# Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks

arXiv: https://arxiv.org/abs/1912.07882 · HTML: https://mlsanity.com/paper/1912.07882/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction](https://mlsanity.com/paper/2202.11884/) (2022)
- [FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs](https://mlsanity.com/paper/2211.16197/) (2022)
- [Data-driven Traffic Simulation: A Comprehensive Review](https://mlsanity.com/paper/2310.15975/) (2023)
- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference](https://mlsanity.com/paper/2209.11904/) (2022)
- [Physically Feasible Vehicle Trajectory Prediction](https://mlsanity.com/paper/2104.14679/) (2021)
- [Disentangled Neural Relational Inference for Interpretable Motion Prediction](https://mlsanity.com/paper/2401.03599/) (2024)
- [Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving](https://mlsanity.com/paper/2102.11905/) (2021)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search](https://mlsanity.com/paper/2108.01036/) (2021)
- [Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?](https://mlsanity.com/paper/2206.11110/) (2022)
- [DIDER: Discovering Interpretable Dynamically Evolving Relations](https://mlsanity.com/paper/2208.10592/) (2022)
- [Multi-modal anticipation of stochastic trajectories in a dynamic environment with Conditional Variational Autoencoders](https://mlsanity.com/paper/2103.03912/) (2021)
- [Critical Example Mining for Vehicle Trajectory Prediction using Flow-based Generative Models](https://mlsanity.com/paper/2410.16083/) (2024)
- [Choose Your Game Wisely: Measuring Game-Theoretic Structures in Real-World Vehicle Interactions](https://mlsanity.com/paper/2608.25917/) (2026)

## Abstract (arXiv)

In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions. Specifically, we propose a graph neural network that jointly predicts the discrete interaction modes and 5-second future trajectories for all agents in the scene. Our model infers an interaction graph whose nodes are agents and whose edges capture the long-term interaction intents among the agents. In order to train the model to recognize known modes of interaction, we introduce an auto-labeling function to generate ground truth interaction labels. Using a large-scale real-world driving dataset, we demonstrate that jointly predicting the trajectories along with the explicit interaction types leads to significantly lower trajectory error than baseline methods. Finally, we show through simulation studies that the learned interaction modes are semantically meaningful.