1 citations · 1 across the 12 of their papers we have counts for
12 papers
ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation
Boni Hu, Xiong Wei, Haoming Huang +20
Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three…
Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving
Zongzheng Zhang, Jijun Wang, Saining Zhang +8
Traffic elements such as traffic lights and road signs play a fundamental role in human driving decisions and should naturally influence end-to-end driving performance. However, ex…
HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Driving
Yiru Wang, Zichong Gu, Yu Gao +5
Vision-Language-Action (VLA) models offer promising capabilities for autonomous driving through multimodal understanding. However, their utilization in safety-critical scenarios is…
GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models
Hao Sun, Lei Fan, Donglin Di +1
Articulated object generation has seen increasing advancements, yet existing models often lack the ability to be conditioned on text prompts. To address the significant gap between…
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…
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
Anqing Jiang, Yu Gao, Zhigang Sun +11
Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which ena…
Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models
Haohan Chi, Huan-ang Gao, Ziming Liu +12
Vision-Language-Action (VLA) models for autonomous driving show promise but falter in unstructured corner case scenarios, largely due to a scarcity of targeted benchmarks. To addre…
SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction
Anqing Jiang, Jinhao Chai, Yu Gao +10
Recent advancements in high-definition \emph{HD} map construction have demonstrated the effectiveness of dense representations, which heavily rely on computationally intensive bird…
Open-World Drone Active Tracking with Goal-Centered Rewards
Haowei Sun, Jinwu Hu, Zhirui Zhang +7
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations, providing a more practical solution for effe…