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Cooperative Vehicular Platooning (Co-VP) is a paradigmatic example of a Cooperative Cyber-Physical System (Co-CPS), which holds the potential to vastly improve road safety by partially removing humans from the driving task. However, the challenges are substantial, as the domain involves several topics, such as control theory, communications, vehicle dynamics, security, and traffic engineering, that must be coupled to describe, develop and validate these systems of systems accurately. This work presents a comprehensive survey of significant and recent advances in Co-VP relevant fields. We start by overviewing the work on control strategies and underlying communication infrastructures, focusing on their interplay. We also address a fundamental concern by presenting a cyber-security overview regarding these systems. Furthermore, we present and compare the primary initiatives to test and validate those systems, including simulation tools, hardware-in-the-loop setups, and vehicular testbeds. Finally, we highlight a few open challenges in the Co-VP domain. This work aims to provide a fundamental overview of highly relevant works on Co-VP topics, particularly by exposing their inter-dependencies, facilitating a guide that will support further developments in this challenging field.info:eu-repo/semantics/publishedVersio

A Full-fledge Simulation Framework for the Assessment of Connected Cars

Abstract Intelligent Transport Systems (ITS) have emerged as an integral part of smart cities, providing increased ease of mobility as well as efficiency and safety in vehicular traffic. Given its wide array of applications, ITS has also become a multidisciplinary field of work where vehicular communications, traffic control, ADAS (Advance Driver Assistance System) sensors, and vehicle dynamics have all to be accounted for. The study of such diverse aspects makes the evaluation of new ITS approaches, algorithms, and protocols not a small feat. For this reason, the availability of an effective, scalable, and comprehensive tool for the investigation and virtual validation of new ITS solutions is paramount. In this work, we present a simulation framework, called CoMoVe (Communication, Mobility, Vehicle dynamics), that effectively addresses the above need, as it enables the virtual validation of innovative solutions for vehicles that are both connected and equipped with ADAS sensors. Our framework encapsulates the important attributes of vehicle communication, road traffic, and dynamics into a single environment, by combining the strengths of different simulators. CoMoVe finds its use to evaluate the impact of vehicle connectivity, while imposing causality on vehicle dynamics and mobility. Such an assessment can greatly facilitate the development of control systems, algorithms, and protocols for real-world ITS

Co-simulated digital twin on the network edge: A vehicle platoon

This paper presents an approach to create high-fidelity models suited for digital twin application of distributed multi-agent cyber–physical systems (CPSs) exploiting the combination of simulation units through co-simulation. This approach allows for managing the complexity of cyber–physical systems by decomposing them into multiple intertwined components tailored to specific domains. The native modular design simplifies the building, testing, prototyping, and extending CPSs compared to monolithic simulator approaches. A system of platoon of vehicles is used as a case study to show the advantages achieved with the proposed approach. Multiple components model the physical dynamics, the communication network and protocol, as well as different control software and external environmental situations. The model of the platooning system is used to compare the performance of Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm. Moreover, exploiting the detailed model of vehicle dynamics, different road surface conditions are considered to evaluate the performance of the platooning system. Finally, taking advantage of the co-simulation approach, a solution to drive a platoon in critical road conditions has been proposed. The paper shows how co-simulation and design space exploration can be used for parameter calibration and the design of countermeasures to unsafe situations

A Survey on platoon-based vehicular cyber-physical systems

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another one and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared to driving individually, such a platoon-based driving pattern can significantly improve the road capacity and energy efficiency. Moreover, with the emerging vehicular adhoc network (VANET), the performance of platoon in terms of road capacity, safety and energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of VANET. Such a complex system can be considered as a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on platoon-based VCPS. We first review the related work of platoon-based VCPS. We then introduce two elementary techniques involved in platoon-based VCPS: the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control (CACC), platoon-based vehicular communications, etc., and all of which are characterized by the tight coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions

Connected and Autonomous Vehicles Applications Development and Evaluation for Transportation Cyber-Physical Systems

Cyber-Physical Systems (CPS) seamlessly integrate computation, networking and physical devices. A Connected and Autonomous Vehicle (CAV) system in which each vehicle can wirelessly communicate and share data with other vehicles or infrastructures (e.g., traffic signal, roadside unit), requires a Transportation Cyber-Physical System (TCPS) for improving safety and mobility, and reducing greenhouse gas emissions. Unfortunately, a typical TCPS with a centralized computing service cannot support real-time CAV applications due to the often unpredictable network latency, high data loss rate and expensive communication bandwidth, especially in a mobile network, such as a CAV environment. Edge computing, a new concept for the CPS, distributes the resources for communication, computation, control, and storage at different edges of the systems. TCPS with edge computing strategy forms an edge-centric TCPS. This edge-centric TCPS system can reduce data loss and data delivery delay, and fulfill the high bandwidth requirements. Within the edge-centric TCPS, Vehicle-to-X (V2X) communication, along with the in-vehicle sensors, provides a 360-degree view for CAVs that enables autonomous vehicles’ operation beyond the sensor range. The addition of wireless connectivity would improve the operational efficiency of CAVs by providing real-time roadway information, such as traffic signal phasing and timing information, downstream traffic incident alerts, and predicting future traffic queue information. In addition, temporal variation of roadway traffic can be captured by sharing Basic Safety Messages (BSMs) from each vehicle through the communication between vehicles as well as with roadside infrastructures (e.g., traffic signal, roadside unit) and traffic management centers. In the early days of CAVs, data will be collected only from a limited number of CAVs due to a low CAV penetration rate and not from other non-connected vehicles. This will result in noise in the traffic data because of low penetration rate of CAVs. This lack of data combined with the data loss rate in the wireless CAV environment makes it challenging to predict traffic behavior, which is dynamic over time. To address this challenge, it is important to develop and evaluate a machine learning technique to capture stochastic variation in traffic patterns over time. This dissertation focuses on the development and evaluation of various connected and autonomous vehicles applications in an edge-centric TCPS. It includes adaptive queue prediction, traffic data prediction, dynamic routing and Cooperative Adaptive Cruise Control (CACC) applications. An adaptive queue prediction algorithm is described in Chapter 2 for predicting real-time traffic queue status in an edge-centric TCPS. Chapter 3 presents noise reduction models to reduce the noise from the traffic data generated from the BSMs at different penetration of CAVs and evaluate the performance of the Long Short-Term Memory (LSTM) prediction model for predicting traffic data using the resulting filtered data set. The development and evaluation of a dynamic routing application in a CV environment is detailed in Chapter 4 to reduce incident recovery time and increase safety on a freeway. The development of an evaluation framework is detailed in Chapter 5 to evaluate car-following models for CACC controller design in terms of vehicle dynamics and string stability to ensure user acceptance is detailed in Chapter 5. Innovative methods presented in this dissertation were proven to be providing positive improvements in transportation mobility. These research will lead to the real-world deployment of these applications in an edge-centric TCPS as the dissertation focuses on the edge-centric TCPS deployment strategy. In addition, as multiple CAV applications as presented in this dissertation can be supported simultaneously by the same TCPS, public investments will only include infrastructure investments, such as investments in roadside infrastructure and back-end computing infrastructure. These connected and autonomous vehicle applications can potentially provide significant economic benefits compared to its cost

Quantifying the Impact of Cellular Vehicle-to-Everything (C-V2X) on Transportation System Efficiency, Energy and Environment

69A43551747123As communication technology develops at a rapid pace, connected vehicles (CVs) can potentially enhance vehicle safety while reducing energy consumption and emissions via data sharing. Many researchers have attempted to quantify the impacts of such CV applications and cellular vehicle-to-everything (C-V2X) communication. Highly efficient information interchange in a CV environment can provide timely data to enhance the transportation system\u2019s capacity, and it can support applications that improve vehicle safety and minimize negative impacts on the environment. This study summarizes existing literature on the safety, mobility, and environmental impacts of CV applications; gaps in current CV research; and recommended directions for future CV research. The study investigates a C-V2X eco-routing application that considers the performance of the C-V2X communication technology (mainly packet loss). The performance of the C-V2X communication is dependent on the vehicular traffic density, which is affected by traffic mobility patterns and vehicle routing strategies. As a case study of C-V2X applications, we developed an energy-efficient dynamic routing application using C-V2X Vehicle-to-Infrastructure (V2I) communication technology. Specifically, we developed a Connected Energy-Efficient Dynamic Routing (C-EEDR) application and used it in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles

Distributed H∞ Controller Design and Robustness Analysis for Vehicle Platooning Under Random Packet Drop

This paper presents the design of a robust distributed state-feedback controller in the discrete-time domain for homogeneous vehicle platoons with undirected topologies, whose dynamics are subjected to external disturbances and under random single packet drop scenario. A linear matrix inequality (LMI) approach is used for devising the control gains such that a bounded H∞ norm is guaranteed. Furthermore, a lower bound of the robustness measure, denoted as γ gain, is derived analytically for two platoon communication topologies, i.e., the bidirectional predecessor following (BPF) and the bidirectional predecessor leader following (BPLF). It is shown that the γ gain is highly affected by the communication topology and drastically reduces when the information of the leader is sent to all followers. Finally, numerical results demonstrate the ability of the proposed methodology to impose the platoon control objective for the BPF and BPLF topology under random single packet drop