EvTTC: An Event Camera Dataset for Time-to-Collision Estimation

TL;DR

EvTTC introduces a multi-sensor dataset combining event cameras, standard cameras, LiDAR, and GNSS/INS data to improve time-to-collision estimation in high-speed, challenging driving scenarios, addressing limitations of traditional frame-based systems.

Contribution

This paper presents the first multi-sensor dataset focusing on TTC under high-relative-speed scenarios, including a small-scale testbed and open-source data for benchmarking.

Findings

Event cameras provide ultra-high temporal resolution for TTC estimation.

The dataset covers diverse collision scenarios with multi-sensor data.

Open-source tools facilitate development of vision-based TTC algorithms.

Abstract

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.