Traffic Light Recognition using Convolutional Neural Networks: A Survey
Svetlana Pavlitska Nico Lambing Ashok Kumar Bangaru J. Marius Zöllner
Real-time traffic light recognition is essential for autonomous driving. Yet,
a cohesive overview of the underlying model architectures for this task is
currently missing. In this work, we conduct a comprehensive survey and analysis
of traffic light recognition methods that use convolutional neural networks
(CNNs). We focus on two essential aspects: datasets and CNN architectures.
Based on an underlying architecture, we cluster methods into three major
groups: (1) modifications of generic object detectors which compensate for
specific task characteristics, (2) multi-stage approaches involving both
rule-based and CNN components, and (3) task-specific single-stage methods. We
describe the most important works in each cluster, discuss the usage of the
datasets, and identify research gaps.