IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections
Abstract
Traffic simulators are widely used to study the operational efficiency of road infrastructure, but their rule-based approach limits their ability to mimic real-world driving behavior. Traffic intersections are critical components of the road infrastructure, both in terms of safety risk (nearly 28% of fatal crashes and 58% of nonfatal crashes happen at intersections) as well as the operational efficiency of a road corridor. This raises an important question: can we create a data-driven simulator that can mimic the macro- and micro-statistics of the driving behavior at a traffic intersection? Deep Generative Modeling-based trajectory prediction models provide a good starting point to model the complex dynamics of vehicles at an intersection. But they are not tested in a "live" micro-simulation scenario and are not evaluated on traffic engineering-related metrics. In this study, we propose traffic engineering-related metrics to evaluate generative trajectory prediction models and provide a simulation-in-the-loop pipeline to do so. We also provide a multi-headed self-attention-based trajectory prediction model that incorporates the signal information, which outperforms our previous models on the evaluation metrics.
Cite
@article{arxiv.2506.08957,
title = {IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections},
author = {Yash Ranjan and Rahul Sengupta and Anand Rangarajan and Sanjay Ranka},
journal= {arXiv preprint arXiv:2506.08957},
year = {2025}
}