%A Rill, R.A. %D 2020 %T Intuitive Estimation of Speed using Motion and Monocular Depth Information %K %X Advances in deep learning make monocular vision approaches attractive for the autonomous driving domain. This work investigates a method for estimating the speed of the ego-vehicle using state-of-the-art deep neural network based optical flow and single-view depth prediction models. Adopting a straightforward intuitive approach and approximating a single scale factor, several application schemes of the deep networks are evaluated and meaningful conclusions are formulated, such as: combining depth information with optical flow improves speed estimation accuracy as opposed to using optical flow alone; the quality of the deep neural network results influences speed estimation performance; using the depth and optical flow data from smaller crops of wide images degrades performance. With these observations in mind, a RMSE of less than 1 m/s for ego-speed estimation was achieved on the KITTI benchmark using monocular images as input. Limitations and possible future directions are discussed as well. %U https://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/49 %J Studia Universitatis BabeČ™-Bolyai Informatica %0 Journal Article %R 10.24193/subbi.2020.1.03 %P 33-45%V 65 %N 1 %@ 2065-9601 %8 2020-04-30