arXiv subjects

Andreas A. Malikopoulos

Publications and source records attributed to Andreas A. Malikopoulos.

At least 127 records · Page 7Linked to original sources

arXiv2021

Time-Optimal Coordination for Connected and Automated Vehicles at Adjacent Intersections

In this paper, we provide a hierarchical coordination framework for connected and automated vehicles (CAVs) at two adjacent intersections. This framework consists of an upper-level scheduling problem and a low-level optimal control problem. By partitioning the area around two adjacent intersections into different zones, we formulate a scheduling problem for each individual CAV aimed at minimizing its total travel time. For each CAV, the solution of the upper-level problem designates the arrival times at each zones on its path which becomes the inputs of the low-level problem. The solution of the low-level problem yields the optimal control input (acceleration/deceleration) of each CAV to exit the intersections at the time specified in the upper-level scheduling problem. We validate the performance of our proposed hierarchical framework through extensive numerical simulations and comparison with signalized intersections, centralized scheduling, and FIFO queuing policy.

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arXiv2021

Socioeconomic Impact of Emerging Mobility Markets and Implementation Strategies

Emerging mobility systems such as connected and automated vehicles (CAVs) provide the most intriguing opportunity for more accessible, safe, and efficient transportation. CAVs are expected to significantly improve safety by eliminating the human factor and ensure transportation efficiency by allowing users to monitor transportation network conditions and make better operating decisions. However, CAVs could alter the users' tendency-to-travel, leading to a higher traffic demand than expected, thus causing rebound effects (e.g., increased vehicle-miles-traveled). In this chapter, we focus on tackling social factors that could drive an emerging mobility system to unsustainable congestion levels. We propose a mobility market that models the economic in-nature interactions of the travelers in a smart city network with roads and public transit infrastructure. Using techniques from mechanism design, we introduce appropriate monetary incentives (e.g., tolls, fares, fees) and show how a mobility system consisting of selfish travelers that seek to travel either with a CAV or use public transit can be socially efficient. Furthermore, the proposed mobility market ensures that travelers always report their true travel preferences and always benefit from participating in the market; lastly, we also show that the market generates enough revenue to potentially cover its operating costs.

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arXiv2021

Robust Learning-Based Trajectory Planning for Emerging Mobility Systems

In this paper, we extend a framework that we developed earlier for coordination of connected and automated vehicles (CAVs) at a signal-free intersection to incorporate uncertainty. Using the possibly noisy observations of actual time trajectories and leveraging Gaussian process regression, we learn the bounded confidence intervals for deviations from the nominal trajectories of CAVs online. Incorporating these confidence intervals, we reformulate the trajectory planning as a robust coordination problem, the solution of which guarantees that constraints in the system are satisfied in the presence of bounded deviations from the nominal trajectories. We demonstrate the effectiveness of our extended framework through a numerical simulation.

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arXiv2021

A Dynamic Program for a Team of Two Agents with Nested Information

In this paper, we investigate a sequential dynamic team problem consisting of two agents with a nested information structure. We use a combination of the person-by-person and prescription approach to derive structural results for optimal control strategies for the team. We then use these structural results to present a dynamic programming (DP) decomposition to derive the optimal control strategies for a finite time horizon. We show that our DP utilizes the nested information structure to simplify the computation of the optimal control laws for the team at the final time step.

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arXiv2021

A Scalable Last-Mile Delivery Service: From Simulation to Scaled Experiment

In this paper, we investigate the problem of a last-mile delivery service that selects up to $N$ available vehicles to deliver $M$ packages from a centralized depot to $M$ delivery locations. The objective of the last-mile delivery service is to jointly maximize customer satisfaction (minimize delivery time) and minimize operating cost (minimize total travel time) by selecting the optimal number of vehicles to perform the deliveries. We model this as an assignment (vehicles to packages) and path planning (determining the delivery order and route) problem, which is equivalent to the NP-hard multiple traveling salesperson problem. We propose a scalable heuristic algorithm, which sacrifices some optimality to achieve a reasonable computational cost for a high number of packages. The algorithm combines hierarchical clustering with a greedy search. To validate our approach, we compare the results of our simulation to experiments in a $1$:$25$ scale robotic testbed for future mobility systems.

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arXiv2021

Optimal Coordination of Platoons of Connected and Automated Vehicles at Signal-Free Intersections

In this paper, we address the problem of coordinating platoons of connected and automated vehicles at signal-free intersections. We present a decentralized, two-level optimal framework to coordinate the platoons with the objective to minimize travel delay and fuel consumption of every platoon crossing the intersection. In the upper-level, each platoon derives an optimal schedule to enter the intersection. In the low-level, the platoons derive their optimal control input (acceleration/deceleration) for the optimal schedule derived in the upper-level. We validate the effectiveness of the proposed framework in simulation and show significant improvements both in travel delay and fuel consumption compared to the baseline scenario with fixed time traffic signals at the intersection.

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arXiv2021

A Platoon Formation Framework in a Mixed Traffic Environment

Connected and automated vehicles (CAVs) provide the most intriguing opportunity to reduce pollution, energy consumption, and travel delays. In this paper, we address the problem of vehicle platoon formation in a traffic network with partial CAV penetration rates. We investigate the interaction between CAV and human-driven vehicle (HDV) dynamics, and provide a rigorous control framework that enables platoon formation with the HDVs by only controlling the CAVs within the network. We present a complete analytical solution of the CAV control policy and the conditions under which a platoon formation is feasible. We evaluate the solution and demonstrate the efficacy of the proposed framework using numerical simulation.

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arXiv2021

Optimal Control of Connected and Automated Vehicles at Multiple Adjacent Intersections

In this paper, we establish a decentralized optimal control framework for connected and automated vehicles (CAVs) crossing multiple adjacent, multi-lane signal-free intersections to minimize energy consumption and improve traffic throughput. Our framework consists of two layers of planning. In the upper-level planning, each CAV computes its optimal arrival time at each intersection recursively along with the optimal lane to improve the traffic throughput. In the low-level planning, we formulate an energy-optimal control problem with interior-point constraints, the solution of which yields the optimal control input (acceleration/deceleration) of each CAV to cross the intersections at the time specified by the upper-level planning. Moreover, we extend the results of the proposed bi-level framework to include a bounded steady-state error in tracking the optimal position of the CAVs. Finally, we demonstrate the effectiveness of the proposed framework through simulation for symmetric and asymmetric intersections and comparison with traditional signalized intersections.

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arXiv2021

Conditions to Provable System-Wide Optimal Coordination of Connected and Automated Vehicles

Connected and automated vehicles (CAVs) provide the most intriguing opportunity to improve energy efficiency, traffic flow, and safety. In earlier work, we addressed the constrained optimal coordination problem of CAVs at different traffic scenarios using Hamiltonian analysis. In this paper, we investigate the properties of the unconstrained problem and provide conditions under which different combination of the state and control constraints become active. We present a condition-based computational framework that improves on the standard iterative solution procedure of the constrained Hamiltonian analysis. Finally, we derive a closed-form analytical solution of the constrained optimal control problem and validate the proposed framework using numerical simulation. The solution can be derived without any recursive steps, and thus it is appropriate for real-time implementation on-board the CAVs.

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arXiv2021

Decentralized Cooperative Merging of Platoons of Connected and Automated Vehicles at Highway On-Ramps

In this paper, we propose an optimization framework for cooperative merging of platoons of connected and automated vehicles at highway on-ramps. The framework includes (1) an optimal scheduling algorithm, through which, each platoon derives the sequence and time to enter the highway safely, and (2) an optimal control algorithm that allows each platoon to derive its optimal control input (acceleration/deceleration) in terms of fuel consumption. We evaluate the efficacy of the proposed optimization framework through VISSIM-MATLAB simulation environment. The proposed framework significantly reduces the crossing time and fuel consumption of platoons at the highway on-ramps.

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arXiv2021

An Overview on Optimal Flocking

The study of robotic flocking has received considerable attention in the past twenty years. As we begin to deploy flocking control algorithms on physical multi-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance. In this paper, we present an overview the literature focusing on optimization approaches to achieve flocking behavior that provide strong safety guarantees. We separate the literature into cluster and line flocking, and categorize cluster flocking with respect to the system-level objective, which may be realized by a reactive or planning control algorithm. We also categorize the line flocking literature by the energy-saving mechanism that is exploited by the agents. We present several approaches aimed at minimizing the communication and computational requirements in real systems via neighbor filtering and event-driven planning, and conclude with our perspective on the outlook and future research direction of optimal flocking as a field.

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arXiv2021

Energy-Optimal Goal Assignment of Multi-Agent System with Goal Trajectories in Polynomials

In this paper, we propose an approach for solving an energy-optimal goal assignment problem to generate the desired formation in multi-agent systems. Each agent solves a decentralized optimization problem with only local information about its neighboring agents and the goals. The optimization problem consists of two sub-problems. The first problem seeks to minimize the energy for each agent to reach certain goals, while the second problem entreats an optimal combination of goal and agent pairs that minimizes the energy cost. By assuming the goal trajectories are given in a polynomial form, we prove the solution to the formulated problem exists globally. Finally, the effectiveness of the proposed approach is validated through the simulation.

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arXiv2021

Optimal Time Trajectory and Coordination for Connected and Automated Vehicles

In this paper, we provide a decentralized theoretical framework for coordination of connected and automated vehicles (CAVs) at different traffic scenarios. The framework includes: (1) an upper-level optimization that yields for each CAV its optimal time trajectory and lane to pass through a given traffic scenario while alleviating congestion; and (2) a low-level optimization that yields for each CAV its optimal control input (acceleration/deceleration). We provide a complete, analytical solution of the low-level optimization problem that includes the rear-end, speed-dependent safety constraint. Furthermore, we provide a problem formulation for the upper-level optimization in which there is no duality gap. The latter implies that the optimal time trajectory for each CAV does not activate any of the state, control, and safety constraints of the low-level optimization, thus allowing for online implementation. Finally, we present a geometric duality framework with hyperplanes to derive the condition under which the optimal solution of the upper-level optimization always exists. We validate the effectiveness of the proposed theoretical framework through simulation.

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arXiv2021

Social Media and Misleading Information in a Democracy: A Mechanism Design Approach

In this paper, we present a resource allocation mechanism for the problem of incentivizing filtering among a finite number of strategic social media platforms. We consider the presence of a strategic government and private knowledge of how misinformation affects the users of the social media platforms. Our proposed mechanism incentivizes social media platforms to filter misleading information efficiently, and thus indirectly prevents the spread of fake news. In particular, we design an economically inspired mechanism that strongly implements all generalized Nash equilibria for efficient filtering of misleading information in the induced game. We show that our mechanism is individually rational, budget balanced, while it has at least one equilibrium. Finally, we show that for quasi-concave utilities and constraints, our mechanism admits a generalized Nash equilibrium and implements a Pareto efficient solution.

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arXiv2020

A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities

Connected and automated vehicles (CAVs) can alleviate traffic congestion, air pollution, and improve safety. In this paper, we provide a decentralized coordination framework for CAVs at a signal-free intersection to minimize travel time and improve fuel efficiency. We employ a simple yet powerful reinforcement learning approach, an off-policy temporal difference learning called Q-learning, enhanced with a coordination mechanism to address this problem. Then, we integrate a first-in-first-out queuing policy to improve the performance of our system. We demonstrate the efficacy of our proposed approach through simulation and comparison with the classical optimal control method based on Pontryagin's minimum principle.

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arXiv2020

Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems

Shared mobility can provide access to transportation on a custom basis without vehicle ownership. The advent of connected and automated vehicle technologies can further enhance the potential benefits of shared mobility systems. Although the implications of a system with shared autonomous vehicles have been investigated, the research reported in the literature has exhibited contradictory outcomes. In this paper, we present a summary of the research efforts in shared autonomous vehicle systems that have been reported in the literature to date and discuss potential future research directions.

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arXiv2020

Energy-Optimal Motion Planning for Agents: Barycentric Motion and Collision Avoidance Constraints

As robotic swarm systems emerge, it is increasingly important to provide strong guarantees on energy consumption and safety to maximize system performance. One approach to achieve these guarantees is through constraint-driven control, where agents seek to minimize energy consumption subject to a set of safety and task constraints. In this paper, we provide a sufficient and necessary condition for an energy-minimizing agent with integrator dynamics to have a continuous control input at the transition between unconstrained and constrained trajectories. In addition, we present and analyze barycentric motion and collision avoidance constraints to be used in constraint-driven control of swarms.

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arXiv2020

Social Resource Allocation in a Mobility System with Connected and Automated Vehicles: A Mechanism Design Problem

In this paper, we investigate the social resource allocation in an emerging mobility system consisting of connected and automated vehicles (CAVs) using mechanism design. CAVs provide the most intriguing opportunity for enabling travelers to monitor mobility system conditions efficiently and make better decisions. However, this new reality will influence travelers' tendency-of-travel and might give rise to rebound effects, e.g., increased-vehicle-miles traveled. To tackle this phenomenon, we propose a mechanism design formulation that provides an efficient social resource allocation of travel time for all travelers. Our focus is on the socio-technical aspect of the problem, i.e., by designing appropriate socio-economic incentives, we seek to prevent potential rebound effects. In particular, we propose an economically inspired mechanism to influence the impact of the travelers' decision-making on the well-being of an emerging mobility system.

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