Road structure classification through artificial neural network for automotive radar systems

This study proposes an artificial neural network-based method to classify road structures for automotive radar systems. Generally, road structures generate unwanted echoes called clutter, which degrades the performance of target detection, and may cause critical errors in autonomous driving modes. However, recognition and classification of each individual structure type is very difficult, because there are various types of structures made by different materials which cause ambiguity in the recognition and classification process. To deal with these classification ambiguities, the authors propose an artificial neural network-based recognition and classification method. Road structures are classified by applying artificial neural network to the frequency domain-received signals of automotive radar systems. Once the neural network has been trained with the received signals, it is used to determine the type of road structure with instantaneous received signals. The classification performance was evaluated by experimenting with the number of antenna elements and radar snapshots. In addition, to find the most suitable artificial neural network structure, the authors experimented with the number of nodes and layers. After completing a period of deep learning using actual experimental data, the total classification accuracy was about 95%.