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Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset
Redouane Khemmar1, Antoine Mauri1, Camille Dulompont1
1Normandie University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France.
Sensors (Basel, Switzerland)
|May 28, 2022
Summary
ESRORAD is a new multimodal dataset for autonomous driving, featuring 100k real and 2.7k virtual images of road and railway scenes. This dataset enhances training for autonomous vehicles (AV) and advanced driver assistance systems (ADAS).
Area of Science:
- Computer Vision
- Robotics
- Artificial Intelligence
Background:
- Autonomous navigation in complex urban environments requires robust visual understanding.
- Current datasets often lack domain specificity, limiting the reliability of perception algorithms for autonomous vehicles (AV) and advanced driver assistance systems (ADAS).
- A significant gap exists in datasets for railway smart mobility applications.
Purpose of the Study:
- To introduce ESRORAD, a novel multimodal hybrid dataset designed to improve the training and validation of AV, ADAS, and autonomous driving systems.
- To address the lack of comprehensive datasets in railway smart mobility.
- To provide a large-scale, richly annotated, and diverse dataset for urban scene understanding.
Main Methods:
- Collected 34 videos, 2.7k virtual images, and 100k real images from road and railway scenes in Rouen and Le Havre.
- Annotated all images with 3D bounding boxes for key classes: persons, cars, and bicycles.
- Conducted an in-depth analysis of dataset characteristics and evaluated state-of-the-art models against popular datasets like KITTI and NUScenes.
Main Results:
- ESRORAD is the first dataset of its kind offering a large volume, abundant annotations, and scene diversity for both road and railway environments.
- Performance evaluation demonstrated the dataset's utility for training and validating 3D object detection algorithms.
- The dataset includes examples of image annotations and prediction results from lightweight 3D object detection algorithms.
Conclusions:
- The ESRORAD dataset significantly enhances the available resources for training and validating perception models for autonomous driving and smart mobility.
- Its comprehensive nature and focus on diverse urban and railway scenarios provide a valuable tool for advancing autonomous system reliability.
- The dataset is publicly available online, facilitating further research and development in the field.
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