Emerging World-Action Models (WAMs) have demonstrated promising performance in autonomous driving by jointly modeling future driving scene evolution and trajectory planning. However, existing WAMs are typically trained with video data, which is only 2D projections of the underlying 4D driving scene. Consequently, WAMs...
Jiacheng Fu, Yibo Yuan, Meng Tian et al.· 0 citations
This work introduces SUV, a unified end-to-end driving framework that casts future Scene Understanding as Video generation using a pretrained video foundation model, and shows that structured future supervision and direct future-stream access yield higher trajectory planning scores.
Yibo Yuan, Jiacheng Fu, Jiangtong Zhu et al.· 1 citation
Deep reinforcement learning (DRL) has achieved great success in many simulated and real-world robotic tasks. However, the difficulty of designing efficient and dense reward functions makes applying DRL to tackle complex long-horizon and open-world tasks a great challenge. Generative adversarial imitation learning (GAIL...
Keyvan Zhang, Zheng Fang, Enqi Zhao et al.· IEEE Transactions on robotic...· 0 citations
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