  4. Path Tracking Model
  5. Linear Parameter Varying System
  6. Problem Formulation And Hierarchical Framework
  7. Problem Formulation
  8. Architecture of Hierarchical Framework
  9. Motion Planning Approach
  10. Objective Function
  11. Traffic-behavioral Obstacle Formulation
  12. Offline Constrained RMPC Design
  13. Case Studies
  14. Baseline Controllers
  15. Online Constrained Robust Model Predictive Control (Online), see, wan2003efficient
  16. ...and 4 more sections

Figures (9)

  • Figure 1: Schematic of highway driving strategy.
  • Figure 2: Hierarchical motion planning and control strategies.
  • Figure 3: Case studies: (a) Normal scenarios, (b) Aggressive scenarios, and (c) Unexpected scenarios with a simple pedestrian speed profile.
  • Figure 4: Input parameters: (a) Steering wheel angle and (b) Steering wheel angle rate in normal scenarios.
  • Figure 5: State parameters: (a) Lateral position error, (b) Lateral velocity error, (c) Yaw angle error, and (d) Yaw rate error in normal scenarios.
  • ...and 4 more figures