TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios

Yuheng Shi, Zehao Huang, Yan Yan, Naiyan Wang, Xiaojie Guo

Keywords: Time-to-Contact Estimation, Dataset

TL;DR: 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.

Abstract: 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.

Primary Area: datasets and benchmarks

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Submission Number: 9433