7 citations · 16 across the 18 of their papers we have counts for
10 papers · 1 filter
UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models
Lei Yang, Yuner Zhang, Ziyang Liu +5
Recent advances in multimodal foundation models, exemplified by GPT-6 Astra, raise the prospect of general-purpose embodied reasoning and planning. Using Internet videos for embodi…
InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting
Songkai Wang, Xiaokai Bai, Lei Yang +1
Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system. Roadside scenes are globally structured yet locally evolving, requ…
MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving
Ziying Song, Shengkai Zhang, Lei Yang +6
Long-horizon planning enables autonomous vehicles to anticipate scene evolution and potential risks, supporting safe and stable decisions in complex interactions. However, existing…
Towards robust multimodal 3D object detection via visual foundation models
Ziying Song, Lin Liu, Hongyu Pan +4
Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and camera sensors. However, exi…
PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout
Haozhuang Chi, Jingsong Liang, Ziying Song +4
Local pedestrian-vehicle forecasting spans heterogeneous physical scales: pedestrians combine root locomotion with articulated motion, whereas vehicles are rigid bodies described b…
InfraOcc: An Infrastructure Occupancy Benchmark with Static-to-Dynamic Reasoning
Lei Yang, Xiaokai Bai, Boqi Li +8
Fixed-viewpoint infrastructure sensors repeatedly observe the same traffic space, making roadside 3D occupancy structurally different from ego-vehicle perception: a near-persistent…
MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving
Ziying Song, Shengkai Zhang, Lin Liu +8
Long-horizon planning is critical for safe autonomous driving in complex scenarios. Existing methods improve planning continuity with temporal memory, but such memory may become in…
Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving
Yuchen Liu, Ziying Song, Shengkai Zhang +6
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-l…
GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +3
Feature misalignment in BEV perception is a critical yet often overlooked challenge in autonomous driving, especially under calibration uncertainties between LiDAR and camera senso…
GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +8
End-to-end autonomous driving has made significant progress by unifying perception, prediction, and planning within a single learning framework, achieving strong performance in sho…