TL;DR
A Conflict-Aware Heterogeneous Graph-based Reinforcement Learning (HGRL) Framework for decentralized intersection management under mixed traffic flows enables CAVs to make adaptive and coordinated decisions while explicitly accounting for conflict risks.
Results indicate that the proposed V2I2V cooperative system provides robust and scalable performance at smart intersections by using digital twins deployed on roadside units to eliminate blind spots and centrally coordinate connected and automated vehicles in smart intersections.
A demand-driven signal control strategy is developed to allocate green time based on real-time vehicle demand, eliminating wasted signal phases and providing a scalable and intelligent solution for modern smart city traffic systems.
UrbanMix is presented, an interactive simulation platform that embeds cognitively diverse agents (human drivers, cautious AVs, aggressive AVs, and delivery robots) with distinct behavioral profiles inside a Simulation of Urban MObility (SUMO) framework of real urban road networks.
A novel cooperative MARL-based approach for adaptive traffic signal control in multi-intersection networks that significantly outperforms existing methods in relation to average pheromone intensity, average noise emission, and average waiting time is proposed.
Simulation experiments demonstrate that the proposed joint optimization model effectively reduces delays across most movements even at low CAV penetration rates, and as the CAV penetration rate increases, consistent and more pronounced reductions in both delay and energy consumption are observed for all movements.
An agent-based modeling framework coupling a physical infrastructure layer, a connectivity layer (V2X), and a decision layer integrating reinforcement learning and multi-agent reinforcement learning for platoon formation and charging coordination for platoon formation and charging coordination is presented.
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