2 citations · 2 across the 4 of their papers we have counts for
5 papers
Long-Horizon Consistent and Interaction-Aware World Models for Multi-Style End-to-End Driving
Yuxuan Han, Kunyuan Wu, Liyunong Yang +4
End-to-end autonomous driving has increasingly adopted world model-based reinforcement learning frameworks to improve learning efficiency through \textit{imagined rollouts}. Howeve…
LiDAR Teach, Radar Repeat: Robust Cross-Modal Navigation in Degenerate and Varying Environments
Renxiang Xiao, Yichen Chen, Yuanfan Zhang +5
Long-term autonomy requires robust navigation in environments subject to dynamic and static changes, as well as adverse weather conditions. Teach-and-Repeat (T\&R) navigation offer…
AppleVLM: End-to-end Autonomous Driving with Advanced Perception and Planning-Enhanced Vision-Language Models
Yuxuan Han, Kunyuan Wu, Qianyi Shao +6
End-to-end autonomous driving has emerged as a promising paradigm integrating perception, decision-making, and control within a unified learning framework. Recently, Vision-Languag…
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments
Xiuzhong Hu, Guangming Xiong, Zheng Zang +3
Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit…
PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments
Xiuzhong Hu, Guangming Xiong, Zheng Zang +3
Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost. Although the recently developed neural radiance fields (NeRF…