Obstacle Avoidance of Autonomous Vehicles: An LPVMPC with Scheduling Trust Region

TL;DR

This paper introduces a linear parameter-varying model predictive control approach with a scheduling trust region for obstacle avoidance in autonomous vehicles, balancing computational efficiency and performance.

Contribution

It proposes a novel LPV-MPC control scheme with a scheduling trust region to improve obstacle avoidance while ensuring feasibility and reducing computational complexity.

Findings

The method achieves real-time obstacle avoidance in autonomous driving scenarios.

It maintains feasibility through the scheduling trust region constraints.

Compared to nonlinear MPC, it offers faster solutions with acceptable performance loss.

Abstract

Reference tracking and obstacle avoidance rank among the foremost challenging aspects of autonomous driving. This paper proposes control designs for solving reference tracking problems in autonomous driving tasks while considering static obstacles. We suggest a model predictive control (MPC) strategy that evades the computational burden of nonlinear nonconvex optimization methods after embedding the nonlinear model equivalently to a linear parameter-varying (LPV) formulation using the so-called scheduling parameter. This allows optimal and fast solutions of the underlying convex optimization scheme as a quadratic program (QP) at the expense of losing some performance due to the uncertainty of the future scheduling trajectory over the MPC horizon. Also, to ensure that the modeling error due to the application of the scheduling parameter predictions does not become significant, we propose the concept of scheduling trust region by enforcing further soft constraints on the states and inputs. A consequence of using the new constraints in the MPC is that we construct a region in which the scheduling parameter updates in two consecutive time instants are trusted for computing the system matrices, and therefore, the feasibility of the MPC optimization problem is retained. We test the method in different scenarios and compare the results to standard LPVMPC as well as nonlinear MPC (NMPC) schemes.