IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections

• URL: http://arxiv.org/abs/2506.08957v1
• Date: Tue, 10 Jun 2025 16:27:42 GMT
• Title: IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections
• Authors: Yash Ranjan, Rahul Sengupta, Anand Rangarajan, Sanjay Ranka,
• Abstract summary: Traffic simulators are widely used to study the operational efficiency of road infrastructure.<n>Their rule-based approach limits their ability to mimic real-world driving behavior.<n>We propose traffic engineering-related metrics to evaluate generative trajectory prediction models.
• Score: 8.484294935626224
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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