The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving

Personal use of this material is permitted. The A TLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving Rupert Polley 1, Nikolai Polley 2, Dominik Heid 1, Marc Heinrich 1, Sven Ochs 1, and J. Marius Z ollner 1, 2 Abstract -- 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. T o 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. 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. I NTRODUCTION Perception of traffic lights plays a pivotal role in ensuring the safe navigation of urban environments for autonomous driving (AD). To operate reliably, autonomous vehicles must not only detect and classify traffic lights accurately but also interpret their relevance to the vehicle's current context and programmed trajectory. Complex intersections, occlusions, and environmental conditions such as rain or nighttime visibility remain a challenge. Unlike other perception tasks, such as object detection, where LiDAR can complement vision-based approaches, traffic light recognition primarily relies on real-time camera-based perception. While V ehicle-to-Everything (V2X) communication has the potential to provide traffic light state information, its deployment remains sparse, making vision-based detection the only widely available method.