Object Classification of Laser Scanner by Using Recurrent Neural Network

Abstract

These days, laser scanners becomes the primary sensor for advanced driver assistance system (ADAS). The most important theme of ADAS is to distinguish surroundings of egovehicle because notification of situation is the beginning of ADAS such as path planning, mapping and tracking. In this paper, we present approach for object classification by using a laser scanner mounted in vehicle. For object classification, we suggest Recurrent Neural Network (RNN) which is widely used in linguistic study or language model. We rearrange laser scanner data to equivalent theta intervals and apply recurrent neural network model to identify of class about laser scanner point. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in real-life situation.