Track-centric Iterative Learning for Global Trajectory Optimization in Autonomous Racing

• URL: http://arxiv.org/abs/2601.21027v1
• Date: Wed, 28 Jan 2026 20:41:23 GMT
• Title: Track-centric Iterative Learning for Global Trajectory Optimization in Autonomous Racing
• Abstract summary: This paper presents a global trajectory optimization framework for minimizing lap time in autonomous racing under uncertain vehicle dynamics.<n>We propose a track-centric approach that directly learns and optimize the full-horizon trajectory.<n>The effectiveness of the proposed framework is validated through simulations and real-world experiments, demonstrating lap time improvement of up to 20.7% over a nominal baseline.
• Score: 9.502981420673102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a global trajectory optimization framework for minimizing lap time in autonomous racing under uncertain vehicle dynamics. Optimizing the trajectory over the full racing horizon is computationally expensive, and tracking such a trajectory in the real world hardly assures global optimality due to uncertain dynamics. Yet, existing work mostly focuses on dynamics learning at the tracking level, without updating the trajectory itself to account for the learned dynamics. To address these challenges, we propose a track-centric approach that directly learns and optimizes the full-horizon trajectory. We first represent trajectories through a track-agnostic parametric space in light of the wavelet transform. This space is then efficiently explored using Bayesian optimization, where the lap time of each candidate is evaluated by running simulations with the learned dynamics. This optimization is embedded in an iterative learning framework, where the optimized trajectory is deployed to collect real-world data for updating the dynamics, progressively refining the trajectory over the iterations. The effectiveness of the proposed framework is validated through simulations and real-world experiments, demonstrating lap time improvement of up to 20.7% over a nominal baseline and consistently outperforming state-of-the-art methods.

Related papers

• BFMTrack: Latent Sequence Optimization for Physics-Based Motion Tracking with Behavioral Foundation Models [7.959692704349906]
  Behavioral Foundation Models (BFMs) organize a rich repertoire of physically plausible behaviors into a latent space, guided by a large-scale motion dataset.<n>These models excel at time-invariant tasks, such as goal-reaching and state-based reward optimization, but their latent space does not directly support time-varying objectives, such as tracking a motion sequence.<n>We propose a novel Latent Sequence Optimization (LSO) to address these shortcomings.
  arXiv  Detail & Related papers  (2026-06-23T18:12:22Z)
• Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions [1.3299507495084417]
  This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) handover missions.<n>We propose a neural surrogate planner utilizing decoupled encoder-decoder long short-term memory (LSTM) networks to generate coordinated handover trajectory predictions.<n> Benchmark evaluations demonstrate that the learning-augmented planning framework achieves more than a threefold speedup and 100% optimization success rate.
  arXiv  Detail & Related papers  (2026-05-19T09:07:59Z)
• HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving [51.268878540511054]
  We propose HAD, an end-to-end planning framework with a Hierarchical Diffusion Policy.<n>We show that HAD achieves new state-of-the-art performance on both NAVSIM and HUGSIM.
  arXiv  Detail & Related papers  (2026-04-04T04:12:47Z)
• 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)
• Fast Near Time-Optimal Motion Planning for Holonomic Vehicles in Structured Environments [8.195916347194315]
  The approach aims to solve the problem of motion planning for planar motion systems using magnetic levitation.<n>The presented approach encodes the environment representation using free-space corridors and represents the motion of the vehicle through such a corridor using a motion primitive.<n>The method achieves significantly lower times compared to the state-of-the-art and is validated on a real-world Beckhoff XPlanar system.
  arXiv  Detail & Related papers  (2026-02-02T21:26:30Z)
• Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning [10.551597725130675]
  Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems.<n>This paper presents a self-supervised residual learning and trajectory optimization framework to address the aforementioned challenges.
  arXiv  Detail & Related papers  (2026-01-06T06:01:58Z)
• Track-On2: Enhancing Online Point Tracking with Memory [57.820749134569574]
  We extend our prior model Track-On into Track-On2, a simple and efficient transformer-based model for online long-term tracking.<n>Track-On2 improves both performance and efficiency through architectural refinements, more effective use of memory, and improved synthetic training strategies.
  arXiv  Detail & Related papers  (2025-09-23T15:00:18Z)
• Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer [9.812530969395906]
  In real-world applications, GPS trajectories often suffer from low sampling rates, with large and irregular intervals between consecutive points.<n>This paper addresses the task of map-constrained trajectory recovery, aiming to enhance trajectory sampling rates.
  arXiv  Detail & Related papers  (2025-05-20T03:09:17Z)
• Understanding Optimization in Deep Learning with Central Flows [95.5647720254338]
  We develop theory that can describe the dynamics of optimization in a complex regime.<n>Our results suggest that central flows can be a valuable theoretical tool for reasoning about optimization in deep learning.
  arXiv  Detail & Related papers  (2024-10-31T17:58:13Z)
• 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)
• Learning to Initialize Trajectory Optimization for Vision-Based Autonomous Flight in Unknown Environments [4.0543433786183485]
  We present a Neural-Enhanced Tray Planner (NEO-Planner) for autonomous flight in unknown environments.<n>NEO-Planner learns to predict spatial and temporal parameters for trajectories directly from raw sensor observations.<n>It reduces optimization by 20%, leading to a 26% decrease in trajectory time compared with pure optimization-based methods.
  arXiv  Detail & Related papers  (2023-09-19T15:07:26Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Autonomous Drone Racing with Deep Reinforcement Learning [39.757652701917166]
  In many robotic tasks, such as drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. In this work, a new approach to minimum-time trajectory generation for quadrotors is presented.
  arXiv  Detail & Related papers  (2021-03-15T18:05:49Z)

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.