Sparse Planner: 条件付き変分オートエンコーダによる効率的サンプリングを実現するハイブリッドプランナ
Sparse Planner: A Hybrid Planner for Efficient Sampling via a Conditional Variational Autoencoder
条件付き変分オートエンコーダ（CVAE）を用いてシーン文脈と有効な軌道パラメータの関係を学習し、サンプリング密度を大幅に削減しつつ高品質な軌道計画を可能にするSparse Plannerを提案した。
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著者: Wenguang Xu, Giovanni Lucente, Karem Mohamed, Richard Membarth
分類: cs.RO
原文アブストラクト
Trajectory planning is a core component of autonomous driving systems, where real-time performance and solution quality directly affect safety and reliability. Sample-Based Motion Planning (SBMP) is widely adopted for its ability to approximate near-optimal solutions through parameter space sampling. However, achieving high-quality trajectories typically requires dense sampling, leading to substantial computational overhead and significant runtime variability in complex traffic scenarios. To address this limitation, we propose a Sparse Planner (SP) that improves sampling efficiency by learning the conditional relationship between scene context and effective trajectory parameters using a Conditional Variational Autoencoder (CVAE). By modeling the structure of high-quality sampling distributions, SP directly generates cost-effective samples in the parameter space, significantly reducing the required sampling density while preserving solution quality. Experimental results show that SP achieves lower trajectory cost than the state-of-the-art FISS+ planner while using only one-eighth of the sampling density. In addition, SP demonstrates improved distance-keeping capability in obstacle-rich scenarios and maintains reduced and more stable runtime characteristics, indicating enhanced computational efficiency and predictable runtime behavior.
