# Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera

arXiv: https://arxiv.org/abs/2005.01632 · HTML: https://mlsanity.com/paper/2005.01632/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 3 of 3)

- [Geolocation estimation of target vehicles using image processing and geometric computation](https://mlsanity.com/paper/2203.10938/) (2022)
- [Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention](https://mlsanity.com/paper/2212.05432/) (2022)
- [A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction](https://mlsanity.com/paper/2609.07125/) (2026)

## Abstract (arXiv)

Understanding ego-motion and surrounding vehicle state is essential to enable automated driving and advanced driving assistance technologies. Typical approaches to solve this problem use fusion of multiple sensors such as LiDAR, camera, and radar to recognize surrounding vehicle state, including position, velocity, and orientation. Such sensing modalities are overly complex and costly for production of personal use vehicles. In this paper, we propose a novel machine learning method to estimate ego-motion and surrounding vehicle state using a single monocular camera. Our approach is based on a combination of three deep neural networks to estimate the 3D vehicle bounding box, depth, and optical flow from a sequence of images. The main contribution of this paper is a new framework and algorithm that integrates these three networks in order to estimate the ego-motion and surrounding vehicle state. To realize more accurate 3D position estimation, we address ground plane correction in real-time. The efficacy of the proposed method is demonstrated through experimental evaluations that compare our results to ground truth data available from other sensors including Can-Bus and LiDAR.