Author

Lei Yang

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Preprint Oct 2026

InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting

Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system. Roadside scenes are globally structured yet locally evolving, requiring comprehensive scene-level context while precisely modeling sparse localized motion. However, existing forecasting paradigms struggle t...

Song Wang, Xiao-Kai Bai, Lei Yang et al. · 0 citations

Preprint Sep 2026

MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving

Long-horizon planning enables autonomous vehicles to anticipate scene evolution and potential risks, supporting safe and stable decisions in complex interactions. However, existing methods struggle to propagate motion trends from observed history into the future. Long rollouts based on a single latent state may further...

Zi-Ying Song, Sheng-Kai Zhang, Lei Yang et al. · 0 citations

Preprint Sep 2026

RoadOcc Learns When to Persist, Transport, or Refresh Memory for Roadside Occupancy Prediction

Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal memory can recover weak observations, but reusing moving evidence at stale locations can corrupt occupancy predictions. Motion compensation addresses displacement, while reliance on the resulting history remains a separ...

Xiao-Kai Bai, Lei Yang, Song Wang et al. · 0 citations

Preprint Sep 2026

Towards robust multimodal 3D object detection via visual foundation models

This work introduces SAM-AD, a domain-specific pretraining strategy that fine-tunes SAM on autonomous-driving imagery to extract feature representations with rich semantic information, and develops the Depth-Guided Wavelet Attention (DGWA) module, which suppresses high-frequency sensor noise while preserving critical c...

Zi-Ying Song, Lin Liu, Hong-Yu Pan et al. · 0 citations

Preprint Aug 2026

Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving

Reliable long-horizon planning remains a key challenge in end-to-end autonomous driving. By accounting for future motion evolution and potential consequences, it provides forward-looking guidance for safe and consistent driving in evolving traffic environments. Existing methods use historical planning states as tempora...

Yu-Chen Liu, Zi-Ying Song, Sheng-Kai Zhang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout

PV-WM, a history-only world model over structured post-perception tracks, recurrently advances pedestrian root motion, 15-joint articulation, and learned vehicle states within a synchronized heterogeneous state while preserving type-specific pedestrian and vehicle dynamics.

Hao-Zhuang Chi, Jing-Song Liang, Zi-Ying Song et al. · 2 citations

Preprint Aug 2026

MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving

MomADv2, a reliable state-space memory framework for long-horizon end-to-end autonomous driving, introduces a Selective State-Space Planning Memory Query Module, which filters historical planning queries based on temporal continuity and command consistency, and models the evolution of planning intentions through a sele...

Zi-Ying Song, Sheng-Kai Zhang, Lin Liu et al. · 0 citations