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arXiv · 2309.02158

Traffic Light Recognition using Convolutional Neural Networks: A Survey

Svetlana Pavlitska·Nico Lambing·Ashok Kumar Bangaru·J. Marius Zöllner
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Abstract

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.

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Svetlana Pavlitska, Nico Lambing, Ashok Kumar Bangaru, J. Marius Zöllner. 2023-09-05. Traffic Light Recognition using Convolutional Neural Networks: A Survey. https://arxiv.org/abs/2309.02158

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