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

Key points

• End-to-end autonomous driving demands trajectory planners that are both highly accurate and cheap enough for edge deployment.1
• 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.1
• 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.1
• 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

Sources (1)

• [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.

Extractive summary: sentences quoted from the sources.