TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis
<|reference_start|>TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis: 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.<|reference_end|>
@article{polley2024tld-ready:,
    title={TLD-READY: Traffic Light Detection -- Relevance Estimation and
  Deployment Analysis},
    author={Nikolai Polley, Svetlana Pavlitska, Yacin Boualili, Patrick Rohrbeck,
  Paul Stiller, Ashok Kumar Bangaru, J. Marius Z"ollner},
    journal={arXiv preprint arXiv:2409.07284},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07284},
    primaryClass={cs.CV cs.LG}
}