Abstract
The advancement of safety-critical research in driving behavior in ADAS-equipped vehicles require real-world datasets that not only include diverse traffic scenarios but also capture high-risk edge cases such as near-miss events and system failures. However, existing datasets are largely limited to either simulated environments or human-driven vehicle data, lacking authentic ADAS (Advanced Driver Assistance System) vehicle behavior under risk conditions. To address this gap, this paper introduces SAVeD, a large-scale video dataset curated from publicly available social media content, explicitly focused on ADAS vehicle-related crashes, near-miss incidents, and disengagements. SAVeD features 2,119 first-person videos, capturing ADAS vehicle operations in diverse locations, lighting conditions, and weather scenarios. The dataset includes video frame-level annotations for collisions, evasive maneuvers, and disengagements, enabling analysis of both perception and decision-making failures. We demonstrate SAVeD's utility through multiple analyses and contributions: (1) We propose a novel framework integrating semantic segmentation and monocular depth estimation to compute real-time Time-to-Collision (TTC) for dynamic objects. (2) We utilize the Generalized Extreme Value (GEV) distribution to model and quantify the extreme risk in crash and near-miss events across different roadway types. (3) We establish benchmarks for state-of-the-art VLLMs (VideoLLaMA2 and InternVL2.5 HiCo R16), showing that SAVeD's detailed annotations significantly enhance model performance through domain adaptation in complex near-miss scenarios.
Abstract (translated)
在配备了高级驾驶辅助系统（ADAS）的车辆中，推进安全关键性的驾驶行为研究需要包含多样化的交通场景以及捕捉高风险边缘情况的真实世界数据集，例如险情和系统故障。然而，现有的数据集主要局限于模拟环境或人类驾驶员的数据，缺乏在危险条件下真实ADAS车辆行为的信息。为了填补这一空白，本文介绍了SAVED，这是一个大规模的视频数据集，从公开可用的社会媒体内容中精选而来，重点关注与ADAS车辆相关的碰撞、险情和系统退出情况。 SAVED包含2,119个第一视角视频，涵盖了不同地点、光照条件和天气场景下的ADAS车辆操作。该数据集包括了针对碰撞、规避动作以及系统退出的逐帧注释，支持对感知和决策失败的分析。我们通过多种分析和贡献展示了SAVED的实用性： (1) 我们提出了一种新的框架，将语义分割与单目深度估计相结合，用于计算动态物体的实际时间到碰撞（TTC）。 (2) 我们使用广义极值分布来建模并量化不同道路类型中的严重风险在碰撞和险情事件中的发生情况。 (3) 对于当前最先进的视频语言模型VLLMs（VideoLLaMA2 和 InternVL2.5 HiCo R16），我们建立了基准，表明通过复杂险境场景下的领域适应性，SAVED的详细注释显著提升了这些模型的表现。
URL
https://arxiv.org/abs/2512.17724
