首个融合事件相机与眼动数据的循迹数据集，助力事件驱动模型研发。
MMDVS-LF: Multi-Modal Dynamic Vision Sensor and Eye-Tracking Dataset for Line Following
- 整合事件相机、眼动追踪等多模态数据，构建循迹场景全维度记录
- 包含12名驾驶员在小车上的循迹实验数据，覆盖多种感知模态
- 适合研究事件视觉、人机协同与自动驾驶感知的开发者使用
动态视觉传感器（DVS）因其高时间分辨率和异步事件数据，在控制应用中具有独特优势，但其在机器学习中的应用仍受限。为填补这一空白并推动利用DVS特性建模的发展，我们提出了MMDVS-LF：面向循迹任务的多模态动态视觉传感器与眼动追踪数据集。该数据集是首个集成多个传感模态的数据集，包括小型标准化车辆的DVS记录和驾驶员眼动追踪数据，同时包含RGB视频、里程计、惯性测量单元（IMU）数据以及驾驶员人口统计信息。丰富多样的数据为开发事件驱动深度学习算法提供了新机遇，类似于MNIST对卷积神经网络的推动作用。
原文摘要 · Abstract (English)
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 MMDVS-LF: Multi-Modal Dynamic Vision Sensor and Eye-Tracking Dataset for Line Following. This comprehensive dataset is the first to integrate multiple sensor modalities, including DVS recordings and eye-tracking data from a small-scale standardized vehicle. Additionally, the dataset includes RGB video, odometry, Inertial Measurement Unit (IMU) data, and demographic data of drivers performing a Line Following. With its diverse range of data, MMDVS-LF opens new opportunities for developing event-based deep learning algorithms just like the MNIST dataset did for Convolutional Neural Networks.
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