Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights to ensure safe navigation through complex urban environments. In this work, we propose a modularized perception framework that integrates state-of-the-art detection models with a novel real-time association and decision framework, enabling seamless deployment into an autonomous driving stack. To address the limitations of existing public datasets, we introduce the ATLAS dataset, which provides comprehensive annotations of traffic light states and pictograms across diverse environmental conditions and camera setups. This dataset is publicly available atthis https URL. We train and evaluate several state-of-the-art traffic light detection architectures on ATLAS, demonstrating significant performance improvements in both accuracy and robustness. Finally, we evaluate the framework in real-world scenarios by deploying it in an autonomous vehicle to make decisions at traffic light-controlled intersections, highlighting its reliability and effectiveness for real-time operation.
View on arXiv@article{polley2025_2504.19722,
title={ The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving },
author={ Rupert Polley and Nikolai Polley and Dominik Heid and Marc Heinrich and Sven Ochs and J. Marius Zöllner },
journal={arXiv preprint arXiv:2504.19722},
year={ 2025 }
}