7 citations · 16 across the 18 of their papers we have counts for
4 papers · 1 filter
DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
Ziying Song, Lin Liu, Hongyu Pan +7
Most end-to-end autonomous driving methods rely on imitation learning from single expert demonstrations, often leading to conservative and homogeneous behaviors that limit generali…
Collaborative Perception Datasets for Autonomous Driving: A Review
Naibang Wang, Deyong Shang, Yan Gong +6
Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving…
Fully Unified Motion Planning for End-to-End Autonomous Driving
Lin Liu, Caiyan Jia, Ziying Song +6
Current end-to-end autonomous driving methods typically learn only from expert planning data collected from a single ego vehicle, severely limiting the diversity of learnable drivi…
Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +7
End-to-end autonomous driving frameworks enable seamless integration of perception and planning but often rely on one-shot trajectory prediction, which may lead to unstable control…