Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models

Abstract

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye view).

Moving Object Path Prediction in Traffic Scenes Using Contextual Information

“…In previous work [1,2], only the past positions of the observed object in a scene have been used to predict its future path. However, in traffic scenarios there is a rich set of additional information available about the environment of the ego vehicle and each object in the scene.…”

“…In [2], it was shown that LSTMs can be extended to predict multiple paths by combining them with Mixture Density Models (MDMs) as a final layer. However, in this work, a single path was predicted to better analyse the impact of the contextual features.…”