  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.
