MCENET

Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

authored by: Hao Cheng, Wentong Liao, Michael Ying Yang, Monika Sester, Bodo Rosenhahn
Abstract: Trajectory prediction in urban mixed-traffic zones (a.k. a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.
Organisation(s): Institute of Cartography and Geoinformatics
Institute of Information Processing
Leibniz Research Centre FZ:GEO
External Organisation(s): University of Twente
Type: Conference contribution
No. of pages: 9
Publication date: 2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Information Systems and Management, Artificial Intelligence, Decision Sciences (miscellaneous), Education, Modelling and Simulation
Sustainable Development Goals: SDG 11 - Sustainable Cities and Communities
Electronic version(s): https://arxiv.org/abs/2002.05966 (Access: Open)
https://doi.org/10.1109/ITSC45102.2020.9294296 (Access: Closed)