Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. Utilizing a comprehensive dataset amalgamation, including the Bosch Small Traffic Lights Dataset, LISA, the DriveU Traffic Light Dataset, and a proprietary dataset from Karlsruhe, we ensure a robust evaluation across varied scenarios. Furthermore, we propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation. On the DriveU dataset, this approach results in 96% accuracy in relevance estimation. Finally, a real-world evaluation is performed to evaluate the deployment and generalizing abilities of these models. For reproducibility and to facilitate further research, we provide the model weights and code: https://github.com/KASTEL-MobilityLab/traffic-light-detection.
@article{arxiv.2409.07284,
title = {TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis},
author = {Nikolai Polley and Svetlana Pavlitska and Yacin Boualili and Patrick Rohrbeck and Paul Stiller and Ashok Kumar Bangaru and J. Marius Zöllner},
journal= {arXiv preprint arXiv:2409.07284},
year = {2025}
}