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

• URL: http://arxiv.org/abs/2605.21111v1
• Date: Wed, 20 May 2026 12:44:36 GMT
• Title: Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
• Abstract summary: Feedforward steering control is a key component of hierarchical control architectures for autonomous racing.<n>This paper presents a benchmark of two learning-based and two empirical feedforward controllers.<n>We test the feedforward controllers in a high-fidelity simulation framework based on the real-world Abu Dhabi Autonomous Racing League competition.
• Score: 2.183305325115666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning [57.68768189557651]
  In Formula 1, drivers optimize racing lines within tire grip limits to minimize lap times.<n>In rally racing, drivers intentionally break traction to drift on loose surfaces.<n>This paper proposes a planning-control framework specifically designed for MLT drifting scenario.
  arXiv  Detail & Related papers  (2026-07-31T08:50:40Z)
• When is Your LLM Steerable? [56.656180566692946]
  Activation steering offers a lightweight approach to control language models' behavior at inference time.<n>Finding the regime and boundaries of successful steering typically requires expensive grid searches.<n>We investigate whether steerability can be predicted from the model's internal states at the beginning of the generation process.
  arXiv  Detail & Related papers  (2026-06-10T02:55:34Z)
• Simple but Stable, Fast and Safe: Achieve End-to-end Control by High-Fidelity Differentiable Simulation [14.322775509774246]
  Obstacle avoidance is a fundamental vision-based task essential for enabling quadrotors to perform advanced applications.<n>We propose a novel end-to-end policy that directly maps depth images to low-level bodyrate commands by reinforcement learning.<n>Results demonstrate that the proposed approach achieves the highest success rate and the lowest jerk among state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-04-12T09:38:00Z)
• Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO [0.0]
  Pure Pursuit (PP) is widely used in autonomous racing for real-time path tracking.<n>We propose a reinforcement-learning (RL) approach that jointly chooses the lookahead Ld and a steering gain g online.<n>Across simulation and real-car tests, the proposed RL-PP controller consistently outperforms fixed-lookahead PP, velocity-scheduled adaptive PP, and an RL lookahead-only variant.
  arXiv  Detail & Related papers  (2026-02-20T17:48:21Z)
• Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing [74.83272587893508]
  We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction.
  arXiv  Detail & Related papers  (2026-02-17T15:10:44Z)
• Path Tracking with Dynamic Control Point Blending for Autonomous Vehicles: An Experimental Study [40.36217526625217]
  This paper presents a path-tracking framework for autonomous vehicles in which the lateral control command is applied to a dynamic control point along the wheelbase.<n>Instead of enforcing a fixed reference at either the front or rear axle, the proposed method continuously interpolates between both.<n>The results in closed-loop tracking and backward maneuvers show improved trajectory accuracy, smoother steering profiles, and increased adaptability compared to fixed control-point baselines.
  arXiv  Detail & Related papers  (2026-02-02T10:03:37Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [57.278726604424556]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers.<n>Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy.<n>We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Modelling, Positioning, and Deep Reinforcement Learning Path Tracking Control of Scaled Robotic Vehicles: Design and Experimental Validation [3.807917169053206]
  Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF) and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm.
  arXiv  Detail & Related papers  (2024-01-10T14:40:53Z)
• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential [71.53284767149685]
  We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
  arXiv  Detail & Related papers  (2023-12-22T16:29:55Z)
• Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing [0.0]
  This paper addresses the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars. We propose a partial end-to-end algorithm that decouples the planning and control tasks. By leveraging the robustness of a classical controller, our partial end-to-end driving algorithm exhibits better robustness towards model mismatches than standard end-to-end algorithms.
  arXiv  Detail & Related papers  (2023-12-11T14:27:10Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning [0.0]
  This paper proposes an adaptable path tracking control system based on Reinforcement Learning (RL) for autonomous cars. The tuning of the tracker uses an educated Q-Learning algorithm to minimize the lateral and steering trajectory errors.
  arXiv  Detail & Related papers  (2023-01-09T14:17:12Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.