F1データ駆動初期化による自動運転レースの効率的軌道最適化
Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization
F1のGPSテレメトリから専門家の走行ラインを学習し、軌道最適化の初期解として用いることで収束を高速化する手法を提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Samir Shehadeh, Lukas Kutsch, Nils Dengler, Sicong Pan, Maren Bennewitz
分類: cs.RO
原文アブストラクト
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.
