arXiv subjects

Andreas A. Malikopoulos

Publications and source records attributed to Andreas A. Malikopoulos.

At least 55 records · Page 3Linked to original sources

arXiv2025

Deviation Between Team-Optimal Solution and Nash Equilibrium in Flow Assignment Problems

We investigate the relationship between the team-optimal solution and the Nash equilibrium (NE) to assess the impact of strategy deviation on team performance. As a working use case, we focus on a class of flow assignment problems in which each source node acts as a cooperating decision maker (DM) within a team that minimizes the team cost based on the team-optimal strategy. In practice, some selfish DMs may prioritize their own marginal cost and deviate from NE strategies, thus potentially degrading the overall performance. To quantify this deviation, we explore the deviation bound between the team-optimal solution and the NE in two specific scenarios: (i) when the team-optimal solution is unique and (ii) when multiple solutions do exist. This helps DMs analyze the factors influencing the deviation and adopting the NE strategy within a tolerable range. Furthermore, in the special case of a potential game model, we establish the consistency between the team-optimal solution and the NE. Once the consistency condition is satisfied, the strategy deviation does not alter the total cost, and DMs do not face a strategic trade-off. Finally, we validate our theoretical analysis through some simulation studies.

cs.GT↗

arXiv2025

Routing Guidance for Emerging Transportation Systems with Improved Dynamic Trip Equity

This paper presents a dynamic routing guidance system that optimizes route recommendations for individual vehicles in an emerging transportation system while enhancing travelers' trip equity. We develop a framework to quantify trip quality and equity in dynamic travel environments, providing new insights into how routing guidance influences equity in road transportation. Our approach enables real-time routing by incorporating both monitored and anticipated traffic congestion. We provide conditions that ensure perfect trip equity for all travelers in a free-flow network. Simulation studies on 1,000 vehicles traversing an urban road network in Boston demonstrate that our method improves trip equity by approximately 11.4\% compared to the shortest-route strategy. In addition, the results reveal that our approach redistributes travel costs across vehicle types through route optimization, contributing to a more equitable transportation system.

eess.SY↗

arXiv2025

Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding

In this paper, we expand the Bayesian persuasion framework to account for unobserved confounding variables in sender-receiver interactions. While traditional models assume that belief updates follow Bayesian principles, real-world scenarios often involve hidden variables that impact the receiver's belief formation and decision-making. We conceptualize this as a sequential decision-making problem, where the sender and receiver interact over multiple rounds. In each round, the sender communicates with the receiver, who also interacts with the environment. Crucially, the receiver's belief update is affected by an unobserved confounding variable. By reformulating this scenario as a Partially Observable Markov Decision Process (POMDP), we capture the sender's incomplete information regarding both the dynamics of the receiver's beliefs and the unobserved confounder. We prove that finding an optimal observation-based policy in this POMDP is equivalent to solving for an optimal signaling strategy in the original persuasion framework. Furthermore, we demonstrate how this reformulation facilitates the application of proximal learning for off-policy evaluation in the persuasion process. This advancement enables the sender to evaluate alternative signaling strategies using only observational data from a behavioral policy, thus eliminating the necessity for costly new experiments.

cs.AI↗

arXiv2025

A United Framework for Planning Electric Vehicle Charging Accessibility

The shift towards electric vehicles (EVs) is crucial for establishing sustainable and low-emission urban transportation systems. However, the success of this transition depends on the strategic placement of the charging infrastructure. This paper addresses the challenge of optimizing charging station locations in dense urban environments while balancing efficiency with spatial accessibility. We propose an optimization framework that integrates traffic simulation, energy consumption modeling, and a mobility equity measure to evaluate the social reach of each potential charging station. Using New York City as a case study, we demonstrate consistent improvements in accessibility (15-20% reduction in travel time variability). Our results provide a scalable methodology for incorporating equity considerations into EV infrastructure planning, although economic factors and grid integration remain important areas for future development.

eess.SY↗

arXiv2025

Robust Accelerated Dynamics for Subnetwork Bilinear Zero-Sum Games with Distributed Restarting

In this paper, we investigate distributed Nash equilibrium seeking for a class of two-subnetwork zero-sum games characterized by bilinear coupling. We present a distributed primal-dual accelerated mirror-descent algorithm with convergence guarantees. However, we demonstrate that this time-varying algorithm is not robust, as it fails to converge under arbitrarily small disturbances. To address this limitation, we introduce a distributed accelerated algorithm that incorporates a coordinated restarting mechanism. We model this new algorithm as a hybrid dynamical system and establish its structural robustness.

math.OC↗

arXiv2025

Stable and Fair Benefit Allocation in Mixed-Energy Truck Platooning: A Coalitional Game Approach

This paper addresses the benefit allocation in a mixed-energy truck platoon composed of fuel-powered and electric trucks. The interactions among trucks during platoon formation are modeled as a coalitional game with transferable utility. We first design a stable payoff allocation scheme that accounts for truck heterogeneity in energy savings and platoon roles (leader or follower), establishing core-stability conditions to ensure that no subset of trucks has an incentive to deviate for greater benefit. To enhance payoff fairness, we then propose a closed-form, Shapley value-based allocation approach that is computationally efficient and independent of the platoon size. Sufficient conditions under which the allocation is both fair and core-stable are provided. In scenarios where the Shapley value falls outside the core, we develop an alternative allocation based on the stable payoff that minimizes the mean relative deviation from the Shapley value while preserving core stability. This deviation is further proved to be upper-bounded by $1$, showing a favorable trade-off between stability and fairness. Finally, extensive numerical studies validate the theoretical results and demonstrate the effectiveness of the proposed framework in facilitating stable, equitable, and sustainable cooperation in mixed-energy truck platooning.

eess.SY↗

arXiv2025

Platoon Coordination and Leader Selection in Mixed Transportation Systems via Dynamic Programming

With the growing penetration of electric trucks, freight transportation is transitioning toward a mixed system comprising both fuel-powered and electric trucks. Enhancing truck platoon formation in such a heterogeneous environment presents new challenges. This paper investigates the hub-based platoon coordination problem in a mixed truck fleet, where the focus is to optimize the trucks' waiting times, charging amounts for electric trucks, and platoon leader assignments. The objective is to maximize the overall platoon revenue of the fleet while accounting for the associated waiting and charging costs. We formulate the problem as a mixed-integer linear program and present a dynamic programming approach to compute its sub-optimal solution efficiently. The proposed method operates in polynomial time, ensuring scalable computational efficiency. Simulation studies involving 1,000 trucks traveling between two hubs in Sweden demonstrate the effectiveness and scalability of the proposed approach.

math.OC↗

arXiv2025

VisioPath: Vision-Language Enhanced Model Predictive Control for Safe Autonomous Navigation in Mixed Traffic

In this paper, we introduce VisioPath, a novel framework combining vision-language models (VLMs) with model predictive control (MPC) to enable safe autonomous driving in dynamic traffic environments. The proposed approach leverages a bird's-eye view video processing pipeline and zero-shot VLM capabilities to obtain structured information about surrounding vehicles, including their positions, dimensions, and velocities. Using this rich perception output, we construct elliptical collision-avoidance potential fields around other traffic participants, which are seamlessly integrated into a finite-horizon optimal control problem for trajectory planning. The resulting trajectory optimization is solved via differential dynamic programming with an adaptive regularization scheme and is embedded in an event-triggered MPC loop. To ensure collision-free motion, a safety verification layer is incorporated in the framework that provides an assessment of potential unsafe trajectories. Extensive simulations in Simulation of Urban Mobility (SUMO) demonstrate that VisioPath outperforms conventional MPC baselines across multiple metrics. By combining modern AI-driven perception with the rigorous foundation of optimal control, VisioPath represents a significant step forward in safe trajectory planning for complex traffic systems.

eess.SY↗

arXiv2025

Teaching Cars to Drive: Spotlight on Connected and Automated Vehicles

In recent decades, society has witnessed significant advancements in emerging mobility systems. These systems refer to transportation solutions that incorporate digital technologies, automation, connectivity, and sustainability to create safer, more efficient, and user-centered mobility. Examples include connected and automated vehicles (CAVs), shared mobility services (car-pooling), electric vehicles, and mobility-as-a-service platforms. These innovations have the potential to greatly impact areas such as safety, pollution, comfort, travel time, and fairness. In this article, we explore the current landscape of CAVs. We discuss their role in daily life and their future potential, while also addressing the challenges they may introduce. Following, we also examine the practical difficulties in research associated with CAVs especially simulating and testing CAV-related algorithms in real-world settings. We present existing solutions that aim to overcome these limitations. Finally, we provide an accessible introduction to modeling CAVs using basic kinematic principles and offer an open-source tutorial to help interested students begin exploring the field.

eess.SY↗

arXiv2025

Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization

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 class of control design problems for dynamical systems operating in different environments or conditions represented by contextual parameters. The overarching goal is to identify the controller parameters that maximize the controlled system's performance, given different realizations of the contextual parameters.We formulate a contextual Bayesian optimization problem in which the solution is actively learned using Gaussian processes to approximate the controller adaptation strategy. We demonstrate the efficacy of the proposed framework with a sim-to-real example. We learn the optimal weighting strategy of a model predictive control for connected and automated vehicles interacting with human-driven vehicles from simulations and then deploy it in a real-time experiment.

eess.SY↗

arXiv2025

On the Robustness of Derivative-free Methods for Linear Quadratic Regulator

Policy optimization has drawn increasing attention in reinforcement learning, particularly in the context of derivative-free methods for linear quadratic regulator (LQR) problems with unknown dynamics. This paper focuses on characterizing the robustness of derivative-free methods for solving an infinite-horizon LQR problem. To be specific, we estimate policy gradients by cost values, and study the effect of perturbations on the estimations, where the perturbations may arise from function approximations, measurement noises, etc. We show that under sufficiently small perturbations, the derivative-free methods converge to any pre-specified neighborhood of the optimal policy. Furthermore, we establish explicit bounds on the perturbations, and provide the sample complexity for the perturbed derivative-free methods.

math.OC↗

arXiv2025

Handling Pedestrian Uncertainty in Coordinating Autonomous Vehicles at Signal-Free Intersections

In this paper, we provide a theoretical framework for the coordination of connected and automated vehicles (CAVs) at signal-free intersections, accounting for the unexpected presence of pedestrians. First, we introduce a general vehicle-to-infrastructure communication protocol and a low-level controller that determines the optimal unconstrained trajectories for CAVs, in terms of fuel efficiency and travel time, to cross the intersection without considering pedestrians. If such an unconstrained trajectory is unattainable, we introduce sufficient certificates for each CAV to cross the intersection while respecting the associated constraints. Next, we consider the case where an unexpected pedestrian enters the road. When the CAV's sensors detect a pedestrian, an emergency mode is activated, which imposes certificates related to an unsafe set in the pedestrian's proximity area. Simultaneously, a re-planning mechanism is implemented for all CAVs to accommodate the trajectories of vehicles operating in emergency mode. Finally, we validate the efficacy of our approach through simulations conducted in MATLAB and RoadRunner softwares, which facilitate the integration of sensor tools and the realization of real-time implementation.

eess.SY↗

arXiv2025

AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning

In this paper, we present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environment to enhance a single vehicle's travel efficiency. The problem is framed within a Markov decision process setting and is addressed through an adherence-aware deep Q network, which takes into account the partial compliance of human drivers with the recommended actions. This approach is evaluated within CARLA's driving environment under realistic scenarios.

cs.LG↗

arXiv2025

Safe and Efficient Coexistence of Autonomous Vehicles with Human-Driven Traffic at Signalized Intersections

The proliferation of connected and automated vehicles (CAVs) has positioned mixed traffic environments, which encompass both CAVs and human driven vehicles (HDVs), as critical components of emerging mobility systems. Signalized intersections are paramount for optimizing transportation efficiency and enhancing energy economy, as they inherently induce stop and go traffic dynamics. In this paper, we present an integrated framework that concurrently optimizes signal timing and CAV trajectories at signalized intersections, with the dual objectives of maximizing traffic throughput and minimizing energy consumption for CAVs. We first formulate an optimal control strategy for CAVs that prioritizes trajectory planning to circumvent state constraints, while incorporating the impact of signal timing and HDV behavior. Furthermore, we introduce a traffic signal control methodology that dynamically adjusts signal phases based on vehicular density per lane, while mitigating disruption for CAVs scheduled to traverse the intersection. Acknowledging the system's inherent dynamism, we also explore event triggered replanning mechanisms that enable CAVs to iteratively refine their planned trajectories in response to the emergence of more efficient routing options. The efficacy of our proposed framework is evaluated through comprehensive simulations in MATLAB.

eess.SY↗

arXiv2025

Distributed Mixed-Integer Quadratic Programming for Mixed-Traffic Intersection Control

In this paper, we present a distributed algorithm utilizing the proximal alternating direction method of multipliers (ADMM) in conjunction with sequential constraint tightening to address mixed-integer quadratic programming (MIQP) problems associated with traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We formulate a comprehensive MIQP model aimed at optimizing the coordination of traffic light systems and CAVs, thereby fully capitalizing on the advantages of CAV integration under conditions of high penetration rates. To effectively approximate the intricate multi-agent MIQP challenges, we develop a distributed algorithm that employs proximal ADMM for solving the convex relaxation of the MIQP while systematically tightening the constraint coefficients to uphold integrality requirements. The performance of our control framework and the efficacy of the distributed algorithm are rigorously validated through a series of simulations conducted across varying penetration rates and traffic volumes.

eess.SY↗

arXiv2025

A Cooperative Compliance Control Framework for Socially Optimal Mixed Traffic Routing

In mixed traffic environments, where Connected and Autonomed Vehicles (CAVs) coexist with potentially non-cooperative Human-Driven Vehicles (HDVs), the self-centered behavior of human drivers may compromise the efficiency, optimality, and safety of the overall traffic network. In this paper, we propose a Cooperative Compliance Control (CCC) framework for mixed traffic routing, where a Social Planner (SP) optimizes vehicle routes for system-wide optimality while a compliance controller incentivizes human drivers to align their behavior with route guidance from the SP through a "refundable toll" scheme. A key challenge arises from the heterogeneous and unknown response models of different human driver types to these tolls, making it difficult to design a proper controller and achieve desired compliance probabilities over the traffic network. To address this challenge, we employ Control Lyapunov Functions (CLFs) to adaptively correct (learn) crucial components of our compliance probability model online, construct data-driven feedback controllers, and demonstrate that we can achieve the desired compliance probability for HDVs, thereby contributing to the social optimality of the traffic network.

eess.SY↗

arXiv2025

Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming

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 formulate a mixed-integer optimization problem for receding horizon motion planning of a multi-robot system, taking into account the surrounding obstacles. To address the resulting multi-agent MICP problem in real time, we propose a framework that utilizes heterogeneous graph attention networks to learn the latent mapping from problem parameters to optimal binary solutions. Furthermore, we apply a distributed proximal alternating direction method of multipliers algorithm for solving the convex continuous optimization problem. We demonstrate the effectiveness of our proposed framework through experiments conducted on a robotic testbed.

eess.SY↗

arXiv2025

Combining Cooperative Re-Routing with Intersection Coordination for Connected and Automated Vehicles in Urban Networks

In this paper, we present a hierarchical framework that integrates upper-level routing with low-level optimal trajectory planning for connected and automated vehicles (CAVs) traveling in an urban network. The upper-level controller efficiently distributes traffic flows by utilizing a dynamic re-routing algorithm that leverages real-time density information and the fundamental diagrams of each network edge. This re-routing approach predicts when each edge will reach critical density and proactively adjusts the routing algorithm's weights to prevent congestion before it occurs. The low-level controller coordinates CAVs as they cross signal-free intersections, generating optimal, fuel-efficient trajectories while ensuring safe passage by satisfying all relevant constraints. We formulate the problem as an optimal control problem and derive an analytical solution. Using the SUMO micro-simulation platform, we conduct simulation experiments on a realistic network. The results show that our hierarchical framework significantly enhances network performance compared to a baseline static routing approach. By dynamically re-routing vehicles, our approach successfully reduces total travel time and mitigates congestion before it develops.

eess.SY↗