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

TL;DR

A neural network is presented that predicts an expert-like raceline offset directly from local track geometry, without explicitly modeling vehicle dynamics or forces, and is used as an informed seed for a minimum-time optimal control solver.

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.

Figures (5)

• Figure 1: Overview of our proposed learning-informed initialization method. First, expert Formula 1 telemetry (left) is used to train a geometric raceline predictor that captures structural properties of near-optimal racing behavior. The predicted raceline then serves as an initialization for a minimum-time trajectory optimization solver. Finally, the resulting optimized trajectory is transferred to a RoboRacer charles2025advancing platform (right), demonstrating robustness to domain shift from full-scale Formula 1 data to a 1:10 autonomous racing system operating on an initially unknown map.
• Figure 2: The racing track is described by a centerline parameterized by the arc length, with track boundaries and racelines expressed as lateral offsets relative to this reference.
• Figure 3: Network architecture for raceline prediction. History and future windows are encoded using dilated temporal convolutional networks and fused with the target window via convolutional and multi-head temporal attention layers. The fused features are flattened and processed by an MLP to predict a raceline, which is subsequently used as initialization seed for a minimum-time optimizer.
• Figure 4: Neural network training behavior and qualitative raceline prediction results. (a) Training and validation loss curves show stable convergence and similar trends, indicating good generalization without overfitting. (b–d) Predicted racelines (green) compared to the reconstructed expert Formula 1 raceline (blue) and the track centerline (black dashed) on representative circuits (Spielberg, Monza, and COTA). The predicted trajectories closely follow the expert racing line, particularly in corner entry and exit regions, while clearly deviating from the geometric centerline, demonstrating that the network captures expert driving structure from track geometry alone. On COTA the deviation is slightly higher, due to the long straights before the curve where acceleration profiles are difficult to predict.
• Figure 5: Qualitative example of the optimized raceline on the RoboRacer test track. The F1-NN-initialized trajectory (red) follows smooth cornering arcs resulting in high speed and trackability.