7 citations · 12 across the 12 of their papers we have counts for
5 papers · 1 filter
DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers
Yuntao Chen, Yuqi Wang, Zhaoxiang Zhang
World model-based searching and planning are widely recognized as a promising path toward human-level physical intelligence. However, current driving world models primarily rely on…
FreeVS: Generative View Synthesis on Free Driving Trajectory
Qitai Wang, Lue Fan, Yuqi Wang +2
Existing reconstruction-based novel view synthesis methods for driving scenes focus on synthesizing camera views along the recorded trajectory of the ego vehicle. Their image rende…
DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model
Yuqi Wang, Ke Cheng, Jiawei He +5
Driving world models have gained increasing attention due to their ability to model complex physical dynamics. However, their superb modeling capability is yet to be fully unleashe…
Monocular Occupancy Prediction for Scalable Indoor Scenes
Hongxiao Yu, Yuqi Wang, Yuntao Chen +1
Camera-based 3D occupancy prediction has recently garnered increasing attention in outdoor driving scenes. However, research in indoor scenes remains relatively unexplored. The cor…
Enhancing End-to-End Autonomous Driving with Latent World Model
Yingyan Li, Lue Fan, Jiawei He +4
In autonomous driving, end-to-end planners directly utilize raw sensor data, enabling them to extract richer scene features and reduce information loss compared to traditional plan…