首个真实世界昼夜对齐暗光自动驾驶数据集，解决夜间视觉感知难题。
DarkDriving: A Real-World Day and Night Aligned Dataset for Autonomous Driving in the Dark Environment
- 基于自动轨迹跟踪的位姿匹配方法，在69亩封闭场地采集昼夜图像对。
- 含9538对精确对齐图像，定位误差仅几厘米，每对标注2D边界框。
- 支持暗光增强、检测等4项任务，可推广至nuScenes等场景。
低光照条件对自动驾驶的视觉感知系统构成挑战。本文提出首个真实世界昼夜对齐的暗光自动驾驶基准数据集DarkDriving。现有真实低光照数据集多限于小范围曝光控制与静态场景，且夜间数据缺乏精确对应的白天图像。在69英亩封闭测试场，通过提出的自动昼夜轨迹跟踪位姿匹配（TTPM）方法，首次实现了动态驾驶场景下的昼夜图像精准对齐。DarkDriving包含9538对位置与内容精确对齐的昼夜图像对，对齐误差仅数厘米，并为每对图像手动标注了2D边界框。该数据集涵盖四项感知任务：低光照增强、广义低光照增强，以及暗光环境下2D与3D目标检测。实验表明，DarkDriving为评估暗光增强提供了全面基准，且可泛化至其他低光照驾驶场景（如nuScenes）。代码与数据集将公开于https://github.com/DriveMindLab/DarkDriving-ICRA-2026。
原文摘要 · Abstract (English)
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.
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