The low-light conditions are challenging to the vision-centric perception systems for autonomous driving in the dark environment. In this paper, we propose a new benchmark dataset (named DarkDriving) to investigate the low-light enhancement for autonomous driving. The existing real-world low-light enhancement benchmark datasets can be collected by controlling various exposures only in small-ranges and static scenes. The dark images of the current nighttime driving datasets do not have the precisely aligned daytime counterparts. The extreme difficulty to collect a real-world day and night aligned dataset in the dynamic driving scenes significantly limited the research in this area. With a proposed automatic day-night Trajectory Tracking based Pose Matching (TTPM) method in a large real-world closed driving test field (area: 69 acres), we collected the first real-world day and night aligned dataset for autonomous driving in the dark environment. The DarkDriving dataset has 9,538 day and night image pairs precisely aligned in location and spatial contents, whose alignment error is in just several centimeters. For each pair, we also manually label the object 2D bounding boxes. DarkDriving introduces four perception related tasks, including low-light enhancement, generalized low-light enhancement, and low-light enhancement for 2D detection and 3D detection of autonomous driving in the dark environment. The experimental results show that our DarkDriving dataset provides a comprehensive benchmark for evaluating low-light enhancement for autonomous driving and it can also be generalized to enhance dark images and promote detection in some other low-light driving environment, such as nuScenes.

翻译：低光照条件对黑暗环境下基于视觉的自动驾驶感知系统构成严峻挑战。本文提出一个新的基准数据集（命名为DarkDriving），用于研究面向自动驾驶的低光照增强技术。现有真实世界低光照增强基准数据集仅能通过在小范围静态场景中控制不同曝光时间采集，而当前夜间驾驶数据集的暗光图像缺乏精确对齐的日间对应图像。在动态驾驶场景中采集真实世界昼夜对齐数据集的极端困难严重制约了该领域的研究。通过在大规模真实封闭驾驶测试场（面积69英亩）中提出的自动昼夜轨迹追踪位姿匹配方法（TTPM），我们首次采集了面向黑暗环境下自动驾驶的真实世界昼夜对齐数据集。DarkDriving数据集包含9,538对在位置和空间内容上精确对齐的昼夜图像对，其对齐误差仅为厘米级。针对每对图像，我们还人工标注了目标二维边界框。DarkDriving引入了四项与感知相关的任务，包括低光照增强、泛化低光照增强，以及面向黑暗环境下自动驾驶的二维检测与三维检测低光照增强。实验结果表明，我们的DarkDriving数据集为评估面向自动驾驶的低光照增强方法提供了全面基准，并可泛化至其他低光照驾驶环境（如nuScenes）中的暗光图像增强与检测性能提升。