发表机构
Westlake Institute for Optoelectronics; CenBRAIN Neurotech, School of Engineering, Westlake University（西湖光电研究院; 西湖大学工程学院CenBRAIN神经科技中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SDPAD是首个全脉冲驱动的端到端自动驾驶规划流水线，通过量化转换、脉冲3D提升与Spike-QFormer，在精度接近ANN规划器的同时，能耗仅为其2%，性能优于现有SNN规划器。
AI 中文摘要
端到端自动驾驶需要轨迹规划器兼具高精度与边缘部署所需的低计算成本。最先进的人工神经网络（ANN）规划器虽满足精度要求，但需以繁重的密集计算为代价；而脉冲神经网络（SNN）虽有望通过稀疏、事件驱动的算术实现数个数量级的能效提升，但其规划精度仍远落后于ANN。本文提出SDPAD，一种全脉冲驱动的端到端规划流水线，旨在缩小这一差距。SDPAD通过量化ANN2SNN转换将预训练的ANN感知栈转换为整数脉冲形式，利用脉冲驱动最大值（SDM）深度分布（Spike-3D-Lift）将多视角图像提升至鸟瞰（BEV）空间，并通过Spike-QFormer进行规划——这是一种脉冲查询Transformer，其中从BEV场景中提取的自车、智能体和地图查询，通过可学习的路径点查询经交叉注意力融合，再经可变形脉冲交叉注意力细化。所有操作均由整数脉冲门控，推理为单次前馈传播，无需时间模拟循环。在nuScenes开环基准测试中，SDPAD的平均L₂误差为0.40米，碰撞率为0.12%，与强大的ANN规划器相当，而能耗为69.9毫焦，不到近期ANN基线的2%。在NAVSIM导航测试拆分的闭环评估中，SDPAD达到86.3 PDMS，较此前的SNN规划器SAD提升4.3分，且能效仅为主流ANN规划器的一小部分。据所知，SDPAD是首个在端到端自动驾驶中评估的全脉冲驱动规划器，证明SNN可在复杂驾驶任务中与密集ANN相媲美。
英文摘要
End-to-end autonomous driving demands trajectory planners that are both highly accurate and cheap enough for edge deployment. State-of-the-art artificial neural network (ANN) planners meet the accuracy requirement at the cost of heavy dense computation, while spiking neural networks (SNNs)---though promising orders-of-magnitude energy savings through sparse, event-driven arithmetic---still lag far behind in planning accuracy. We present \textbf{SDPAD}, a fully spike-driven end-to-end planning pipeline that closes this gap. SDPAD converts a pre-trained ANN perception stack into integer-spike form via quantized ANN2SNN conversion, lifts multi-view images into the bird's-eye-view (BEV) space with a spike-driven-max (SDM) depth distribution (Spike-3D-Lift), and plans through the Spike-QFormer, a spiking query transformer in which ego, agent, and map queries distilled from the BEV scene are fused by learnable waypoint queries via cross-attention, followed by deformable spike-cross-attention refinement. Every operation is gated by integer spikes and inference is a single feed-forward pass without temporal simulation loops. On the nuScenes open-loop benchmark, SDPAD achieves an average $L_2$ error of 0.40\,m and a collision rate of 0.12\%, on par with strong ANN planners while consuming 69.9\,mJ---less than 2\% of recent ANN baselines. In closed-loop evaluation on the NAVSIM navtest split, SDPAD reaches 86.3 PDMS, surpassing the previous SNN planner SAD by 4.3 points and matching mainstream ANN planners at a fraction of their energy. To our knowledge, SDPAD is the first fully spike-driven planner evaluated in end-to-end autonomous driving, demonstrating that SNNs can rival dense ANNs in complex driving tasks.
