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

2026-05-20

AI Summary

This study addresses the challenge of improving path-tracking accuracy and lap time in autonomous racing by effectively predicting inverse lateral vehicle dynamics to minimize steering corrections from the feedback controller. The authors propose a novel empirical feedforward method—Empirical Handling Dynamics (EHD)—based on polynomial surface fitting, which captures speed-dependent nonlinear steering characteristics with minimal parametrization. Using a high-fidelity two-track vehicle dynamics simulator, they systematically evaluate two learning-based and two empirical feedforward controllers within a combined open-loop and closed-loop validation framework. Results show that although learning-based approaches achieve the lowest open-loop prediction error, the proposed EHD method delivers superior closed-loop robustness and faster lap times, underscoring the critical importance of evaluating feedforward strategies within the full control stack rather than in isolation.

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.

Problem

• feedforward steering control
• autonomous racing
• learning-based control
• empirical modeling
• trajectory tracking

Innovation

• feedforward steering control
• empirical modeling
• polynomial surface fit
• autonomous racing
• closed-loop evaluation