{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tld-ready-traffic-light-detection-relevance","title":"TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis","arxiv_id":"2409.07284","date":"2024-09-11","proceeding":null,"authors":["Nikolai Polley","Svetlana Pavlitska","Yacin Boualili","Patrick Rohrbeck","Paul Stiller","Ashok Kumar Bangaru","J. Marius Zöllner"],"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.","url_abs":"https://arxiv.org/abs/2409.07284v1","url_pdf":"https://arxiv.org/pdf/2409.07284v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tld-ready-traffic-light-detection-relevance","repo_url":"https://github.com/kastel-mobilitylab/traffic-light-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2409.07284","atlas_url":"https://app.syntology.ai/?focus=2409.07284","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}