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arXiv AI By Chengjun Zhang, Yuhao Zhang, Jie Yang, Mohamad Sawan

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

Read the original on arXiv AI →

SDPAD is a fully spike‑driven end‑to‑end planning pipeline for autonomous driving that converts a pre‑trained ANN perception stack into integer‑spike form, lifts multi‑view images into a bird’s‑eye‑view space with a spike‑driven depth distribution, and plans via a spiking query transformer that fuses ego, agent, and map queries. On the nuScenes benchmark it matches strong ANN planners with an average L₂ error of 0.40 m and a collision rate of 0.12 %, while consuming only 69.9 mJ—less than 2 % of recent ANN baselines. In closed‑loop NAVSIM evaluation it achieves 86.3 PDMS, surpassing the previous SNN planner SAD by 4.3 points and matching mainstream ANN planners at a fraction of their energy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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