14 citations · 17 across the 9 of their papers we have counts for
6 papers · 1 filter
Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks
Viet-Anh Le, Andreas A. Malikopoulos
In this paper, we present a learning-based framework that accelerates time- and energy-optimal trajectory planning for connected and automated vehicles (CAVs) using graph neural ne…
Distributed Mixed-Integer Quadratic Programming for Mixed-Traffic Intersection Control
Viet-Anh Le, Andreas A. Malikopoulos
In this paper, we develop a distributed solution framework for coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We formula…
Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming
Viet-Anh Le, Panagiotis Kounatidis, Andreas A. Malikopoulos
In this paper, we develop a fast mixed-integer convex programming (MICP) framework for multi-robot navigation by combining graph attention networks and distributed optimization. We…
Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections
Viet-Anh Le, Andreas A. Malikopoulos
In this paper, we consider the problem of coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim to develop an optimizati…
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization
Viet-Anh Le, Andreas A. Malikopoulos
In this work, we propose a framework for adapting the controller's parameters based on learning optimal solutions from contextual black-box optimization problems. We consider a cla…
Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios
Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou +1
Addressing safe and efficient interaction between connected and automated vehicles (CAVs) and human-driven vehicles in a mixed-traffic environment has attracted considerable attent…