拡散モデルによる自動運転のための安全でスタイル化された軌道計画
Safe and Stylized Trajectory Planning for Autonomous Driving via Diffusion Model
拡散モデルを用いて、安全制約と運転スタイルをリアルタイムに両立させる軌道計画フレームワークSDD Plannerを提案し、ベンチマークと実車試験で有効性を示した。
詳しい要約
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著者: Shuo Pei, Yong Wang, Yuanchen Zhu, Chen Sun, Qin Li, Yanan Zhao, Huachun Tan
分類: cs.RO
原文アブストラクト
Achieving safe and stylized trajectory planning in complex real-world scenarios remains a critical challenge for autonomous driving systems. This paper proposes the SDD Planner, a diffusion-based framework designed to effectively reconcile safety constraints with driving styles in real time. The framework integrates two core modules: a Multi-Source Style-Aware Encoder, which employs distance-sensitive attention to fuse dynamic agent data and environmental contexts for heterogeneous safety-style perception; and a Style-Guided Dynamic Trajectory Generator, which adaptively modulates priority weights within the diffusion denoising process to generate user-preferred yet safe trajectories. Extensive experiments demonstrate that SDD Planner achieves state-of-the-art performance. On the StyleDrive benchmark, it improves the SM-PDMS metric by 3.9% over WoTE, the strongest baseline. Furthermore, on the NuPlan Test14 and Test14-hard benchmarks, SDD Planner ranks first with overall scores of 91.76 and 80.32, respectively, outperforming leading methods such as PLUTO. Real-vehicle closed-loop tests further confirm that SDD Planner maintains high safety standards while aligning with preset driving styles, validating its practical applicability for real-world deployment.
