# Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model

## TL;DR

This paper introduces an interpretable, multi-modal probabilistic model for predicting the joint future motions of interacting agents in complex scenarios, enhancing autonomous systems' safety and reliability.

## Contribution

It presents a novel generative, interpretable model that jointly predicts multi-agent behaviors over time, addressing the challenge of coupled interactions and uncertainty in long-term predictions.

## Key findings

- Effective in complex roundabout scenarios
- Joint multi-agent prediction improves accuracy
- Model provides interpretable reasoning for predictions

## Abstract

For autonomous agents to successfully operate in real world, the ability to anticipate future motions of surrounding entities in the scene can greatly enhance their safety levels since potentially dangerous situations could be avoided in advance. While impressive results have been shown on predicting each agent's behavior independently, we argue that it is not valid to consider road entities individually since transitions of vehicle states are highly coupled. Moreover, as the predicted horizon becomes longer, modeling prediction uncertainties and multi-modal distributions over future sequences will turn into a more challenging task. In this paper, we address this challenge by presenting a multi-modal probabilistic prediction approach. The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents. Most importantly, our model is interpretable, which can explain the underneath logic as well as obtain more reliability to use in real applications. A complicate real-world roundabout scenario is utilized to implement and examine the proposed method.