9 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…
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment
Ziang Guo, Chen Min, Xuefeng Zhang +5
End-to-end (E2E) autonomous driving aims to directly map sensory observations to driving actions, but its real-world deployment is hindered by evolving data distributions and the h…
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…
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…
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…