Descriptor: LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS)

2026-07-02

AI Summary

This study addresses the limitations of existing road safety systems, which predominantly focus on post-collision mitigation and struggle to enable early collision prediction under low-visibility conditions. To bridge this gap, the authors introduce a multimodal dataset comprising 4,000 synchronized RGB-infrared image pairs, captured across diverse driving environments—including urban, rural, and mountainous regions near Grenoble, France—under varied weather and lighting conditions. The dataset features vehicle platoons compliant with European standards and incorporates real-world edge cases. Leveraging this resource, the authors evaluate the cross-domain generalization capability of the YOLOv8n model, demonstrating its significant improvement in robustness and generalization for pedestrian detection in realistic driving scenarios. This work provides critical data infrastructure to support the development of highly reliable advanced driver assistance systems.

Abstract

Current road safety systems primarily focus on minimizing post-collision damage. However, advances in algorithmic perception are shifting focus toward early collision prediction, especially in lowvisibility conditions like nighttime or fog, where thermal infrared sensing outperforms both human vision and RGB imaging. While available RGB-infrared datasets such as FLIR ADAS and LLVIP are good benchmarks, they mostly consist of clear weather and overly simple scenarios. In this article, we introduce the LYNRED-MDS: Multimodal Detection Subset, a subset of the LYNRED Mobility Dataset, comprised of 4000 RGB-infrared image pairs captured under diverse weather, lighting, and road conditions around Grenoble, France. Our dataset spans varied driving contexts (urban, rural, mountainous, etc.) and a vehicle fleet compliant with Western European standards. Thermal cross-dataset evaluation using a YOLOv8n baseline suggests that our dataset offers strong generalization potential for pedestrian detection in driving scenarios. By covering critical edge cases, our dataset supports the development of more reliable and deployable vision systems for advanced driver-assistance systems.

Problem

• early collision prediction
• low-visibility conditions
• RGB-infrared dataset
• pedestrian detection
• advanced driver-assistance systems

Innovation

• multimodal dataset
• thermal infrared imaging
• pedestrian detection
• low-visibility conditions
• YOLOv8