Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving

TL;DR

This paper develops and validates a real-time nonlinear model predictive control system for autonomous driving, tested on a Ford Focus across simulation and real-world environments, ensuring safety and performance.

Contribution

It introduces a real-time NMPC strategy with full vehicle validation, integrating advanced optimization and validation techniques for autonomous driving applications.

Findings

Successful deployment on a Ford Focus vehicle

Validated across simulation and physical testing environments

Achieved high-speed collision avoidance and lane change performance

Abstract

In this paper, we present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle. Non-linear model predictive control (NMPC) is designed and deployed on a system with hard real-time constraints. We show the properties of sequential quadratic programming (SQP) optimization solvers that are suitable for driving tasks. Importantly, the designed algorithms are validated based on a standard automotive XiL development cycle: model-in-the-loop (MiL) with high fidelity vehicle dynamics, hardware-in-the-loop (HiL) with vehicle actuation and embedded platform, and full vehicle-hardware-in-the-loop (VeHiL). The autonomous driving environment contains both virtual simulation and physical proving ground tracks. NMPC algorithms and optimal control problem formulation are fine-tuned using a deployable C code via code generation compatible with the target embedded toolchains. Finally, the developed systems are applied to autonomous collision avoidance, trajectory tracking, and lane change at high speed on city/highway and low speed at a parking environment.