oTTC: Object Time-to-Contact for Motion Estimation in Autonomous Driving

TL;DR

This paper introduces oTTC, a method that extends object detection models to estimate time-to-contact for each object in autonomous driving, improving safety and efficiency with a single image.

Contribution

The paper proposes a novel per-object time-to-contact estimation approach integrated with object detection models, reducing computational complexity compared to per-pixel methods.

Findings

Achieves higher precision than existing methods.

Operates effectively using only a single image.

Provides benchmarking results on standard datasets.

Abstract

Autonomous driving systems require a quick and robust perception of the nearby environment to carry out their routines effectively. With the aim to avoid collisions and drive safely, autonomous driving systems rely heavily on object detection. However, 2D object detections alone are insufficient; more information, such as relative velocity and distance, is required for safer planning. Monocular 3D object detectors try to solve this problem by directly predicting 3D bounding boxes and object velocities given a camera image. Recent research estimates time-to-contact in a per-pixel manner and suggests that it is more effective measure than velocity and depth combined. However, per-pixel time-to-contact requires object detection to serve its purpose effectively and hence increases overall computational requirements as two different models need to run. To address this issue, we propose per-object time-to-contact estimation by extending object detection models to additionally predict the time-to-contact attribute for each object. We compare our proposed approach with existing time-to-contact methods and provide benchmarking results on well-known datasets. Our proposed approach achieves higher precision compared to prior art while using a single image.