2026
LADY: Linear Attention for Autonomous Driving Efficiency Without Transformers
RA-L 2026
End-to-end autonomous driving has emerged as a promising paradigm. However, state-of-the-art methods rely heavily on Transformer architectures. The inherent quadratic complexity of Transformers restricts their ability to model long-range spatial and temporal dependencies, particularly on resource-co