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

2025/06/10 by Yash Ranjan, Rahul Sengupta, Ranjan, Yash +4
Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques #Traffic control and management

paper · pdf · doi:10.48550/arxiv.2506.08957

openalex publication_date 2025/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics
• Data-driven Traffic Simulation: A Comprehensive Review
• Motion Transformer with Global Intention Localization and Local Movement Refinement
• DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
• TNT: Target-driveN Trajectory Prediction
• VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
• Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
• Deep Gaussian Mixture Models
• Deep Gaussian mixture models
• InfoVAE: Information Maximizing Variational Autoencoders
• An Introduction to Convolutional Neural Networks
• Generating Sequences With Recurrent Neural Networks

Related

• Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics
• KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections
• SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections
• TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
• Data-Driven Traffic Simulation for an Intersection in a Metropolis