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arXiv · 2409.07284

TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis

Nikolai Polley·Svetlana Pavlitska·Yacin Boualili·Patrick Rohrbeck·Paul Stiller·Ashok Kumar Bangaru·J. Marius Zöllner
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Abstract

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.

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Nikolai Polley, Svetlana Pavlitska, Yacin Boualili, Patrick Rohrbeck, Paul Stiller, Ashok Kumar Bangaru, J. Marius Zöllner. 2024-09-11. TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis. https://doi.org/10.1109/itsc58415.2024.10919699

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