DiffE2E: 拡散と教師あり方策を統合したハイブリッド行動拡散によるエンドツーエンド自動運転の再考
DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy
拡散モデルと教師あり方策を組み合わせたハイブリッドデコーダを提案し、マルチセンサー知覚特徴の階層的双方向クロスアテンション統合により、CARLAとNAVSIMで最先端性能を達成したエンドツーエンド自動運転フレームワーク。
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著者: Rui Zhao, Yuze Fan, Ziguo Chen, Fei Gao, Zhenhai Gao
分類: cs.RO
原文アブストラクト
End-to-end learning has emerged as a transformative paradigm in autonomous driving. However, the inherently multimodal nature of driving behaviors and the generalization challenges in long-tail scenarios remain critical obstacles to robust deployment. We propose DiffE2E, a diffusion-based end-to-end autonomous driving framework. This framework first performs multi-scale alignment of multi-sensor perception features through a hierarchical bidirectional cross-attention mechanism. It then introduces a novel class of hybrid diffusion-supervision decoders based on the Transformer architecture, and adopts a collaborative training paradigm that seamlessly integrates the strengths of both diffusion and supervised policy. DiffE2E models structured latent spaces, where diffusion captures the distribution of future trajectories and supervision enhances controllability and robustness. A global condition integration module enables deep fusion of perception features with high-level targets, significantly improving the quality of trajectory generation. Subsequently, a cross-attention mechanism facilitates efficient interaction between integrated features and hybrid latent variables, promoting the joint optimization of diffusion and supervision objectives for structured output generation, ultimately leading to more robust control. Experiments demonstrate that DiffE2E achieves state-of-the-art performance in both CARLA closed-loop evaluations and NAVSIM benchmarks. The proposed integrated diffusion-supervision policy offers a generalizable paradigm for hybrid action representation, with strong potential for extension to broader domains including embodied intelligence. More details and visualizations are available at \href{https://infinidrive.github.io/DiffE2E/}{project website}.
関連論文
- 拡散モデルによるエンドツーエンド自動運転の可能性を解き放つ自動運転/拡散モデル
