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Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation — arXiv:2609.13328

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13328
License (as archived): CC0-1.0
Archived: 2026-10-15T22:48:24.579Z
SHA-256 of the archived text: eb29aff8c84304d52103b13af4196ed31db8284b7ce70c6fd434944c88f79292
Stable URL of this page: https://metraipolis.com/documento/dc_7becfe4191fb

How to cite

Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation — arXiv:2609.13328 — arXiv (en), https://arxiv.org/abs/2609.13328 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7becfe4191fb (Metraipolis archive dc_7becfe4191fb, sha256 eb29aff8c84304d52103b13af4196ed31db8284b7ce70c6fd434944c88f79292)

BibTeX

@misc{dc7becfe4191fb,
  title = {Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation — arXiv:2609.13328},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13328},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7becfe4191fb; sha256 eb29aff8c84304d52103b13af4196ed31db8284b7ce70c6fd434944c88f79292},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13328. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Anticipating whether a pedestrian will cross the road is safety-critical for autonomous vehicles, requiring real-time inference under challenging conditions including motion blur, high dynamic range, and class imbalance. Conventional frame-based deep networks process redundant RGB data at fixed frame rates, limiting their temporal resolution and energy efficiency. In this work we present an end-to-end pipeline that (i) converts real-world driving footage from the Joint Attention in Autonomous Driving (JAAD) dataset into synthetic dynamic vision sensor (DVS) event streams using the v2e simulator, (ii) augments training with the CARLA-simulated DVS sequences of the DVS-PedX dataset under both normal and adverse weather conditions, and (iii) trains a novel convolutional spiking neural network (Conv-SNN) with clip-consistent DVS augmentation to classify pedestrian crossing intent as binary: crossing or non-crossing. We detail all architectural decisions, the exact leaky-integrate-and-fire neuron dynamics with surrogate-gradient learning, the class-balanced loss formulation, JAAD oversampling at 6x, and a 70/15/15 stratified splitting protocol. The trained model achieves 95.83% accuracy and F1 = 0.9695 on the JAAD DVS test set, 97.79% accuracy and F1 = 0.9478 on normal CARLA DVS, and 94.78% accuracy and F1 = 0.8369 on adverse-weather CARLA DVS, all from a 1.07M-parameter architecture trained on CPU. Compared to prior frame-based approaches on JAAD, our method closes or surpasses the reported accuracy while operating natively on sparse temporal representations. We include a thorough analysis of the convergence behaviour across all 15 training epochs, domain transfer characteristics, and a quantitative comparison with representative related work.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.13328 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:29:03.457Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).