Trajectory Prediction of Road Users for Autonomous Vehicles

Abstract

Trajectory prediction of road users is a very important task in autonomous vehicles for road safety. It becomes even more important when road users are pedestrians being more vulnerable. Recently, machine learning based approaches have been used for trajectory prediction of pedestrians including deep learning using deep neural networks. It is a question that what are the best features to include in order to predict the future trajectory of pedestrians more accurately. Moreover, the recurrent neural networks (RNNs) based decoder in enocoder-decoder architecture suffer from the accumulated error that degrades long term predictions. In this work, feature selection for the trajectory prediction of pedestrians has been done and a novel bi-directional RNN decoder has been proposed. JAAD and PIE datasets have been used which focus on pedestrians. Different groups of available features in the datasets were selected, and the exploration analysis was done. Furthermore, in order to address the issue of accumulated error in unidirectional RNN decoders, a bi-directional decoder which has a forward RNN and a backward RNN, was proposed and compared with the uni-directional decoder. Results show that attributes features when added with spatial features improve the accuracy of the trajectory prediction and the proposed bi-directional decoder improves the accuracy of long term trajectory prediction.