Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Gutesa, Slobodan; Lee, Joyoung; Besenski, Dejan

doi:10.1177/0361198121994580

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Based on the assumption that vehicle trajectories can be defined as cubic interpolated splines, having the flexibility to reflect delays from the given signal timing, Gutesta et al [14] developed a trajectory-driven intersection control for the CAVs. Vehicle trajectories were developed for several vehicles passing multiple intersections.…”

Section: Trajectory Planning Intersection Control Logics For Cavsmentioning

confidence: 99%

Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment

Mirbakhsh

Lee

Besenski

2023

Future Transportation

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Assuming a full market penetration rate of connected and autonomous vehicles (CAVs) would provide an opportunity to remove costly and inefficient traffic lights from intersections, this paper presents a signal-free intersection control system relying on CAVs’ communicability. This method deploys a deep reinforcement learning algorithm and pixel reservation logic to avoid potential collisions and minimize the overall delay at the intersection. To facilitate a traffic-oriented assessment of the model, the proposed model’s application is coupled with VISSIM traffic microsimulation software, and its performance is compared with other intersection control systems, including fixed traffic lights, actuated traffic lights, and the Longest Queue First (LQF) control system. The simulation result revealed that the proposed model reduces delay by 50%, 29%, and 23% in moderate, high, and extreme volume regimes, respectively, compared to another signal-free control system. Noticeable improvements are also gained in travel time, fuel consumption, emission, and Surrogate Safety Measures.

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Section: Trajectory Planning Intersection Control Logics For Cavsmentioning

confidence: 99%

Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment

Mirbakhsh

Lee

Besenski

2023

Future Transportation

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“…Shen et al [35] made use of centralized MPC-based mechanism and provided a platform for CAVs to approach the signalized intersection with a smooth speed. Gutesa and Besenski [50] developed a centralized IM mechanism for CAVs based on trajectory planning at the signalized intersection. A control algorithm allocated the optimal path considering various inputs such as vehicle's position, speed, signalization status and current traffic at the intersection.…”

Section: Signalized Intersectionsmentioning

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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“…Tere are good results under diferent conditions of CAV permeability. Gutesa et al [4] proposed an intersection management strategy for automated vehicle corridors based on the vehicle trajectory-driven optimization method. Te automated driving trajectory driven optimization model was based on the vehicle position, trafc conditions, and signal status on the corridor to calculate the optimal trajectory for CAV, and reduced vehicle delays along the signal corridor with fxed time signal control.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Trajectory Control and Signal Timing Optimization of Isolated Intersection under V2X Environment

Zou

Guo

et al. 2023

Journal of Advanced Transportation

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This study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%–60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model.

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Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Cited by 5 publications

References 18 publications

Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment

Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Vehicle Trajectory Control and Signal Timing Optimization of Isolated Intersection under V2X Environment

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