Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910240594722816 |
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| author | Jank, Georg Piccinini, Mattia Wenk, Sebastian Pitschi, Phillip Betz, Johannes Lohmann, Boris |
| author_facet | Jank, Georg Piccinini, Mattia Wenk, Sebastian Pitschi, Phillip Betz, Johannes Lohmann, Boris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21111 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing Jank, Georg Piccinini, Mattia Wenk, Sebastian Pitschi, Phillip Betz, Johannes Lohmann, Boris Robotics Systems and Control 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. |
| title | Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2605.21111 |