DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
This work addresses the problem of efficient and accurate object classification in automotive applications, representing an incremental improvement over prior methods.
The paper tackles object classification for automotive radar by introducing a lightweight deep learning method that processes unordered radar reflection lists, outperforming existing handcrafted and learned feature methods in experiments with real data.
This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class information such as pedestrian, cyclist, car, or non-obstacle. The method is both powerful and efficient, by using a light-weight deep learning approach on reflection level radar data. It fills the gap between low-performant methods of handcrafted features and high-performant methods with convolutional neural networks. The proposed network exploits the specific characteristics of radar reflection data: It handles unordered lists of arbitrary length as input and it combines both extraction of local and global features. In experiments with real data the proposed network outperforms existing methods of handcrafted or learned features. An ablation study analyzes the impact of the proposed global context layer.