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

TL;DR

This paper presents a new deep-learning traffic light detection system that uses diverse datasets and a relevance estimation method based on directional arrows, achieving high accuracy and demonstrating real-world deployment capabilities.

Contribution

Introduces a novel traffic light detection system with relevance estimation using directional arrows, eliminating the need for prior maps, and provides comprehensive evaluation and deployment analysis.

Findings

96% accuracy in relevance estimation on DriveU dataset

Robust evaluation across multiple datasets

Successful real-world deployment demonstration

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.