Dynamic Vision Sensors (DVS), offer a unique advantage in control applications, due to their high temporal resolution, and asynchronous event-based data. Still, their adoption in machine learning algorithms remains limited. To address this gap, and promote the development of models that leverage the specific characteristics of DVS data, we introduce the Multi-Modal Dynamic-Vision-Sensor Line Following dataset (MMDVS-LF). This comprehensive dataset, is the first to integrate multiple sensor modalities, including DVS recordings, RGB video, odometry, and Inertial Measurement Unit (IMU) data, from a small-scale standardized vehicle. Additionally, the dataset includes eye-tracking and demographic data of drivers performing a Line Following task on a track. With its diverse range of data, MMDVS-LF opens new opportunities for developing deep learning algorithms, and conducting data science projects across various domains, supporting innovation in autonomous systems and control applications.

翻译：动态视觉传感器（DVS）凭借其高时间分辨率和基于事件的异步数据特性，在控制应用中展现出独特优势。然而，其在机器学习算法中的应用仍较为有限。为弥补这一差距，并推动利用DVS数据特性的模型发展，我们提出了多模态动态视觉传感器循线数据集（MMDVS-LF）。该综合性数据集首次整合了来自小型标准化车辆的多传感器模态数据，包括DVS记录、RGB视频、里程计以及惯性测量单元（IMU）数据。此外，数据集还包含了驾驶员在赛道上执行循线任务时的眼动追踪数据与人口统计学信息。凭借其多样化的数据范围，MMDVS-LF为开发深度学习算法及开展跨领域数据科学项目提供了新的机遇，有助于推动自主系统与控制应用的创新发展。