AN AUTOMOTIVE RADAR DATASET FOR OBJECT CLASSIFICATION

Autonomous and semi-autonomous navigation systems use multiple sensors for perception. Amongst all the sensors, the most prominently used are camera, lidar and radar. In addition to being more expensive than radar, camera and lidar fail to operate smoothly in adverse weather conditions. Radar, however, can operate in a wide range of temperatures and weather conditions. Given radar's capabilities and recent advancements in deep learning, can a low-cost and robust perception solution be achieved? To address this question, we make the following contributions via this work. We present a novel 77 GHz automotive radar dataset composed of static and moving objects combined with a detailed analysis of the proposed dataset. We also propose using a novel 7 x 5 object representation framework for automotive radar data-based object classification. We design a lightweight CNN architecture to classify objects in the automotive radar scene and demonstrate that the proposed CNN delivers strong performance on our dataset. Additionally, we experiment with the convLSTM architecture to exploit temporal characteristics present in the radar data. Further, we evaluate the performance of standard machine learning algorithms on the proposed dataset. Finally, we show that our CNN can also perform well on an open-source automotive radar dataset. Our dataset and codes are available at https://github.com/lfovia/UAY-INVECAS-IITH