  Speed estimation of an ego vehicle is crucial to enable autonomous driving
and advanced driver assistance technologies. Due to functional and legacy
issues, conventional methods depend on in-car sensors to extract vehicle speed
through the Controller Area Network bus. However, it is desirable to have
modular systems that are not susceptible to external sensors to execute
perception tasks. In this paper, we propose a novel 3D-CNN with
masked-attention architecture to estimate ego vehicle speed using a single
front-facing monocular camera. To demonstrate the effectiveness of our method,
we conduct experiments on two publicly available datasets, nuImages and KITTI.
We also demonstrate the efficacy of masked-attention by comparing our method
with a traditional 3D-CNN.

Computer Vision and Pattern Recognition