  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.
