# The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving

arXiv: https://arxiv.org/abs/2504.19722 · HTML: https://mlsanity.com/paper/2504.19722/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [DeepPrivacy2: Towards Realistic Full-Body Anonymization](https://mlsanity.com/paper/2211.09454/) (2022)
- [EgoBlur: Responsible Innovation in Aria](https://mlsanity.com/paper/2308.13093/) (2023)
- [Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars](https://mlsanity.com/paper/1906.11886/) (2019)
- [CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research](https://mlsanity.com/paper/2404.17550/) (2024)
- [One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level](https://mlsanity.com/paper/2404.02645/) (2024)
- [TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis](https://mlsanity.com/paper/2409.07284/) (2024)
- [Traffic Light Recognition using Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2309.02158/) (2023)
- [Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving](https://mlsanity.com/paper/2409.12620/) (2024)
- [A Chefs KISS -- Utilizing semantic information in both ICP and SLAM framework](https://mlsanity.com/paper/2504.02086/) (2025)

## Built on by (most cited first, 2 of 2)

- [Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition](https://mlsanity.com/paper/2511.14391/) (2025)
- [Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors](https://mlsanity.com/paper/2506.04823/) (2025)

## Abstract (arXiv)

Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights to ensure safe navigation through complex urban environments. In this work, we propose a modularized perception framework that integrates state-of-the-art detection models with a novel real-time association and decision framework, enabling seamless deployment into an autonomous driving stack. To address the limitations of existing public datasets, we introduce the ATLAS dataset, which provides comprehensive annotations of traffic light states and pictograms across diverse environmental conditions and camera setups. This dataset is publicly available at https://url.fzi.de/ATLAS. We train and evaluate several state-of-the-art traffic light detection architectures on ATLAS, demonstrating significant performance improvements in both accuracy and robustness. Finally, we evaluate the framework in real-world scenarios by deploying it in an autonomous vehicle to make decisions at traffic light-controlled intersections, highlighting its reliability and effectiveness for real-time operation.