Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention

TL;DR

This paper introduces a novel 3D convolutional neural network with masked attention to estimate ego vehicle speed from a single monocular camera, reducing reliance on external sensors and improving perception modularity.

Contribution

The paper presents a new 3D-CNN architecture with masked attention for vehicle speed estimation using monocular images, validated on public datasets.

Findings

The proposed method outperforms traditional 3D-CNNs in speed estimation accuracy.

Masked attention enhances the model's focus on relevant features.

Effective speed estimation achieved without external sensors.

Abstract

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.