Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing

TL;DR

This paper systematically benchmarks various feedforward steering controllers for autonomous racing, revealing that a new empirical approach offers the best robustness and lap times despite lower prediction accuracy.

Contribution

It introduces a novel polynomial surface fit (EHD) for feedforward steering control and evaluates its performance against learning-based and empirical methods in a realistic simulation.

Findings

Learning-based controllers have the lowest prediction errors.

Improved prediction accuracy does not necessarily improve lap times.

The proposed EHD method offers superior closed-loop robustness and lap times.

Abstract

Feedforward steering control is a key component of hierarchical control architectures for autonomous racing. The goal is to reduce steering corrections from the feedback controllers by predicting the vehicle's inverse lateral dynamics. This paper presents a systematic benchmark of two learning-based and two empirical (analytical) feedforward steering controllers. We introduce a new \acf{ehd} formulation based on a polynomial surface fit that captures velocity-dependent nonlinear steering behavior with minimal parametrization. We test the feedforward controllers in a high-fidelity simulation framework based on the real-world Abu Dhabi Autonomous Racing League competition, using a high-fidelity double-track vehicle dynamics simulator. Open-loop evaluation shows that the learning-based controllers achieve the lowest prediction errors; however, closed-loop testing reveals that this improved accuracy does not translate into superior path tracking performance or lap times, even after iterative fine-tuning. In contrast, the proposed EHD approach achieves the best overall closed-loop robustness and lap time, highlighting the necessity of evaluating feedforward strategies within the complete trajectory planning and control software stack. Our code is available at https://github.com/TUMRT/steering_ff_control.