Distributed Model Predictive Intersection Control of Multiple Vehicles

Kloock, Maximilian; Scheffe, Patrick; Marquardt, Sascha; Maczijewski, Janis; Alrifaee, Bassam; Kowalewski, Stefan

doi:10.1109/itsc.2019.8917117

Cited by 39 publications

(30 citation statements)

References 19 publications

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“…Additionally, in [187] a cooperative optimization algorithm was used for an optimal safe crossing order of the vehicles with minimal travel time. Kloock et al [188] analyzed the safe and efficient intersection management using distributed model predictive control (DMPC) scheme. Vehicles followed predefined routes and priorities were applied to them based on their reaction times to stop before the intersection in case they could not pass the intersection with the adjusted speed.…”

Section: Safety and Efficiencymentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.INDEX TERMS Vehicular ad-hoc networks (VANETs), intersection management (IM), vehicle-tovehicle (V2V), vehicle-to-infrastructure (V2I), trajectory planning (TP), spatio-temporal (ST), connected autonomous vehicles (CAVs), vulnerable road users (VRUs), connected vehicles (CVs), autonomous vehicle (AVs). II. INTERSECTION MANAGEMENT (IM) FUNDAMENTALS A. ARCHITECTURE ALTERNATIVESASHKAN GHOLAMHOSSEINIAN (Graduate Student Member, IEEE) received the bachelor's degree in computer engineering-software in Iran, the M.Sc. degree in computer network engineering from Halmstad University, Sweden, and the Post master's degree in communications for intelligent transport systems from Eurecom, France. He is currently pursuing the Ph.D. degree in electrical engineering and information technology with the Communication Networks Group, Technische Universität Ilmenau, Germany. His research interests include vehicular networks, intelligent transport systems, computer networks, and wireless and mobile networks and communications. JOCHEN...

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Section: Safety and Efficiencymentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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“…Works in the literature that have proposed solutions for this type of problem can be classified as centralized [14][15][16][17][18][19][20][21][22] and decentralized or distributed traffic control approaches [5,[23][24][25][26][27][28][29][30]. One of the first works approaching the problem by modeling a Multiagent System (MAS) is proposed by [15].…”

Section: Previous Related Workmentioning

confidence: 99%

“…The intersection is modeled with two through lanes and one left turn per approach, respectively. In [30], the authors also design an MPC that aims to minimize the speed and jerk deviations. The authors of [28] tackle the problem by applying Mixed-Integer Nonlinear Programming (MINLP).…”

Section: Previous Related Workmentioning

confidence: 99%

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

2021

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This paper studies the planning of conflict-free and efficient crossings of antagonistic vehicles’ movements at lightless intersections. A fully automated infrastructure environment is considered, where all vehicles that enter the intersection area are connected and automated (CAVs), i.e., they are equipped with advanced communication and automation technologies. In such a futuristic environment, traffic lights that regulate the right-of-way of different traffic streams are obsolete because of vehicle communication capabilities. The connectivity is utilized to derive vehicle trajectories such that a safe and efficient crossing of lightless intersections is possible. So far, published studies lack the application to complex intersection layouts. To fill this gap, we introduce a control method for CAV pairing allowing for the safe, collision-free crossing of the intersecting area and optimize traffic conditions, i.e., total delays of the system. Simulation results demonstrate the feasibility and applicability of the presented approach, given that all the technical specifications (e.g., communications, velocity actuators) are present. Finally, we conduct a sensitivity analysis for the algorithm’s main parameters, which provides practical insights for the studied experimental scenarios and other existing algorithms in the literature that tackle this problem.

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“…In the second level, the authors considered a local MPC given the arrival time computed from the first level. Other authors have also formulated a local MPC problem for each CAV with defined crossing priorities [24]- [26].…”

Section: A Related Workmentioning

confidence: 99%

An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections

Chalaki

Malikopoulos

2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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In this paper, we provide a decentralized optimal control framework for coordinating connected and automated vehicles (CAVs) in two interconnected intersections. We formulate a control problem and provide a solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each CAV under the safety constraint at "conflict zones," where there is a chance of potential collision. Our objective is to minimize travel time for each CAV. If no such solution exists, then each CAV solves an energy-optimal control problem. We evaluate the effectiveness of the efficiency of the proposed framework through simulation.

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Distributed Model Predictive Intersection Control of Multiple Vehicles

Cited by 39 publications

References 19 publications

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections

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