# Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention

arXiv: https://arxiv.org/abs/2212.05432 · HTML: https://mlsanity.com/paper/2212.05432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 11 Dec 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Video Action Transformer Network](https://mlsanity.com/paper/1812.02707/) (2018)
- [Long-Term Feature Banks for Detailed Video Understanding](https://mlsanity.com/paper/1812.05038/) (2018)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [YOLOP: You Only Look Once for Panoptic Driving Perception](https://mlsanity.com/paper/2108.11250/) (2021)
- [Video Transformers: A Survey](https://mlsanity.com/paper/2201.05991/) (2022)
- [Human Pose Estimation in Space and Time using 3D CNN](https://mlsanity.com/paper/1609.00036/) (2016)
- [Hand3D: Hand Pose Estimation using 3D Neural Network](https://mlsanity.com/paper/1704.02224/) (2017)
- [Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera](https://mlsanity.com/paper/2005.01632/) (2020)
- [Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information](https://mlsanity.com/paper/1907.06989/) (2019)

## Built on by (most cited first, 1 of 1)

- [nuScenes Revisited: Progress and Challenges in Autonomous Driving](https://mlsanity.com/paper/2512.02448/) (2025)

## Abstract (arXiv)

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.