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（Time-to-Contact, TTC）估计是评估碰撞风险的关键任务，广泛应用于各类驾驶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长足发展。当前基于学习的方法迫切需要面向真实场景的大规模TTC数据集。本文提出了一个驾驶场景中面向对象的大规模TTC数据集，旨在推动基于单目摄像头的TTC估计研究。为收集有价值样本并实现不同TTC值数据的相对均衡，我们遍历上千小时驾驶数据，按照预设数据分布精选出超过20万条序列。针对小TTC样本量不足的问题，我们采用最新神经渲染方法生成了补充片段。此外，我们提供了多个简洁高效的TTC估计基线方法，并在所构建数据集上进行了广泛评估以验证其有效性。该数据集已公开于https://open-dataset.tusen.ai/TSTTC。