DeepSight: 潜在状態予測による長期的世界モデリングを用いたエンドツーエンド自動運転
DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
自動運転のための世界モデルを提案し、BEV空間で将来フレームの潜在意味特徴を並列予測することで長期的な状態予測を行い、さらに適応的なテキスト推論で長尾シナリオの性能を向上させ、閉ループベンチマークでSOTAを達成した。
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: Lingjun Zhang, Changjie Wu, Linzhe Shi, Jiangyang Li, Jiaxin Liu, Lei Yang, Hang Zhang, Mu Xu, Hong Wang
分類: cs.CV, cs.RO
原文アブストラクト
End-to-end autonomous driving systems are increasingly integrating Vision-Language Model (VLM) architectures, incorporating text reasoning or visual reasoning to enhance the robustness and accuracy of driving decisions. However, the reasoning mechanisms employed in most methods are direct adaptations from general domains, lacking in-depth exploration tailored to autonomous driving scenarios, particularly within visual reasoning modules. In this paper, we propose a driving world model that performs parallel prediction of latent semantic features for consecutive future frames in the bird's-eye-view (BEV) space, thereby enabling long-horizon modeling of future world states. We also introduce an efficient and adaptive text reasoning mechanism that utilizes additional social knowledge and reasoning capabilities to further improve driving performance in challenging long-tail scenarios. We present a novel, efficient, and effective approach that achieves state-of-the-art (SOTA) results on the closed-loop Bench2drive benchmark. Codes are available at: https://github.com/hotdogcheesewhite/DeepSight.
