Coordination of connected autonomous and human-operated vehicles at the intersection

Shen, Zhaokun; Mahmood, Arshad; Wang, Lin

doi:10.1109/aim.2019.8868658

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Another extension of [15] was presented in [63], where different types of semi-autonomous vehicles with features like cruise control received reservations similar to the CAVs. Shen et al [64] deployed a FCFS-based centralized hybrid intersection system where on-board units (OBUs) were proposed for non-autonomous vehicles to communicate with the controller using different signals like pass and stop. Further, Sharon et al [65] designed a centralized hybrid intersection management protocol that involved autonomous and manually operated vehicles.…”

Section: Semi-autonomous Intersections (Hybrid)mentioning

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: Semi-autonomous Intersections (Hybrid)mentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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“…The trajectory tracking of CAVs can be solved by vehicle lateral control [28]- [30]. For convenience, the Cartesian coordinate system is transformed into the coordinate system for vehicle path tracking, and the corresponding descriptions for left turn trajectory, straight trajectory, right turn trajectory are formulated as follows.…”

Section: A Path Planning Modelmentioning

confidence: 99%

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

Zhou¹,

Shen²,

Wang³

et al. 2022

Preprint

Self Cite

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With the rapid development of connected and automated vehicles (CAVs) and intelligent transportation infrastructure, CAVs and connected human-driven vehicles (CHVs) will coexist on the roads in the future for a long time. This paper comprehensively considers the different traffic characteristics of CHVs and CAVs, and systemically investigates the unsignalized intersection management strategy from upper decision-making level to lower execution level. Combined with the designed vehicle planning and control algorithm, the unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm, and the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle priority management model considering the difference between CAVs and CHVs is built to design the right of way management for CAVs and CHVs, respectively. In the lower level for vehicle planning and control algorithm, different control modes of CAVs are designed according to the upper level decision made by the HPQ algorithm. Moreover, the vehicle control execution is realized by the model predictive controller combined with the geographical environment constraints and the unsignalized intersection management strategy. The proposed strategy is evaluated by simulations, which show that the proposed intersection management strategy can effectively reduce travel time and improve traffic efficiency. The intersection management strategy captures the real-world balance between efficiency and safety for potential future intelligent traffic systems.

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“…This approach was tested on an actual vehicle and an API for the driver. In Reference [111], Shen et al propose to use an On-Board Unit (OBU) to convey different communication signals to HVs. Two commands are envisioned for both CAVs and HVs: "pass" and "stop," and HVs are assumed to follow the command.…”

Section: Hybrid (Human-cav) Intersectionsmentioning

confidence: 99%

A Survey on Intersection Management of Connected Autonomous Vehicles

Khayatian

Mehrabian

Andert

et al. 2020

ACM Trans. Cyber-Phys. Syst.

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Intersection management of Connected Autonomous Vehicles (CAVs) has the potential to improve safety and mobility. CAVs approaching an intersection can exchange information with the infrastructure or each other to schedule their cross times. By avoiding unnecessary stops, scheduling CAVs can increase traffic throughput, reduce energy consumption, and most importantly, minimize the number of accidents that happen in intersection areas due to human errors. We study existing intersection management approaches from following key perspectives: (1) intersection management interface, (2) scheduling policy, (3) existing wireless technologies, (4) existing vehicle models used by researchers and their impact, (5) conflict detection, (6) extension to multi-intersection management, (7) challenges of supporting human-driven vehicles, (8) safety and robustness required for real-life deployment, (9) graceful degradation and recovery for emergency scenarios, (10) security concerns and attack models, and (11) evaluation methods. We then discuss the effectiveness and limitations of each approach with respect to the aforementioned aspects and conclude with a discussion on tradeoffs and further research directions.

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Coordination of connected autonomous and human-operated vehicles at the intersection

Cited by 6 publications

References 9 publications

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

A Survey on Intersection Management of Connected Autonomous Vehicles

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