# Driving planner preprint reports energy use below 2% of neural-network baselines

_Published Thursday, October 8, 2026 at 10:07 PM EDT · Robotics, AI, Science · Latest · Tier 2 — Notable_

Researchers report that SDPAD, an autonomous driving planner using sparse integer signals, consumed 69.9 mJ on the nuScenes benchmark-less than 2% of recent conventional neural-network baselines. The preprint reports an average trajectory error of 0.40 m and a collision rate of 0.12%, comparable to strong conventional planners.

The system converts a pretrained perception network into integer-spike form, transforms camera views into an overhead scene representation and produces a planned trajectory in one forward pass. In closed-loop evaluation on NAVSIM's navtest split, it scored 86.3 PDMS, exceeding the earlier spiking planner SAD by 4.3 points. These results concern benchmark evaluations.

## Sources

- [arXiv Query: search_query=cat:cs.RO&id_list=&start=0&max_results=30](https://arxiv.org/abs/2610.11583v1)

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Canonical: https://techandbusiness.org/newswire/QJ4HO6Vu7TwQIcFKkbIfkU
Published: 2026-10-09T02:07:16.710Z
Story chronology: 2026-10-08T09:33:19.000Z
Retrieved: 2026-10-09T05:19:13.353Z
Publisher: Tech & Business (techandbusiness.org)