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.

翻译：有效的交通信号灯检测是自动驾驶汽车感知栈的关键组成部分。本研究引入了一种新颖的深度学习检测系统，同时解决了先前工作中的挑战。通过综合数据集融合，包括Bosch小型交通信号灯数据集、LISA、DriveU交通信号灯数据集以及来自卡尔斯鲁厄的专有数据集，我们确保了在各种场景下的鲁棒性评估。此外，我们提出了一种相关性估计系统，该系统创新性地利用道路上的方向箭头标记，从而无需预先创建地图。在DriveU数据集上，该方法在相关性估计中实现了96%的准确率。最后，我们进行了真实世界评估，以评估这些模型的部署和泛化能力。为了可复现性并促进进一步研究，我们提供了模型权重和代码：https://github.com/KASTEL-MobilityLab/traffic-light-detection。