Temporal data augmentation

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papersTODAY 04:00 UTC

Spiking Neural Networks Classify Pedestrian Crossing Intent From Event Cameras

A new arXiv paper proposes using convolutional spiking neural networks with temporal data augmentation to predict whether a pedestrian intends to cross the road, based on event-based camera input. The authors frame the task as safety-critical for autonomous driving, where inference must hold up under motion blur, high dynamic range scenes, and imbalanced classes. The work is positioned as an alternative to conventional frame-based deep learning pipelines for this prediction problem.

spiking-neural-networks/autonomous-driving/event-cameras/safety