This paper explores the representation of vehicle lights in computer vision and its implications for various tasks in the field of autonomous driving. Different specifications for representing vehicle lights, including bounding boxes, center points, corner points, and segmentation masks, are discussed in terms of their strengths and weaknesses. Three important tasks in autonomous driving that can benefit from vehicle light detection are identified: nighttime vehicle detection, 3D vehicle orientation estimation, and dynamic trajectory cues. Each task may require a different representation of the light. The challenges of collecting and annotating large datasets for training data-driven models are also addressed, leading to introduction of the LISA Vehicle Lights Dataset and associated Light Visibility Model, which provides light annotations specifically designed for downstream applications in vehicle detection, intent and trajectory prediction, and safe path planning. A comparison of existing vehicle light datasets is provided, highlighting the unique features and limitations of each dataset. Overall, this paper provides insights into the representation of vehicle lights and the importance of accurate annotations for training effective detection models in autonomous driving applications. Our dataset and model are made available at https://cvrr.ucsd.edu/vehicle-lights-dataset

翻译：本文探讨计算机视觉中车辆灯光的表示方法及其对自动驾驶领域各项任务的影响。我们分析了包括边界框、中心点、角点和分割掩膜在内的不同车辆灯光表示规格的优缺点。研究确定了可从车辆灯光检测中获益的三项重要自动驾驶任务：夜间车辆检测、三维车辆朝向估计和动态轨迹线索预测。每项任务可能要求不同的灯光表示形式。针对训练数据驱动模型所需大规模数据集的收集与标注挑战，本文提出了LISA车辆灯光数据集及配套的灯光可见性模型，该模型专门为车辆检测、意图与轨迹预测及安全路径规划等下游应用提供灯光标注。通过对比现有车辆灯光数据集，本文揭示了各数据集的独特特征与局限性。整体而言，本研究为自动驾驶应用中车辆灯光的表示方法以及精确标注对训练有效检测模型的重要性提供了深入见解。我们的数据集和模型已公开于https://cvrr.ucsd.edu/vehicle-lights-dataset。