Multi-agent Interactive Prediction under Challenging Driving Scenarios

TL;DR

This paper introduces a multi-agent interactive prediction system for autonomous vehicles that effectively predicts complex urban driving scenarios by considering multiple entities, signals, and static map data, enhancing safety and decision-making.

Contribution

The paper presents a novel multi-agent interactive prediction method capable of handling heterogeneous entities and their interactions in challenging urban environments.

Findings

Successfully predicts complex urban scenarios

Handles multiple entities simultaneously

Demonstrates effectiveness in simulated intersection case

Abstract

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.