Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization
This work provides a practical improvement for autonomous racing systems by accelerating trajectory optimization, which is crucial for real-time performance.
The paper addresses the challenge of slow convergence and suboptimal solutions in trajectory optimization for autonomous racing by proposing a learning-informed initialization strategy. By using a neural network trained on Formula 1 telemetry to predict expert-like raceline offsets, the authors significantly reduce solver runtime while maintaining optimal lap times across 17 tracks.
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.