Multi-agent Interactive Prediction under Challenging Driving Scenarios
This addresses the problem of safe autonomous vehicle navigation in complex urban environments, representing an incremental improvement by extending prediction to multi-agent interactions.
The paper tackles multi-agent prediction in challenging urban driving scenarios by proposing a system that accounts for heterogeneous road entities, traffic signals, and map information, and demonstrates its performance in a simulated intersection case study.
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.