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.The code and dataset will be publicly available at https://github.com/DriveMindLab/DarkDriving-ICRA-2026.

翻译：低光照条件对黑暗环境下自动驾驶的视觉感知系统构成挑战。本文提出名为DarkDriving的新型基准数据集，用于研究自动驾驶中的低光照增强技术。现有实景低光照增强基准数据集仅能在小范围静态场景中通过控制不同曝光度采集，当前夜间驾驶数据集的暗光图像缺乏精确对齐的日间对照。动态驾驶场景中采集实景昼夜对齐数据集的极大困难严重制约了该领域研究。通过在大型实景封闭驾驶测试场（面积69英亩）中提出的自动昼夜轨迹追踪姿态匹配方法（TTPM），我们首次构建了面向黑暗环境下自动驾驶的实景昼夜对齐数据集。DarkDriving数据集包含9,538对在位置与空间内容上精确对齐的昼夜图像对，对齐误差仅为数厘米。每对图像均人工标注了目标二维边界框。该数据集引入四项感知相关任务：低光照增强、泛化低光照增强，以及针对黑暗环境下自动驾驶的二维检测与三维检测的低光照增强。实验结果表明，DarkDriving数据集为评估自动驾驶低光照增强技术提供了全面基准，并可推广至其他低光照驾驶环境（如nuScenes）的图像增强与检测任务。代码与数据集将于https://github.com/DriveMindLab/DarkDriving-ICRA-2026公开。