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

arXiv: https://arxiv.org/abs/1903.09381 · HTML: https://mlsanity.com/paper/1903.09381/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2019.

## Built on by (most cited first, 13 of 13)

- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02104/) (2024)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction](https://mlsanity.com/paper/2211.08701/) (2022)
- [Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space](https://mlsanity.com/paper/2103.13726/) (2021)
- [Causal-based Time Series Domain Generalization for Vehicle Intention Prediction](https://mlsanity.com/paper/2112.02093/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling](https://mlsanity.com/paper/2112.00298/) (2021)
- [Maneuver-Aware Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2104.14079/) (2021)
- [Disentangled Sequence Clustering for Human Intention Inference](https://mlsanity.com/paper/2101.09500/) (2021)
- [Maneuver-based Anchor Trajectory Hypotheses at Roundabouts](https://mlsanity.com/paper/2104.11180/) (2021)
- [Dynamic Inference](https://mlsanity.com/paper/2111.14746/) (2021)

## Abstract (arXiv)

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.