Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization

• URL: http://arxiv.org/abs/2603.07126v1
• Date: Sat, 07 Mar 2026 09:33:38 GMT
• Title: Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization
• Abstract summary: Trajectory optimization is a central component of fast and efficient autonomous racing.<n>We propose a learning-informed strategy based on real-world Formula 1 telemetry.
• Score: 7.069718718698565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory optimization is a central component of fast and efficient autonomous racing. However practical optimization pipelines remain highly sensitive to initialization and may converge slowly or to suboptimal local solutions when seeded with heuristic trajectories such as the centerline or minimum-curvature paths. To address this limitation, we leverage expert driving behavior as a initialization prior and propose a learning-informed initialization strategy based on real-world Formula 1 telemetry. To this end, we first construct a multi-track Formula~1 trajectory dataset by reconstructing and aligning noisy GPS telemetry to a standardized reference-line representation across 17 tracks. Building on this, we present a neural network that predicts an expert-like raceline offset directly from local track geometry, without explicitly modeling vehicle dynamics or forces. The predicted raceline is then used as an informed seed for a minimum-time optimal control solver. Experiments on all 17 tracks demonstrate that the learned initialization accelerates solver convergence and significantly reduces runtime compared to traditional geometric baselines, while preserving the final optimized lap time.

Related papers

• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
  Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
  arXiv  Detail & Related papers  (2026-04-10T17:12:07Z)
• 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)
• DELTAv2: Accelerating Dense 3D Tracking [79.63990337419514]
  We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos.<n>We introduce a coarse-to-fine strategy that begins tracking with a small subset of points and progressively expands the set of tracked trajectories.<n>The newly added trajectories are using a learnable module, which is trained end-to-end alongside the tracking network.
  arXiv  Detail & Related papers  (2025-08-02T03:15:47Z)
• 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)
• Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series [0.0]
  This paper proposes a control technique for autonomous RC car racing. It operates only local path planning on the actual LiDAR point cloud. It is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented.
  arXiv  Detail & Related papers  (2024-08-27T15:50:31Z)
• Exploring Dynamic Transformer for Efficient Object Tracking [58.120191254379854]
  We propose DyTrack, a dynamic transformer framework for efficient tracking.<n>DyTrack automatically learns to configure proper reasoning routes for various inputs, gaining better utilization of the available computational budget.<n>Experiments on multiple benchmarks demonstrate that DyTrack achieves promising speed-precision trade-offs with only a single model.
  arXiv  Detail & Related papers  (2024-03-26T12:31:58Z)
• Large-Scale OD Matrix Estimation with A Deep Learning Method [70.78575952309023]
  The proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset.
  arXiv  Detail & Related papers  (2023-10-09T14:30:06Z)
• Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles [3.200238632208686]
  Trajectory planning aims to produce a globally optimal route for Autonomous Passenger Vehicles. Existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk.
  arXiv  Detail & Related papers  (2023-09-25T22:30:18Z)
• 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)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• 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)
• Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning [0.0]
  This paper describes a machine learning approach to generating an accurate prediction of the racing line in real-time on desktop processing hardware. The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver.
  arXiv  Detail & Related papers  (2021-02-03T22:34:22Z)

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.