動的視覚センサデータに対するスパイキングニューラルネットワークを用いた自動運転：信号変化検出のケーススタディ
Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection
CARLAシミュレータの写実的な運転シーンでスパイキングニューラルネットワーク(SNN)を信号変化検出に適用し、その有効性と汎化性を検証した研究。
著者: Xuelei Chen, Sotirios Spanogianopoulos
分類: cs.CV, cs.NE, cs.RO, eess.IV
原文アブストラクト
Autonomous driving is a challenging task that has gained broad attention from both academia and industry. Current solutions using convolutional neural networks require large amounts of computational resources, leading to high power consumption. Spiking neural networks (SNNs) provide an alternative computational model to process information and make decisions. This biologically plausible model has the advantage of low latency and energy efficiency. Recent work using SNNs for autonomous driving mostly focused on simple tasks like lane keeping in simplified simulation environments. This paper studies SNNs on photo-realistic driving scenes in the CARLA simulator, which is an important step toward using SNNs on real vehicles. The efficacy and generalizability of the method will be investigated.