Set-Theoretic Receding Horizon Control for Obstacle Avoidance and Overtaking in Autonomous Highway Driving

TL;DR

This paper introduces a fast set-theoretic receding horizon control method for obstacle avoidance and overtaking in autonomous highway driving, significantly reducing computational load while handling vehicle dynamics and uncertainties.

Contribution

It extends set-theoretic ellipsoidal MPC to motion planning for uncertain polytopic vehicle models, enabling real-time obstacle avoidance in highway scenarios.

Findings

Reduces online computational time by over 90% compared to standard methods.

Successfully handles dynamic traffic disturbances in high-fidelity simulations.

Adapts set-theoretic control to nonlinear vehicle models with uncertainties.

Abstract

This article addresses obstacle avoidance motion planning for autonomous vehicles, specifically focusing on highway overtaking maneuvers. The control design challenge is handled by considering a mathematical vehicle model that captures both lateral and longitudinal dynamics. Unlike existing numerical optimization methods that suffer from significant online computational overhead, this work extends the state-of-the-art by leveraging a fast set-theoretic ellipsoidal Model Predictive Control (Fast-MPC) technique. While originally restricted to stabilization tasks, the proposed framework is successfully adapted to handle motion planning for vehicles modeled as uncertain polytopic discrete-time linear systems. The control action is computed online via a set-membership evaluation against a structured sequence of nested inner ellipsoidal approximations of the exact one-step ahead controllable set within a receding horizon framework. A six-degrees-of-freedom (6-DOF) nonlinear model characterizes the vehicle dynamics, while a polytopic embedding approximates the nonlinearities within a linear framework with parameter uncertainties. Finally, to assess performance and real-time feasibility, comparative co-simulations against a baseline Non-Linear MPC (NLMPC) were conducted. Using the high-fidelity CARLA 3D simulator, results demonstrate that the proposed approach seamlessly rejects dynamic traffic disturbances while reducing online computational time by over 90% compared to standard optimization-based approaches.