Towards Land Vehicle Forward Velocity Estimation using Deep Learning and
Onboard Radars
- Paulo Ricardo Marques de Araujo ,
- Aboelmagd Noureldin ,
- Sidney Givigi
Abstract
Radars have been increasingly implemented in modern vehicles. Their
presence opens opportunities for developing new positioning and
collision avoidance solutions, to cite a few. However, radar scans are
noisy and sparse, which challenges the robustness of the developed
solutions. In this paper, we propose a novel method to structure radar
scans to create point descriptors. The structured data is used in
convolutional neural networks that explore orthogonal views of the radar
point cloud. Experimental results demonstrate that the model performs
well in estimating the forward velocity of the vehicle using only the
radar scans, providing estimations at a higher data rate than odometers
available in the vehicles.