World-action models (WAMs) jointly model future traffic states and driving actions for end-to-end autonomous driving in intelligent transportation systems. However, their action learning remains dominated by expert-trajectory imitation, leaving unsafe alternatives close to expert trajectories insufficiently constrained...
Zi-Hao Zhang, Xin-Yu Zhang, Bing Zeng et al.· 0 citations
ViRA is introduced, a planner-agnostic visual representation alignment framework that keeps the planner architecture and inference cost unchanged and develops ViRA-Diffusion, a diffusion-based planner trained without auxiliary perception supervision, which achieves 92.3 EPDMS on NAVSIM v2 navtest.
Zi-Hao Zhang, Hao-Chen Tian, Tianyu Li et al.· 1 citation
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
This work studies a lightweight coding harness whose execution loop is fixed while three components are varied: planning, action space, and context management, and finds that context management becomes increasingly valuable as the context-window budget tightens.
Run-Ze Fan, Zihao Zhang, Si-Min Ma et al.· 14 citations· ⚡2
Building effective AI systems increasingly depends on writing high-quality task requirements, yet users often struggle to articulate the constraints, preferences, and edge cases that determine success. This problem is especially acute in AI development, where behavior is shaped not only by human expectations but also b...
Pengshan Cai, Zi-Hao Zhang, Ting Jin et al.· 0 citations
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