# Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics

arXiv: https://arxiv.org/abs/2506.08963 · HTML: https://mlsanity.com/paper/2506.08963/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [InfoVAE: Information Maximizing Variational Autoencoders](https://mlsanity.com/paper/1706.02262/) (2017)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Conditional Variational Autoencoder for Neural Machine Translation](https://mlsanity.com/paper/1812.04405/) (2018)
- [CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving](https://mlsanity.com/paper/2310.07794/) (2023)
- [A survey on robustness in trajectory prediction for autonomous vehicles](https://mlsanity.com/paper/2402.01397/) (2024)

## Built on by (most cited first, 3 of 3)

- [Enactor: From Traffic Simulators to Surrogate World Models](https://mlsanity.com/paper/2603.18266/) (2026)
- [A Generative Model for Closed-Loop Microsimulation of Signalized Intersections](https://mlsanity.com/paper/2606.23588/) (2026)
- [PhaseShift: Topology-Aware Data Harmonization and Model Consolidation Across Signalized Intersections](https://mlsanity.com/paper/2608.25275/) (2026)

## Abstract (arXiv)

Traffic Intersections are vital to urban road networks as they regulate the movement of people and goods. However, they are regions of conflicting trajectories and are prone to accidents. Deep Generative models of traffic dynamics at signalized intersections can greatly help traffic authorities better understand the efficiency and safety aspects. At present, models are evaluated on computational metrics that primarily look at trajectory reconstruction errors. They are not evaluated online in a `live' microsimulation scenario. Further, these metrics do not adequately consider traffic engineering-specific concerns such as red-light violations, unallowed stoppage, etc. In this work, we provide a comprehensive analytics tool to train, run, and evaluate models with metrics that give better insights into model performance from a traffic engineering point of view. We train a state-of-the-art multi-vehicle trajectory forecasting model on a large dataset collected by running a calibrated scenario of a real-world urban intersection. We then evaluate the performance of the prediction models, online in a microsimulator, under unseen traffic conditions. We show that despite using ideally-behaved trajectories as input, and achieving low trajectory reconstruction errors, the generated trajectories show behaviors that break traffic rules. We introduce new metrics to evaluate such undesired behaviors and present our results.