RNN-Based Path Prediction of Obstacle Vehicles With Deep Ensemble

Abstract

In this paper, a new approach for obstacle vehicle path prediction, which is important for advanced driver assistance systems (ADAS) and autonomous vehicles, is proposed based on a deep neural network. In order to analyze sequential sensor data, a recurrent neural network (RNN) is used and the input data for RNN is drawn from three sensors: LIDAR, camera and GPS. These sensor data are obtained experimentally with real vehicles. In addition, deep ensemble is used for robustness of the estimation and acquisition of the uncertainty. The predicted path of the proposed method is continuous and it predicts both short-term and long-term path with a single algorithm. The size of the network model is small, but it shows good performance in predicting future trajectory of obstacle vehicles.