A deep learning approach for localization systems of high-speed objects

This paper addresses a novel deep learning technique for localization systems of high-speed mobile objects such as autonomous vehicles. The presented localization method consists of rough and fine localizations. The rough localization exploits the modified Kalman filtering, which produces the rough location estimates of a high-speed object. Due to an inappropriate threshold value, the rough estimates often lead to a divergence in the modified Kalman filtering. In this paper, the fine localization suppresses the divergence. The proposed fine localization is based on a deep learning technique. Using the rough estimates, the deep learning method classifies the current position of the high-speed object into an appropriate region. Based on the classified region, the rough location estimates are refined into the fine location estimates in the fine-localization step. The experimental results verify that the deep learning approach overcomes the weakness of the modified Kalman method in the localization. The results also show that the proposed method outperforms the conventional Kalman approach in the localization of high-speed objects.