SDPAD: A Fully Spike-Driven Pipeline for End-to-End Autonomous Driving
Overview
SDPAD, a spike-driven end-to-end planner, outperforms prior SNN planners and matches ANN planners in energy efficiency, achieving 86.3 PDMS on NAVSIM navtest split. It converts ANN perception stacks into spike form and uses Spike-3D-Lift and Spike-QFormer for planning. SNNs rival dense ANNs in complex driving tasks, per the paper's claims. arXiv:2610.11583v1 Latest: SAD, a prior SNN planner, was surpassed by SDPAD by 4.3 PDMS points. SDPAD is the first fully spike-driven planner evaluated in end-to-end autonomous driving. arXiv:2610.11583v1
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- arXiv Robotics — research abstractsSignalSDPAD: A Fully Spike-Driven Pipeline for End-to-End Autonomous Driving
This paper introduces SDPAD, a fully spike-driven end-to-end planning pipeline for autonomous driving. It converts pre-trained ANN perception stacks into integer-spike form, lifts multi-view images into BEV space using Spike-3D-Lift, and plans through Spike-QFormer, a spiking query transformer. SDPAD achieves high accuracy and low energy consumption, outperforming previous SNN planners and matching mainstream ANN planners in energy efficiency.
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