Abstract
Recently, event-based vision sensors have gained attention for autonomous driving applications, as conventional RGB cameras face limitations in handling challenging dynamic conditions. However, the availability of real-world and synthetic event-based vision datasets remains limited. In response to this gap, we present SEVD, a first-of-its-kind multi-view ego, and fixed perception synthetic event-based dataset using multiple dynamic vision sensors within the CARLA simulator. Data sequences are recorded across diverse lighting (noon, nighttime, twilight) and weather conditions (clear, cloudy, wet, rainy, foggy) with domain shifts (discrete and continuous). SEVD spans urban, suburban, rural, and highway scenes featuring various classes of objects (car, truck, van, bicycle, motorcycle, and pedestrian). Alongside event data, SEVD includes RGB imagery, depth maps, optical flow, semantic, and instance segmentation, facilitating a comprehensive understanding of the scene. Furthermore, we evaluate the dataset using state-of-the-art event-based (RED, RVT) and frame-based (YOLOv8) methods for traffic participant detection tasks and provide baseline benchmarks for assessment. Additionally, we conduct experiments to assess the synthetic event-based dataset's generalization capabilities. The dataset is available at this https URL
Abstract (translated)
最近，基于事件的视觉传感器在自动驾驶应用中引起了关注，因为传统的RGB相机在处理复杂动态条件时存在局限性。然而，实世界和合成事件基于视觉数据集仍然很少可用。为了填补这一空白，我们提出了SEVD，一种前所未有的多视角自利图像和用于CARLA仿真器中的多个动态视觉传感器固定的感知合成事件基于数据集。数据序列在不同的光照（中午，夜景，黄昏）和天气条件（晴朗，云层，潮湿，雨雾）下进行记录，领域转移（离散和连续）也是多样的。SEVD涵盖了城市、郊区、农村和高速公路场景，其中包括各种类型的物体（汽车，卡车，货车，自行车，摩托车和行人）。除了事件数据之外，SEVD还包括RGB图像，深度图，光流，语义和实例分割，从而实现了对场景的全面理解。此外，我们使用最先进的基于事件的（RED，RVT）和基于帧的方法（YOLOv8）对交通参与者检测任务进行评估，并为评估提供了基准基准基准。此外，我们还进行了实验，以评估合成事件基于数据集的泛化能力。该数据集可在https://url上找到。
URL
https://arxiv.org/abs/2404.10540
