论文标题
颞脉冲驱动的尖峰神经网络，用于自动驾驶中的快速对象识别
Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving
论文作者
论文摘要
长期以来，从感觉数据中准确的实时对象识别一直是自主驾驶的至关重要且具有挑战性的任务。即使已成功应用于该领域的深度神经网络（DNN），但大多数现有方法仍然在很大程度上依赖于从激光雷达传感器衍生的脉冲信号的预处理，因此引入了其他计算开销和相当大的潜伏期。在本文中，我们提出了一种使用尖峰神经网络（SNN）的原始时间脉冲直接解决对象识别问题的方法。在各种数据集（包括SIM LIDAR，KITTI和DVS-BARREL）上进行评估，我们提出的方法表现出与最新方法相当的性能，同时实现了显着的时间效率。它突出了SNN在自动驾驶和相关应用中的巨大潜力。据我们所知，这是第一次使用SNN直接对原始时间脉冲执行对象识别的尝试。
Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving. Even though deep neural networks (DNNs) have been successfully applied in this area, most existing methods still heavily rely on the pre-processing of the pulse signals derived from LiDAR sensors, and therefore introduce additional computational overhead and considerable latency. In this paper, we propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN). Being evaluated on various datasets (including Sim LiDAR, KITTI and DVS-barrel) derived from LiDAR and dynamic vision sensor (DVS), our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency. It highlights the SNN's great potentials in autonomous driving and related applications. To the best of our knowledge, this is the first attempt to use SNN to directly perform object recognition on raw temporal pulses.
