A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data

arXiv:2404.03493v1 Announce Type: cross
Abstract: Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Neural Networks (SNNs). However, the state-of-the-art works in SNN-based AD systems still focus on proposing network models that can achieve high accuracy, and they have …

abstract accuracy arxiv automotive autonomous autonomous driving classification computer computer vision consumption cs.ai cs.lg cs.ne cs.ro data detection driving energy event future human image impact low low power methodology mobility network neural network object parameters power segmentation spiking neural network study systems tasks transportation type vision