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

TL;DR

This paper introduces a data-driven initialization method for trajectory optimization in autonomous racing, using a neural network trained on Formula-1 telemetry to improve convergence speed and efficiency.

Contribution

It presents a novel learning-based initialization strategy leveraging real-world F1 telemetry data to enhance trajectory optimization in autonomous racing.

Findings

Accelerates solver convergence across 17 tracks

Reduces runtime significantly compared to geometric baselines

Maintains optimal lap times with improved initialization

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.