Multi-Fidelity Recursive Behavior Prediction

Mihir Jain, Kyle Brown, Ahmed Sadek

Abstract: Predicting the behavior of surrounding vehicles is a critical problem in automated driving. We present a novel game theoretic behavior prediction model that achieves state of the art prediction accuracy by explicitly reasoning about possible future interaction between agents. We evaluate our approach on the NGSIM vehicle trajectory data set and demonstrate lower root mean square error than state-of-the art methods.

Keywords: Behavior prediction, CNN, LSTM, vehicle trajectory prediction, recursive reasoning

TL;DR: We present a novel behavior prediction model, Multi-Fidelity Recursive Behavior Prediction (MFRBP), that improves the vehicle trajectory prediction state of the art by explicitly reasoning about possible future interaction between agents.