Accurate localization and classification of traffic lights in driving scenes are crucial for enhancing road scene understanding in various intelligent vehicles applications. However, determining which traffic lights are relevant for the ego-vehicle remains an under-explored challenge.
In our work, we address both the local task of identifying the state and relevance of each traffic light in an image
and the strictly related global task of recommending the correct course of action for the ego-vehicle (should it stop?).
We propose a novel architecture, which not only localizes each traffic light
and identifies its relevance with respect to the ego-vehicle, but also generates a global recommendation.
To address the scarcity of datasets with these types of annotations, we also introduce the VZC Traffic Light Dataset (VZC-TLD), the first U.S. dataset that provides 3,000 images annotated with traffic light boxes, states, and relevance.
@article{trinci2025color,
title={Color is not enough: dataset and method for identifying relevant traffic lights in driving scenes},
author={Trinci, Tomaso and Magistri, Simone and Bianconcini, Tommaso and Taccari, Leonardo and Sarti, Leonardo and Sambo, Francesco},
journal={IEEE Transactions on Intelligent Transportation Systems},
year={2025},
note={In Press},
}