1 citations · 1 across the 3 of their papers we have counts for
4 papers
SparseWAM: Driving World-Action Models are Worth 16 Register Tokens
Yixiang Yang, Lizhong Zhang, Xingyu Zhang +6
World Action Models (WAMs) transfer scene-dynamics priors to planning by jointly forecasting future scenes and ego trajectories. However, WAMs built on video generators must genera…
ForeDrive: Foresight-Guided End-to-End Autonomous Driving with a Planning-Relevant Latent World Model
Sinuo Wang, Zichong Gu, Yuhan Huang +10
Existing latent world models are typically optimized for future predictability, yet the resulting representations are not necessarily useful for planning in autonomous driving. Pre…
ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models
Qichao Zhang, Xing Fang, Jiaqi Fang +4
Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-based methods are interpretable,…
Prospective Role of Foundation Models in Advancing Autonomous Vehicles
Jianhua Wu, Bingzhao Gao, Jincheng Gao +8
With the development of artificial intelligence and breakthroughs in deep learning, large-scale Foundation Models (FMs), such as GPT, Sora, etc., have achieved remarkable results i…