Traffic Scene Prediction via Deep Learning: Introduction of Multi-Channel Occupancy Grid Map as a Scene Representation

Abstract

When predicting future motions of surrounding vehicles for autonomous vehicles, the inter-vehicular interaction must be considered in order to predict future risks and to make safe and intelligent decisions. This becomes critical when it comes to conflicting driving situation such as lane merge, tollgate area, and unsignalized intersections. Previously developed future prediction algorithms show limited performance when handling interactions and conflicts between vehicles because they focused on predicting individual vehicle motion and/or interaction between a single pair of vehicles rather than the entire traffic scene. In this paper, a scene representation method, namely multi-channel Occupancy Grid Map (OGM), is proposed to describe the entire traffic scene, which is then utilized for the deep learning architecture that predicts the future traffic scene or OGM. Multi-channel OGM represents entire traffic scene as a manner of image-like structure from bird's eye view composed with dynamic layer and static layer depicting the occupancy of the dynamic and static objects. By using this 2D traffic scene representation, future prediction can be modeled as a video processing problem, where future time-serial image sequence need to be predicted. In order to predict future traffic scenes based on past traffic scenes, a deep learning architecture is proposed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Networks. With the proposed deep learning architecture, future prediction accuracy in highly conflicting traffic situation is guaranteed up to 90 percent with 3 seconds of prediction horizon. A video ofthe traffic scene prediction results is available online