Time-to-Collision (TTC) estimation lies in the core of the forward collision warning (FCW) functionality, which is key to all Automatic Emergency Braking (AEB) systems. Although the success of solutions using frame-based cameras (e.g., Mobileye's solutions) has been witnessed in normal situations, some extreme cases, such as the sudden variation in the relative speed of leading vehicles and the sudden appearance of pedestrians, still pose significant risks that cannot be handled. This is due to the inherent imaging principles of frame-based cameras, where the time interval between adjacent exposures introduces considerable system latency to AEB. Event cameras, as a novel bio-inspired sensor, offer ultra-high temporal resolution and can asynchronously report brightness changes at the microsecond level. To explore the potential of event cameras in the above-mentioned challenging cases, we propose EvTTC, which is, to the best of our knowledge, the first multi-sensor dataset focusing on TTC tasks under high-relative-speed scenarios. EvTTC consists of data collected using standard cameras and event cameras, covering various potential collision scenarios in daily driving and involving multiple collision objects. Additionally, LiDAR and GNSS/INS measurements are provided for the calculation of ground-truth TTC. Considering the high cost of testing TTC algorithms on full-scale mobile platforms, we also provide a small-scale TTC testbed for experimental validation and data augmentation. All the data and the design of the testbed are open sourced, and they can serve as a benchmark that will facilitate the development of vision-based TTC techniques.

翻译：碰撞时间（TTC）估计是前向碰撞预警（FCW）功能的核心，而FCW是所有自动紧急制动（AEB）系统的关键。尽管基于帧式相机（例如Mobileye的解决方案）的算法在常规场景中已取得成功，但在某些极端情况下——例如前车相对速度的突变以及行人的突然出现——仍存在无法处理的重大风险。这源于帧式相机固有的成像原理：相邻曝光之间的时间间隔会给AEB系统带来显著延迟。事件相机作为一种新型仿生传感器，具备微秒级的超高时间分辨率，能够异步报告亮度变化。为探索事件相机在上述挑战性场景中的潜力，我们提出了EvTTC数据集。据我们所知，这是首个专注于高相对速度场景下TTC任务的多传感器数据集。EvTTC包含使用标准相机和事件相机采集的数据，涵盖日常驾驶中的多种潜在碰撞场景，涉及多种碰撞对象。此外，数据集还提供了激光雷达（LiDAR）与GNSS/INS测量值，用于计算真实TTC标签。考虑到在全尺寸移动平台上测试TTC算法成本高昂，我们还提供了一个小型TTC测试平台，用于实验验证与数据增强。所有数据及测试平台设计均已开源，可作为基准数据集推动基于视觉的TTC技术发展。