12 papers
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…
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…
ETA-VLA: Efficient Token Adaptation via Temporal Fusion and Intra-LLM Sparsification for Vision-Language-Action Models
Yiru Wang, Anqing Jiang, Shuo Wang +3
The integration of Vision-Language-Action (VLA) models into autonomous driving systems offers a unified framework for interpreting complex scenes and executing control commands. Ho…
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…