LADY: Linear Attention for Autonomous Driving Efficiency without Transformers

• URL: http://arxiv.org/abs/2512.15038v2
• Date: Thu, 18 Dec 2025 04:52:38 GMT
• Title: LADY: Linear Attention for Autonomous Driving Efficiency without Transformers
• Authors: Jihao Huang, Xi Xia, Zhiyuan Li, Tianle Liu, Jingke Wang, Junbo Chen, Tengju Ye,
• Abstract summary: LADY is the first fully linear attention-based generative model for end-to-end autonomous driving.<n>We introduce a lightweight linear cross-attention mechanism that enables effective cross-modal information exchange.<n>The model has been deployed and validated on edge devices, demonstrating its practicality in resource-limited scenarios.
• Score: 12.89500537893449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end paradigms have demonstrated great potential for autonomous driving. Additionally, most existing methods are built upon Transformer architectures. However, transformers incur a quadratic attention cost, limiting their ability to model long spatial and temporal sequences-particularly on resource-constrained edge platforms. As autonomous driving inherently demands efficient temporal modeling, this challenge severely limits their deployment and real-time performance. Recently, linear attention mechanisms have gained increasing attention due to their superior spatiotemporal complexity. However, existing linear attention architectures are limited to self-attention, lacking support for cross-modal and cross-temporal interactions-both crucial for autonomous driving. In this work, we propose LADY, the first fully linear attention-based generative model for end-to-end autonomous driving. LADY enables fusion of long-range temporal context at inference with constant computational and memory costs, regardless of the history length of camera and LiDAR features. Additionally, we introduce a lightweight linear cross-attention mechanism that enables effective cross-modal information exchange. Experiments on the NAVSIM and Bench2Drive benchmarks demonstrate that LADY achieves state-of-the-art performance with constant-time and memory complexity, offering improved planning performance and significantly reduced computational cost. Additionally, the model has been deployed and validated on edge devices, demonstrating its practicality in resource-limited scenarios.

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