Moving Object Path Prediction in Traffic Scenes Using Contextual Information

Abstract

Moving object path prediction in traffic scenes from the perspective of a moving vehicle can improve safety on the road, which is the aim of Advanced Driver Assistance Systems (ADAS). However, this task still remains a challenge. Work has been carried out on the use of x,y positional information of the moving objects only. However, besides positional information there is more information that surrounds a vehicle that can be leveraged in the prediction along with the x,y features. This is known as contextual information. In this work, a deep exploration of these features is carried out by evaluating different types of data, using different fusion strategies. The core architectures of this model are CNN and LSTM architectures. It is concluded that in the prediction task, not only are the features important, but the way they are fused in the developed architecture is also of importance.

Keywords

• time series
• path prediction
• traffic scenes
• LSTMs