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Author

Haochen Tian

2 papers indexed here

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Preprint Oct 2026

SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models

Driving world action models (WAMs) learn from paired trajectories and future observations. Post-training with reinforcement learning allows the policy to explore alternative trajectories and improve planning using driving rewards. However, driving logs contain future videos only for the recorded trajectories, leaving t...

Chang-Hui Jing, Hao-Chen Tian, Hong-Chen Li et al. · 0 citations
Preprint Sep 2026

DriveZero: End-to-End Driving Beyond Human Demonstrations

DriveZero is presented, an end-to-end system that learns driving behavior beyond human demonstrations that achieves state-of-the-art performance on NAVSIMv1, NAVSIMv2 and the closed-loop HUGSIM benchmark without any human trajectory supervision.

Hao He, Cheng-Cheng Hu, Zi-Run Su et al. · 2 citations

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