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

• URL: http://arxiv.org/abs/2409.07284v1
• Date: Wed, 11 Sep 2024 14:12:44 GMT
• Title: TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis
• Authors: Nikolai Polley, Svetlana Pavlitska, Yacin Boualili, Patrick Rohrbeck, Paul Stiller, Ashok Kumar Bangaru, J. Marius Zöllner,
• Abstract summary: 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. We propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation.
• Score: 9.458657306918859
• License: http://creativecommons.org/licenses/by/4.0/
• 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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