1 citations · 2 across the 13 of their papers we have counts for
13 papers
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…
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…
Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive
Yiru Wang, Anqing Jiang, Shuo Wang +4
Open-loop evaluation offers fast, reproducible assessment of autonomous driving planners, but its ability to predict real closed-loop driving performance remains questionable. Prio…
CL-SEC: Cross-Layer Semantic Error Correction Empowered by Language Models
Yirun Wang, Yuyang Du, Soung Chang Liew +3
Achieving reliable communication has long been a fundamental challenge in networked systems. Semantic Error Correction (SEC) leverages the semantic understanding capabilities of la…
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…
UniUncer: Unified Dynamic Static Uncertainty for End to End Driving
Yu Gao, Jijun Wang, Zongzheng Zhang +7
End-to-end (E2E) driving has become a cornerstone of both industry deployment and academic research, offering a single learnable pipeline that maps multi-sensor inputs to actions w…
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…
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…
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…
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…