Polley, Nikolai (Karlsruhe Institute of Technology), Pavlitska, Svetlana (FZI Research Center for Information Technology), Boualili, Yacin (Karlsruhe Institute of Technology), Rohrbeck, Patrick (Karlsruhe Institute of Technology), Stiller, Paul (Karlsruhe Institute of Technology), Bangaru, Ashok Kumar (Karlsruhe Institute of Technology (KIT)), Zöllner, J. Marius (FZI Research Center for Information Technology; KIT Karlsruhe In)

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

Scheduled for presentation during the Poster Session "Transportation Data Analysis and Calibration" (FrAT17), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 8, 2024

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.