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Computers
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Cooperative Vehicular Networking 2023
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Cooperative Vehicular Networking 2023

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5887

Special Issue Editors

Dr. Zoran Duric

E-Mail Website
Guest Editor
Department of Computer Science, The Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: computer vision; information hiding
Prof. Dr. Cing-Dao (Steve) Kan

E-Mail Website
Guest Editor
Department of Computer Science, The Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: computational solid mechanics using non-linear finite element (FE) modeling and analysis methodologies
Dr. Moinul Hossain

E-Mail Website
Guest Editor
Department of Cyber Security Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: security assessment of wireless network protocols; threat modeling and counter-mechanism design; cross-technology spectrum sharing; dynamic spectrum access; vehicle-to-vehicle communications
Prof. Dr. Duminda Wijesekera

E-Mail Website
Guest Editor
Department of Computer Science, The Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: IT security; network security; cloud computing; security; software engineering

Special Issue Information

Dear Colleagues,

Human drivers have been driving in a mostly cooperative and sometimes competitive manner on roads with varying geometries, traffic regulations, weather, and visibility conditions. The objective of this Special Issue is to examine how this behavior can be transferred to fully autonomous driving or more realistically to driving with mixed traffic. The most likely case for autonomous driving is that human drivers will cooperate and sometimes compete with autonomous vehicles to gain advantages. Several major issues need to be addressed in this situation: the sensory capabilities of autonomous vehicles (where the ADAS use online learning methods to gain immediate situational awareness), the communication capabilities to convey this knowledge using standardized protocols, and the capability to use them with pre-programed real time controllers in automated vehicles in addition to ADAS systems that may evolve to support human drivers. Mobile edge computing for enhancing situational awareness and improved cooperative driving is considered crucial for the connected automated vehicles. Even with the same synthesized knowledge, controllers in different vehicles will be constrained by their vehicular dynamics, e.g., the difference between a tractor trailer and a smaller passenger vehicle.

Dr. Zoran Duric
Prof. Dr. Cing-Dao (Steve) Kan
Dr. Moinul Hossain
Prof. Dr. Duminda Wijesekera
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • connected vehicles
  • cooperative driving
  • ADAS
  • situational awareness
  • CV2X
  • mobile edge computing

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Published Papers (3 papers)

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Research

23 pages, 9993 KiB  
Article
Advanced Road Safety: Collective Perception for Probability of Collision Estimation of Connected Vehicles
by Sabrine Belmekki and Dominique Gruyer
Computers 2024, 13(1), 21; https://doi.org/10.3390/computers13010021 - 9 Jan 2024
Viewed by 1736
Abstract
In the dynamic landscape of vehicular communication systems, connected vehicles (CVs) present unprecedented capabilities in perception, cooperation, and, notably, probability of collision management. This paper’s main concern is the collision probability of collision estimation. Achieving effective collision estimation heavily relies on the sensor [...] Read more.
In the dynamic landscape of vehicular communication systems, connected vehicles (CVs) present unprecedented capabilities in perception, cooperation, and, notably, probability of collision management. This paper’s main concern is the collision probability of collision estimation. Achieving effective collision estimation heavily relies on the sensor perception of obstacles and a critical collision probability prediction system. This paper is dedicated to refining the estimation of collision probability through the intentional integration of CV communications, with a specific focus on the collective perception of connected vehicles. The primary objective is to enhance the understanding of the potential probability of collisions in the surrounding environment by harnessing the collective insights gathered through inter-vehicular communication and collaboration. This improvement enables a superior anticipation capacity for both the driving system and the human driver, thereby enhancing road safety. Furthermore, the incorporation of extended perception strategies holds the potential for more accurate collision probability estimation, providing the driving system or human driver with increased time to react and make informed decisions, further fortifying road safety measures. The results underscore a significant enhancement in collision probability awareness, as connected vehicles collectively contribute to a more comprehensive collision probability landscape. Consequently, this heightened collective collision probability perception improves the anticipation capacity of both the driving system and the human driver, contributing to an elevated level of road safety. For future work, the exploration of our extended perception techniques to achieve real-time probability of collision estimation is proposed. Such endeavors aim to drive the development of robust and anticipatory autonomous driving systems that truly harness the benefits of connected vehicle technologies. Full article
(This article belongs to the Special Issue Cooperative Vehicular Networking 2023)
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16 pages, 2729 KiB  
Article
Cooperative Vehicles versus Non-Cooperative Traffic Light: Safe and Efficient Passing
by Johan Thunberg, Taqwa Saeed, Galina Sidorenko, Felipe Valle and Alexey Vinel
Computers 2023, 12(8), 154; https://doi.org/10.3390/computers12080154 - 30 Jul 2023
Cited by 1 | Viewed by 1614
Abstract
Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. [...] Read more.
Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. We consider a scenario where vehicles approaching a traffic light are connected to each other, but the traffic light itself is not cooperative. Information about indented trajectories such as decisions on how and when to accelerate, decelerate and stop, is communicated among the vehicles involved. We provide an optimization-based procedure for efficient and safe passing of traffic lights (or other temporary road blockage) using vehicle-to-vehicle communication (V2V). We locally optimize objectives that promote efficiency such as less deceleration and larger minimum velocity, while maintaining safety in terms of no collisions. The procedure is computationally efficient as it mainly involves a gradient decent algorithm for one single parameter. Full article
(This article belongs to the Special Issue Cooperative Vehicular Networking 2023)
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23 pages, 3686 KiB  
Article
Abstract Entity Patterns for Sensors and Actuators
by Bijayita Thapa, Eduardo B. Fernandez, Ionut Cardei and Maria M. Larrondo-Petrie
Computers 2023, 12(5), 93; https://doi.org/10.3390/computers12050093 - 30 Apr 2023
Cited by 1 | Viewed by 1950
Abstract
Sensors and actuators are fundamental units in Cyber–Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), which [...] Read more.
Sensors and actuators are fundamental units in Cyber–Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), which are patterns that describe abstract conceptual entities. From AEPs, we can derive concrete patterns; a structure combining related AEPs is an Entity Solution Frame (ESF). This paper concentrates on the functional aspects of these devices and defines conceptual units that can be used to design any system that requires sensors and actuators; that is, almost any Cyber–Physical system. For concreteness, we explore them in this study in the context of autonomous cars. An autonomous car is a complex system because, in addition to its own complex design, it interacts with other vehicles and with the surrounding infrastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber–Physical System, where some of its functions are performed via Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appropriate physical actions. Both sensors and actuators are susceptible to malicious attacks due to the large attack surface of the system in which they are used. Our work is intended to make autonomous cars more secure, which also increases their safety. Our final objective is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups together and organizes related security patterns. Full article
(This article belongs to the Special Issue Cooperative Vehicular Networking 2023)
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