6 citations · 7 across the 5 of their papers we have counts for
5 papers
Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving
Zhaoxin Fan, Tianbao Zhang, Wenjun Wu +5
World models offer a promising paradigm for autonomous driving by predicting how traffic scenes may evolve and using such predictions to support action generation. However, existin…
Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection
Yifan Chang, Junjie Huang, Xiaofeng Wang +5
Monocular 3D lane detection is a fundamental task in autonomous driving. Although sparse-point methods lower computational load and maintain high accuracy in complex lane geometrie…
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation
Guosheng Zhao, Chaojun Ni, Xiaofeng Wang +9
Closed-loop simulation is essential for advancing end-to-end autonomous driving systems. Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on condit…
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Xiaofeng Wang, Zheng Zhu, Wenbo Xu +7
Semantic occupancy perception is essential for autonomous driving, as automated vehicles require a fine-grained perception of the 3D urban structures. However, existing relevant be…
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark
Xiaofeng Wang, Zheng Zhu, Yunpeng Zhang +5
In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth esti…