Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention
This addresses the need for modular, sensor-independent speed estimation systems in autonomous driving and advanced driver assistance, though it appears incremental as it builds on existing 3D-CNN methods.
The paper tackles ego vehicle speed estimation by proposing a novel 3D-CNN with masked-attention architecture using a single front-facing monocular camera, achieving results demonstrated on nuImages and KITTI datasets with comparisons to traditional 3D-CNNs.
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.