6 papers
ChainFlow-VLA: Causal Flow Planning with Vision-Language Models
Xiyang Wang, Xinlin Wang, Tingguang Zhou +7
Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consistency. Autoregressive (AR) model…
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…
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…
From Human Intention to Action Prediction: Intention-Driven End-to-End Autonomous Driving
Huan Zheng, Yucheng Zhou, Tianyi Yan +9
While end-to-end autonomous driving has achieved remarkable progress in geometric control, current systems remain constrained by a command-following paradigm that relies on simple…
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…
Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinement
Jianbo Zhao, Taiyu Ban, Xiangjie Li +5
The inherent sequential modeling capabilities of autoregressive models make them a formidable baseline for end-to-end planning in autonomous driving. Nevertheless, their performanc…