English(EN) Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation

脉冲神经网络在行人过马路意图分类中实现高精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新颖的卷积脉冲神经网络（Conv-SNN），用于使用事件视觉分类行人过马路意图。该方法将真实驾驶录像转换为合成动态视觉传感器（DVS）事件流，并通过模拟DVS序列进行训练增强。所得模型在各种数据集上实现了高精度，在稀疏时间数据上运行效率更高，同时优于先前的基于帧的方法。 AI

影响这项研究可以通过提高自动驾驶汽车预测行人行为的能力来增强其安全性和效率。

排序理由该集群包含一篇学术论文，详细介绍了新颖的模型架构及其在特定数据集上的性能。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Henok Teklu, Mustafa Sakhai, Maciej Wielgosz, Matej Mertik · 2026-09-15 04:00

基于事件视觉的行人过马路意图分类：结合卷积脉冲神经网络与时间增强

arXiv:2609.13328v1 Announce Type: cross Abstract: 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. Conventio…