  In order to drive safely on the road, autonomous vehicle is expected to
predict future outcomes of its surrounding environment and react properly. In
fact, many researchers have been focused on solving behavioral prediction
problems for autonomous vehicles. However, very few of them consider
multi-agent prediction under challenging driving scenarios such as urban
environment. In this paper, we proposed a prediction method that is able to
predict various complicated driving scenarios where heterogeneous road
entities, signal lights, and static map information are taken into account.
Moreover, the proposed multi-agent interactive prediction (MAIP) system is
capable of simultaneously predicting any number of road entities while
considering their mutual interactions. A case study of a simulated challenging
urban intersection scenario is provided to demonstrate the performance and
capability of the proposed prediction system.
