1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…