DOI: 10.5281/zenodo.14775869
title: The ATLAS of Traffic Lights

description: This is an older version: Please use the newest available version.Changelog:



31. Jan. 2024: v0.1 - We released a small dataset sample. Until the full release on the 28. Feb. 2025, the annotation format may be subject to change.


ATLAS

ATLAS (Applied Traffic Light Annotation Set) is a new, publicly available dataset designed to improve traffic light detection for autonomous driving. Existing open-source datasets often omit certain traffic light states and lack camera configurations for near and far distances. To address this, ATLAS features over 33,000 images collected from three synchronized cameras—wide, medium, and tele—with varied fields of view in the German city of Karlsruhe. This setup captures traffic lights at diverse distances and angles, including difficult overhead views. Each of the dataset’s 72,998 bounding boxes is meticulously labeled for 25 unique pictogram-state classes, covering rare but critical states (e.g., red-yellow) and pictograms (straight-right, straight-left). Additional annotations include challenging conditions such as heavy rain. All data is anonymized using state-of-the-art tools. ATLAS provides a comprehensive, high-quality resource for robust traffic light detection, overcoming limitations of existing datasets.




Camera
FOV [°]
Resolution
Images


Front-Medium
61 × 39 
1920 × 1200
25,158


Front-Tele
31 × 20 
1920 × 1200
5,109


Front-Wide
106 × 92 
2592 × 2048 
2,777




Directory Format:

We provide the dataset in the following format:

├── ATLAS                                                            ├── train                                                          ├── front_medium                                                    ├── images                                                          ├── front_medium_1722622455-950002160.jpg                    ├── labels                                                         ├── front_medium_1722622455-950002160.txt                  ├── front_tele                                                  ├── front_wide                                                ├── test                                                          ├── ATLAS_classes.yaml                                          ├── LICENSE                                                     └── README.md                                               

 

Annotation Format:

Each line in an annotation file describes one bounding box using five fields:

class_id  x_center  y_center  width  height



class_id: An integer indicating the class of the annotated object. The file ATLAS_classes.yaml contains human-readable names corresponding to each numeric label.

x_center, y_center: The normalized coordinates of the bounding box center, relative to the image dimensions (in the range [0,1]), where x_center is measured horizontally and y_center vertically.

width, height: The normalized width and height of the bounding box, also expressed in the range [0,1]. These values are obtained by dividing the bounding box width and height in pixels by the overall image width and height, respectively.


 

Terms and Conditions

The ATLAS Dataset by FZI Research Center for Information Technology is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Therefore, the Dataset is only allowed to be used for non-commercial purposes, such as teaching and research. The Licensor thus grants the End User the right to use the dataset for its own internal and non-commercial use and the purpose of scientific research only. There may be inaccuracies, although the Licensor tried and will try its best to rectify any inaccuracy once found. We invite all users to report remarks via mail at polley@fzi.deIf the dataset is used in media, a link to the Licensor’s website is to be included. In case the End User uses the dataset within research papers, the following publication should be quoted:



Polley et al.: The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving (under review)