most citedDiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving

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cs.CV2026

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…

cs.CV2026

Unified Map Prior Encoder for Mapping and Planning

Zongzheng Zhang, Sizhe Zou, Guantian Zheng +12

Online mapping and end-to-end (E2E) planning in autonomous driving remain largely sensor-centric, leaving rich map priors, including HD/SD vector maps, rasterized SD maps, and sate…

cs.CV2026

Evaluating Synthetic Images as Effective Substitutes for Experimental Data in Surface Roughness Classification

Binwei Chen, Huachao Leng, Chi Yeung Mang +5

Hard coatings play a critical role in industry, with ceramic materials offering outstanding hardness and thermal stability for applications that demand superior mechanical performa…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…