12 papers
Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation
Xingtai Gui, Meijie Zhang, Tianyi Yan +5
End-to-end autonomous driving aims to generate safe and plausible planning policies from raw sensor input. Driving world models have shown great potential in learning rich represen…
TrajDiff: End-to-end Autonomous Driving without Perception Annotation
Xingtai Gui, Jianbo Zhao, Wencheng Han +5
End-to-end autonomous driving systems directly generate driving policies from raw sensor inputs. While these systems can extract effective environmental features for planning, rely…
HiCoGen: Hierarchical Compositional Text-to-Image Generation in Diffusion Models via Reinforcement Learning
Hongji Yang, Yucheng Zhou, Wencheng Han +4
Recent advances in diffusion models have demonstrated impressive capability in generating high-quality images for simple prompts. However, when confronted with complex prompts invo…
AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
Tianyi Yan, Tao Tang, Xingtai Gui +11
End-to-end models for autonomous driving hold the promise of learning complex behaviors directly from sensor data, but face critical challenges in safety and handling long-tail eve…
RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation
Tianyi Yan, Wencheng Han, Xia Zhou +4
Synthetic data is crucial for advancing autonomous driving (AD) systems, yet current state-of-the-art video generation models, despite their visual realism, suffer from subtle geom…
Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data
Wencheng Han, Dongqian Guo, Xiao Chen +3
Metal artifacts in CT slices have long posed challenges in medical diagnostics. These artifacts degrade image quality, resulting in suboptimal visualization and complicating the ac…