4D-WAM is presented, a model that leverages geometric foundation models for training-time supervision to enable 4D consistent world modeling and effectively models 4D consistent scene evolution and achieves state-of-the-art performance on challenging NAVSIM-v1 and NAVSIM-v2 benchmarks.
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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