ResearchResearch paperRobotics & Embodied AI1 source · Oct 8, 2026

SDPAD: A Fully Spike-Driven Pipeline for End-to-End Autonomous Driving

We present SDPAD, a fully spike-driven end-to-end planning pipeline that closes this gap.

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.
On the nuScenes open-loop benchmark, SDPAD achieves an average $L2$ 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.
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.

[1]SDPAD: A Fully Spike-Driven Pipeline for End-to-End Autonomous Driving
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 09:33 AM
We present SDPAD, a fully spike-driven end-to-end planning pipeline that closes this gap.
End-to-end autonomous driving demands trajectory planners that are both highly accurate and cheap enough for edge deployment.

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