7 papers
Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving
Hao Jiang, Chuan Hu, Yukang Shi +4
Vision-Language Models (VLMs) offer a promising approach to end-to-end autonomous driving due to their human-like reasoning capabilities. However, troublesome gaps remains between…
DiffVLA++: Bridging Cognitive Reasoning and End-to-End Driving through Metric-Guided Alignment
Yu Gao, Anqing Jiang, Yiru Wang +7
Conventional end-to-end (E2E) driving models are effective at generating physically plausible trajectories, but often fail to generalize to long-tail scenarios due to the lack of e…
AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
Jinhao Chai, Anqing Jiang, Hao Jiang +4
End-to-end multi-modal planning has become a transformative paradigm in autonomous driving, effectively addressing behavioral multi-modality and the generalization challenge in lon…
FlowDrive: Energy Flow Field for End-to-End Autonomous Driving
Hao Jiang, Zhipeng Zhang, Yu Gao +11
Recent advances in end-to-end autonomous driving leverage multi-view images to construct BEV representations for motion planning. In motion planning, autonomous vehicles need consi…
IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model
Anqing Jiang, Yu Gao, Yiru Wang +11
Vision-Language-Action (VLA) models have demonstrated potential in autonomous driving. However, two critical challenges hinder their development: (1) Existing VLA architectures are…
DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion
Zhigang Sun, Yiru Wang, Anqing Jiang +13
Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-b…