Optimal Control of Connected and Automated Vehicles at Unsignalized Intersections: Discrete and Regroup

Qian, Lijun; Chen, Chen; Wu, Bing; Xuan, Liang; Wang, Tianxiang

doi:10.1109/iciem48762.2020.9160196

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Their experimental results concluded that the proposed approach minimizes the evacuation time and improved the throughput by 20%. Similar to this study, the authors of References [ 49 , 51 ] worked on space and time-aware approaches to manage the AV at an intersection that is robust against external disturbances and model mismatches. They proposed that the intersection manager is responsible to assign the Time of Arrival and Velocity of Arrival to an autonomous vehicle in such a way that the route of AV before and inside the intersections does not conflict.…”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 95%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 95%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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“…The system showed better throughput than existing IM methods. Qian et al [358] suggested a control method for CAVs to reduce congestion, travel time and ensure safety at the intersection. The optimal strategy was operated by an intersection manager and it was based on the TP by grouping the vehicles according to their longitudinal dynamics.…”

Section: Vehicle Dynamics and Model Mismatchmentioning

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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“…vehicles pass through the intersection safely and efficiently according to the set flow queue mode. In [50], vehicles arriving at the intersection are divided into discrete groups, and the continuous traffic flow problem is simplified to a multi-vehicle cooperation problem with limited vehicles. In [51], a cooperative framework is proposed.…”

Section: ) the Centralized Driving Strategymentioning

confidence: 99%

Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks

Wei

Gong

et al. 2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

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Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.

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Optimal Control of Connected and Automated Vehicles at Unsignalized Intersections: Discrete and Regroup

Cited by 5 publications

References 15 publications

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks

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