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Open Access

ARTICLE

Intention Prediction-Based Automated Vehicle Control Mechanism Using Social-Pooling LSTM and Pass-Through Time Window Optimization

Donghee Oh1, Chris Lee2, Juneyoung Park1,3,*
1 Department of Smart City Engineering, Hanyang University, Ansan, Republic of Korea
2 Department of Civil & Environmental Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON, Canada
3 Department of Transportation and Logistics Engineering, Hanyang University, Ansan, Republic of Korea
* Corresponding Author: Juneyoung Park. Email: email
(This article belongs to the Special Issue: AI-Driven Big Data Analytics for Sustainable Mixed Traffic and Mobility Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076994

Received 30 November 2025; Accepted 01 April 2026; Published online 20 April 2026

Abstract

This study presents a novel integrated framework for autonomous vehicle control at unsignalized intersections in mixed traffic environments, addressing the critical challenge of coordinating Society of Automotive Engineers (SAE) level 4 connected and autonomous vehicles (CAVs) and manually driven vehicles (MVs). The combination of driving intention prediction with a Social Long Short-Term Memory (Social LSTM) and a scheduling algorithm with optimization-driven Pass-through Time Windows (PTWs) is adopted to address traffic flow uncertainty. The Social LSTM model with spatial pooling layers to capture complex multi-vehicle interactions and predict surrounding vehicles’ trajectories and maneuver intentions using naturalistic driving data from the CitySim dataset was applied. Unlike conventional approaches that treat prediction and control separately, this framework leverages high-confidence trajectory predictions to inform proactive scheduling decisions for conflict mitigation. The PTW scheduling algorithm formulates intersection management as a constrained optimization problem, dynamically allocating non-overlapping temporal windows for vehicle entering and exiting while considering vehicle dynamics, safety gaps, and deceleration constraints. Comprehensive simulation analysis across varying traffic volumes and CAV market penetration rates reveals significant improvements in both safety and operational efficiency. The scheduling algorithm has notably reduced traffic delay times while maintaining balance with safety measures. This finding provides a fundamental basis for infrastructure-based cooperative driving research, serving as a contributing factor for the development of advanced traffic management systems during the mixed-traffic period.

Keywords

Scheduling algorithm; linear optimization; trajectory prediction; deep learning; social LSTM model; connected and autonomous vehicle

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