Time-to-Contact (TTC) estimation is a critical task for assessing collision risk and is widely used in various driver assistance and autonomous driving systems. The past few decades have witnessed development of related theories and algorithms. The prevalent learning-based methods call for a large-scale TTC dataset in real-world scenarios. In this work, we present a large-scale object oriented TTC dataset in the driving scene for promoting the TTC estimation by a monocular camera. To collect valuable samples and make data with different TTC values relatively balanced, we go through thousands of hours of driving data and select over 200K sequences with a preset data distribution. To augment the quantity of small TTC cases, we also generate clips using the latest Neural rendering methods. Additionally, we provide several simple yet effective TTC estimation baselines and evaluate them extensively on the proposed dataset to demonstrate their effectiveness. The proposed dataset is publicly available at https://open-dataset.tusen.ai/TSTTC.

翻译：碰撞时间（TTC）估计是评估碰撞风险的关键任务，广泛应用于各类驾驶员辅助系统和自动驾驶系统中。过去几十年间，相关理论与算法得到了显著发展。当前主流的基于学习方法亟需大规模真实场景下的TTC数据集。本文提出一个面向驾驶场景的大规模物体级TTC数据集，旨在推动基于单目摄像头的TTC估计研究。为采集有效样本并使得不同TTC值的数据保持相对均衡，我们遍历了数千小时的驾驶数据，按预设数据分布筛选出超过20万个序列。为增加小TTC样本的数量，我们还利用最新神经渲染技术生成视频片段。此外，我们提供了多个简洁有效的TTC估计基线方法，并在所提数据集上进行了充分评估以验证其有效性。本数据集公开访问地址为：https://open-dataset.tusen.ai/TSTTC。