Intent prediction of vulnerable road users from motion trajectories using stacked LSTM network

Abstract

Intent prediction of vulnerable road users (VRUs) has got some attention recently from the research community, due to its critical role in the advancement of both advanced driving assistance systems (ADAS) and highly automated vehicles development. Most of the proposed techniques for addressing the intent prediction problem have been focusing mainly on two methodologies, namely dynamical motion modelling and motion planning. Despite how powerful these techniques are, but they both rely on hand crafting a set of specific features which are scene specific, which in return affects their generalization to unseen scenes which involves VRUs. In this paper a novel end-to-end data-driven approach is proposed for long-term intent prediction of VRUs such as pedestrians in urban traffic environment based solely on their motion trajectories. The intent prediction problem was formulated as a time-series prediction problem, whereas by just observing a short-window sequence of motion trajectory of pedestrians, a forecasting about their future lateral positions can be made up to 4 secs ahead. In the proposed approach, we utilized the widely adopted architecture of recurrent neural networks, Long-Short Term Memory networks (LSTM) architecture to form a deep stacked LSTM network. The proposed stacked LSTM model was evaluated on one of the popular datasets for intent and path prediction of pedestrians in four unique traffic scenarios that involve pedestrians in an urban environment. Our proposed approach demonstrated competent results in comparison to the baseline approaches in terms of long-term prediction with small lateral position error of 0.39 meters, 0.48 meters, 0.46 meters and 0.51 meters respectively in the four scenarios of the testing dataset.