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This paper presents a cooperative highway platooning strategy that integrates Multi-Agent Reinforcement Learning (MARL) with Proximal Policy Optimization (PPO) to effectively manage the complex task of merging. In modern transportation systems, platooning—where multiple vehicles travel closely together under coordinated control—promises significant improvements in traffic flow and fuel efficiency. However, the challenge of merging, which involves dynamically adjusting the formation to incorporate new vehicles, remains challenging. Our approach leverages the strengths of MARL to enable individual vehicles within a platoon to learn optimal behaviors through interactions. PPO ensures stable and efficient learning by optimizing policies balancing exploration and exploitation. Simulation results show that our method achieves merging with safety and operational efficiency. [ABSTRACT FROM AUTHOR]

This paper provides a detailed literature review of the environmental implications of vehicle platooning, a topic gaining significant attention in transportation. While previous reviews have focused on the safety, planning, fuel economy, and microsimulation aspects of platooning, this paper delves into environmental aspects. It identifies a lack of research adopting a holistic approach to transport and environmental benefits and emphasizes the need for further research to enhance vehicle efficiency and improve air quality and health conditions. This study traces the historical evolution of platooning, highlighting the shift in research focus over the decades. It advocates for more research on platooning’s environmental aspects, particularly pollutant emissions and air quality. The primary contributions of this work are threefold and include the following: firstly, it delineates simulation methodologies for platooning and the associated pollutant emissions; secondly, it offers a critical assessment of the existing literature on vehicle emissions, fuel consumption, and energy savings; and thirdly, it illuminates the prospective research challenges within the specialized domain of vehicle platooning.

This paper provides a detailed literature review of the environmental implications of vehicle platooning, a topic gaining significant attention in transportation. While previous reviews have focused on the safety, planning, fuel economy, and microsimulation aspects of platooning, this paper delves into environmental aspects. It identifies a lack of research adopting a holistic approach to transport and environmental benefits and emphasizes the need for further research to enhance vehicle efficiency and improve air quality and health conditions. This study traces the historical evolution of platooning, highlighting the shift in research focus over the decades. It advocates for more research on platooning's environmental aspects, particularly pollutant emissions and air quality. The primary contributions of this work are threefold and include the following: firstly, it delineates simulation methodologies for platooning and the associated pollutant emissions; secondly, it offers a critical assessment of the existing literature on vehicle emissions, fuel consumption, and energy savings; and thirdly, it illuminates the prospective research challenges within the specialized domain of vehicle platooning. [ABSTRACT FROM AUTHOR]

For the first time, this paper examines the aerodynamic forces that arise in a platoon of Heavy Goods Vehicle lorries travelling in close proximity during an overtaking manoeuvre. Through an in-depth programme of wind tunnel experiments it is demonstrated that there is a complex relationship between the drag and side force coefficients, with a possible hysteresis-like behaviour shown. The influence of the overtaken lorry on the overtaking lorry is shown to be key in terms of the aerodynamic-induced forces experienced by the latter. Furthermore, the overtaking lorry is shown to benefit aerodynamically from the overtaken lorry, and this is attributed to the elliptical nature of the Navier-Stokes equations. In addition to examining the forces that arise during overtaking, the variation of the wind-induced forces with respect to yaw on a single lorry is compared to those on a middle lorry in a 3-lorry platoon. The aerodynamic benefits arising from platooning/vehicles travelling in close proximity are again demonstrated, although it is shown that these benefits may only exist over a limited range of yaw angles, raising questions about the real-world application of this approach. This has important implications for platooning in crosswinds and the drag force reduction that is assumed due to platooning. [ABSTRACT FROM AUTHOR]

Traffic intersections act as pivotal junctions where various streams of traffic intersect and intersecting movements occur. When traffic volumes are high, intersections that are efficiently designed and equipped with intelligent traffic control systems can optimize the movement of vehicles. Accordingly, the time spent by vehicles at intersections increases, and these vehicles cause the vehicles behind them to slow down. This study aims to enhance the efficacy of reservation-based intersection control for platoons comprising vehicles arriving at intersections and turning in various directions. To assess the impact of different platooning algorithms on both the average speed of vehicles within the platoons and the total time spent at intersections, simulations were conducted using the SUMO environment. These simulations observed variations in the average speed values of vehicles and the duration spent by vehicles at intersections. The simulation results showed that the proposed reservation-based platoon ordering algorithm significantly contributed to increasing the average speed and reducing waiting time.

As one of the promising intelligent transportation frameworks, vehicular platooning has the potential to bring about sustainable and efficient mobility solutions. One of the challenges in the development of platooning is maintaining the string stability, which ensures that there is no amplification of the signal of interest along the platoon chain. String stability is dependent on reliable inter-vehicle communications and proper controller design. Therefore, in this paper, we formulate radio resource management (RRM) problem with the purpose of satisfying the reliability of the vehicle-to-vehicle (V2V) links and string stability of the platoon. We tackle the optimization problem from different angles. First, we devise centralized classical approaches based on difference of two convex functions (d.c.) programming, in which we assume the base station (BS) has full knowledge over the V2V channel gains. In the second strategy, we develop decentralized resource allocation approaches based on multi-agent reinforcement learning (MARL). In essence, we model each transmitter vehicle in the platoon as an autonomous agent that tries to find an optimal policy according to its local estimated information to maximize the total expected reward. We also investigate whether the integration of federated learning (FL) with decentralized MARL algorithms can bring any potential benefits. This comparison between classical and machine learning (ML)-based RRM strategies helps us make crucial observations in terms of robustness, sensitivity, and efficacy of the policies that are learned by reinforcement learning (RL)-based resource allocation algorithms.

Traffic intersections act as pivotal junctions where various streams of traffic intersect and intersecting movements occur. When traffic volumes are high, intersections that are efficiently designed and equipped with intelligent traffic control systems can optimize the movement of vehicles. Accordingly, the time spent by vehicles at intersections increases, and these vehicles cause the vehicles behind them to slow down. This study aims to enhance the efficacy of reservation-based intersection control for platoons comprising vehicles arriving at intersections and turning in various directions. To assess the impact of different platooning algorithms on both the average speed of vehicles within the platoons and the total time spent at intersections, simulations were conducted using the SUMO environment. These simulations observed variations in the average speed values of vehicles and the duration spent by vehicles at intersections. The simulation results showed that the proposed reservation-based platoon ordering algorithm significantly contributed to increasing the average speed and reducing waiting time. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration. [ABSTRACT FROM AUTHOR]

For the first time, this paper examines the aerodynamic forces that arise in a platoon of Heavy Goods Vehicle lorries travelling in close proximity during an overtaking manoeuvre. Through an in-depth programme of wind tunnel experiments it is demonstrated that there is a complex relationship between the drag and side force coefficients, with a possible hysteresis-like behaviour shown. The influence of the overtaken lorry on the overtaking lorry is shown to be key in terms of the aerodynamic-induced forces experienced by the latter. Furthermore, the overtaking lorry is shown to benefit aerodynamically from the overtaken lorry, and this is attributed to the elliptical nature of the Navier-Stokes equations. In addition to examining the forces that arise during overtaking, the variation of the wind-induced forces with respect to yaw on a single lorry is compared to those on a middle lorry in a 3-lorry platoon. The aerodynamic benefits arising from platooning/vehicles travelling in close proximity are again demonstrated, although it is shown that these benefits may only exist over a limited range of yaw angles, raising questions about the real-world application of this approach. This has important implications for platooning in crosswinds and the drag force reduction that is assumed due to platooning.

The advent of autonomous vehicles has opened new horizons for transportation efficiency and safety. Platooning, a strategy where vehicles travel closely together in a synchronized manner, holds promise for reducing traffic congestion, lowering fuel consumption, and enhancing overall road safety. This article explores the application of Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO) to optimize autonomous vehicle platooning. We delve into the world of MARL, which empowers vehicles to communicate and collaborate, enabling real-time decision making in complex traffic scenarios. PPO, a cutting-edge reinforcement learning algorithm, ensures stable and efficient training for platooning agents. The synergy between MARL and PPO enables the development of intelligent platooning strategies that adapt dynamically to changing traffic conditions, minimize inter-vehicle gaps, and maximize road capacity. In addition to these insights, this article introduces a cooperative approach to Multi-Agent Reinforcement Learning (MARL), leveraging Proximal Policy Optimization (PPO) to further optimize autonomous vehicle platooning. This cooperative framework enhances the adaptability and efficiency of platooning strategies, marking a significant advancement in the pursuit of intelligent and responsive autonomous vehicle systems. [ABSTRACT FROM AUTHOR]

Truck platooning technology allows trucks to drive at short headways to save fuel and associated emissions. However, fuel savings from platooning are relatively small, so forming platoons should be convenient and associated with minimum detours and delays. In this paper, we focus on developing optimization technology to form truck platoons. We formulate a mathematical program for the platoon routing problem with time windows (PRP-TW) based on a time–space network. We provide polynomial-time algorithms to solve special cases of PRP-TW with two-truck platoons. Based on these special cases, we build several fast heuristics. An extensive set of numerical experiments shows that our heuristics perform well. Moreover, we show that simple two-truck platoons already capture most of the potential savings of platooning. History: Accepted by Pascal van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Funding: This work was supported by the Netherlands Organization for Scientific Research (NWO) as part of the Spatial and Transport Impacts of Automated Driving [Grant 438-15-161] project. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2020.0302) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2020.0302). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability. [ABSTRACT FROM AUTHOR]

Platooning is generally known as a control method for driving a group of connected and automated vehicles in motorway context. Nevertheless, platoon control might also work on urban roads. One possible strategy to increase overall road traffic performance and to reduce congestion in urban traffic networks is to combine platooning with traffic signal control at intersections. The traffic flow can be maximized with coordinated scheduling of traffic signals together with platooning activities, resulting in decreased travel times and fuel consumption. This paper investigates several aspects of this combined control, such as the procedures for coordination and communication between platooning vehicles and traffic signals. Efficient algorithms are suggested to optimize platoon formation and dissolution at junctions and to change traffic signal phases depending on platoon arrival and departure times. The proposed solutions have been tested and verified with SUMO, a high-fidelity microscopic traffic simulator. [ABSTRACT FROM AUTHOR]

In today's world, circumstances, processes, and requirements for software systems are becoming increasingly complex. To operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this article, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework. [ABSTRACT FROM AUTHOR]

The emerging technologies of connected and automated vehicles (CAVs) and deep reinforcement learning (DRL) provide innovative methods and have a great potential for developing new solutions to improve the efficiency of several intersection systems. Based on the multisource data collected from the transportation environments, CAVs with the cooperative adaptive cruise control (CACC) system could merge into platoons and traverse the intersection quickly and smoothly. Meanwhile, the traffic information about the CAVs enables intelligent traffic signal controls with the help of DRL technologies. This research investigates the performance of a state-shared multiagent deep reinforcement learning (MADRL) controlled signal corridor with platooning-based CAVs. A corridor with seven intersections from the Ingolstadt Traffic Scenario (InTAS) in Germany is selected as a case study. The state information is shared between neighboring intersections to overcome the partial information observation of the decentralized agents in the MADRL framework. A platooning framework with specific CACC systems for the leading and following vehicles is proposed. Results indicate that the state-shared MADRL with CAV platoons could significantly decrease the total waiting time, average queue length, and total CO2 emission of the corridor by 80%, 73%, and 54%, respectively, which could be beneficial in further improving the intersection efficiency, designing future intersections, and cooperating signals and CAVs platoons. [ABSTRACT FROM AUTHOR]

This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the PID system, thereby elucidating its potential for incorporation into AI-enabled radio and networks. This research unveils crucial insights for augmenting the performance and safety parameters of vehicle platooning systems within B5G networks, concurrently underlining the prospective applications of LLMs within such technologically advanced communication environments. [ABSTRACT FROM AUTHOR]

Platooning enables vehicles with similar interests and target destinations to travel together in a pre-defined order and is identified as a promising traffic management strategy toward energy efficiency, time management, and highway throughput. However, the formation of a platoon is currently limited to signalized intersections where a vehicle has to wait for an appropriate platoon to join in leading to time wastage. This paper investigates and presents a dynamic architecture that can coordinate platoon manipulations while at transit so that a vehicle can join any available platoon at the beginning of the journey and then transfer during transit. In the presence of an adjacent platoon, the data broadcast by the Platoon Leaders and Members are used to compute evaluation scores such that any vehicle can choose a Leader and the respective Platoon which is most analogous to it and initiate the transfer. Based on this, a multiple-step platoon formation framework is introduced, which assesses an optimal velocity and position for the Incoming Member/Transferee in the platoon to which it is transferred eliminating any conflict. It utilizes Vehicle-to-Vehicle communication and a negotiation algorithm based on Dynamic Games of Complete Information guaranteeing optimal and fair platoon manipulations. Simulation results demonstrate that the proposed algorithm effectively handles the transfer at transit for platoons with a reduction in energy consumption proving the transfers beneficial along with substantial time savings. This research can be employed in situations requiring interaction between any two agents toward a bilateral optimal solution such as logistics and decision-making in common. [ABSTRACT FROM AUTHOR]

This paper proposes a new cooperative driving model for the platooning of connected and automated vehicles on basic freeway sections. We introduce the desired speed, consider the communication delay between vehicles in the actual environment and employ the constant time headway policy as the spacing policy. In the model, the position error, the speed error, the difference between the desired speed and the speed of the considered vehicle and the acceleration are taken into account. The information obtained by vehicle communication is fully utilized, and the control effect of the control strategy is improved. We apply the Lyapunov–Krasovskii theorem to investigate the stability of the system. The effectiveness of the proposed model is verified by several traffic simulation scenarios. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC) is expected to support platooning of vehicles, by running the control logic that computes the acceleration values of the vehicles based on their position and speed. Connectivity between vehicles and the MEC system is likely to be realized through the mobile network. However, platoons will be composed of vehicles that are customers of different co-located mobile operators, hence– in all likelihood– will also be customers of different MEC systems. In this paper, we devise and evaluate an architectural framework that realizes Platooning-as-a-Service (PlaaS) in a multi-operator MEC environment, compliant with the ETSI MEC standard: we describe the entities involved and their interactions, we release an open-source proof-of-concept implementation for the popular Simu5G simulator, and we evaluate the effect of our framework– and especially its latencies– on platoon stability.

Utilizing Vehicle-to-everything (V2X) communication technologies, vehicle platooning systems are expected to realize a new paradigm of cooperative driving with higher levels of traffic safety and efficiency. Connected and Autonomous Vehicles (CAVs) need to have proper awareness of the traffic context. The cooperative platoon’s performance will be influenced by the communication strategy. In particular, time-triggered or event-triggered are of interest here. The expenses related to communication will increase significantly as the number of connected entities increases. Periodic communication can be relaxed to more flexible aperiodic or event-triggered implementations while maintaining desired levels of performance. This paper proposes a predictive model-based and control-aware communication solution for vehicle platoons. The method uses a fully distributed Event-Triggered Communication (ETC) strategy combined with Model-Based Communication (MBC) and aims to minimize communication resource usage while preserving desired closed-loop performance characteristics. In our method, each vehicle runs a remote vehicle state estimator based on the most recently communicated model and the event-driven communication scheme only updates the model when the performance metric error exceeds a certain threshold. Our approach achieves a significant reduction in the average communication rate (82%) while only slightly reducing control performance (e.g., less than 1% speed deviation).

Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration.

Developing radio access technologies that enable reliable and low-latency vehicular communications have become of the utmost importance with the rise of interest in autonomous vehicles. The Third Generation Partnership Project (3GPP) has developed Vehicle to Everything (V2X) specifications based on the 5G New Radio Air Interface (NR-V2X) to support connected and automated driving use cases, with strict requirements to fulfill the constantly evolving vehicular applications, communication, and service demands of connected vehicles, such as ultra-low latency and ultra-high reliability. This paper presents an analytical model for evaluating the performance of NR-V2X communications, with particular reference to the sensing-based semi-persistent scheduling operation defined in the NR-V2X Mode 2, in comparison with legacy sidelink V2X over LTE, specified as LTE-V2X Mode 4. We consider a vehicle platooning scenario and evaluate the impact of multiple access interference on the packet success probability, by varying the available resources, the number of interfering vehicles, and their relative positions. The average packet success probability is determined analytically for LTE-V2X and NR-V2X, taking into account the different physical layer specifications, and the Moment Matching Approximation (MMA) is used to approximate the statistics of the signal-to-interference-plus-noise ratio (SINR) under the assumption of a Nakagami-lognormal composite channel model. The analytical approximation is validated against extensive Matlab simulations that a show good accuracy. The results confirm a boost in performance with NR-V2X against LTE-V2X, particularly for high inter-vehicle distance and a large number of vehicles, providing a concise yet accurate modeling rationale for planning and adaptation of the configuration and parameter setup of vehicle platoons, without having to resort to extensive computer simulation or experimental measurements. [ABSTRACT FROM AUTHOR]

In this article, we introduce physical-layer security enhancement technique based on artificial noise for secure device-to-device (D2D) communications within a platoon in vehicular platooning networks. In particular, we consider a scenario that two D2D-enabled platoons are on the same way. All the vehicles in one platoon are used to generate artificial noise to enhance the secrecy of D2D communications within the other platoon against information leakage to a potential eavesdropping vehicle. We propose artificial noise generation strategies by the platoon according to the assumption on channel state information (CSI): full-CSI and partial CSI assumptions. We further analyze the effect of artificial noise depending on the relationship between the number of vehicles in two platoons. We finally evaluate the performance of our proposed schemes in terms of secrecy rate through extensive simulations and provide useful intuitions for secure platooning.

Abstract Car-sharing services have recently attracted considerable attention. We proposed a platooning system to reduce the number of vehicle distributors. The platooning system uses a measurement device embedded with low-cost infrared distance sensors to measure the relative position and orientation of vehicles. The relative positions and orientations are obtained from the training data. However, preparing training data is time consuming. In this study, a sensor clustering method that selects sensors with similar output characteristics is proposed. Consequently, a set of training data are used repetitively for all relative positions and orientation measurement devices embedded with sensors with similar output characteristics. The verification experiment of the sensor clustering revealed that the calculation range restriction is the key technique. Platooning has been successful in various courses by using sensors with similar output characteristics. Based on the results, the proposed clustering method can effectively collect sensors with similar output characteristics and it realizes the training data transfer to the newly manufactured devices. In addition, it has the potential to improve production efficiency for the mass production of relative position and orientation measurement devices.

Objective: This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control. Background: Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers' responses, but silent failures and their interactions with scenario criticality are understudied. Method: A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher's exact tests were used to investigate the impact of alert and scenario criticality on takeover performance. Results: The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality Conclusion: Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role—by leading to an unsafe maneuver—in critical automated vehicle takeovers. Application: Understanding the implications of silent failure on driver's takeover performance can benefit the assessment of automated vehicles' safety and provide guidance for fail-safe system designs. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are revolutionizing transport and next-generation autonomous mobility. Such vehicles are promising to increase road safety, improve traffic efficiency, reduce vehicle emission, and improve mobility. However, for these vehicles to live up to their full potentials, there are significant research, technological and urgent organizational issues that need to be addressed to reach the highest level of automation, i.e., level 5. Sensors, communication, mobile edge computing, machine learning, data analytic, distributed learning, and so on, are examples of the building blocks technologies and concepts constituting the end-to-end solution. This survey discusses these technologies and concepts and maps their roles to the end-to-end solution. It highlights the challenges for each technology. Moreover, this survey provides an analysis of different solution approaches proposed by relevant stakeholders, utilizing these technologies aiming to achieve level-5 autonomy. Finally, the article details two use cases to present the interplay of the building blocks technologies. [ABSTRACT FROM AUTHOR]

Social costs increase as traffic problems continue. Driverless vehicles are attracting attention as a way to overcome this issue. Many said that fully autonomous driving is not a distant future. As one of the autonomous vehicle operations, vehicle platooning has been paid attention. Therefore, this study analyzes the effects of vehicle platooning on the road network. As a large-scale network, Jeju island, one of the cities in Korea was selected to test the platooning arrangement. A macro simulation was developed and calibrated based on the collected traffic counts, and then the network and assigned paths were imported to the micro simulation, which is capable of forming the platoon. The analysis was conducted by changing various parameters of the platooning vehicle. According to the investigation, it can be seen that as the market share of autonomous vehicles increases, delays decrease, and speed increases. Traffic efficiency was improved through platooning in general. It is expected that the results of this study can be used as primary data to develop an optimal traffic flow management strategy for cluster operation. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles (CAVs) are a promising technology that is ready to be deployed in the near future to improve the traffic efficiency and safety as well as environment. Extensive studies have been done to investigate the potential performance of CAVs on freeways, at roundabouts, and conventional intersections. Nevertheless, innovative intersections, as an important component of today's transportation infrastructure, have been seldom investigated in relation to the performance of CAVs. Hence, this research is designed to examine how CAV technologies can influence the performance of a superstreet, one of the popular innovative intersection designs. In this research, the car-following model, platooning, trajectory planning, and adaptive signal control are specified for CAVs and signal controllers in a superstreet. An equivalent conventional intersection with the same lane configurations is also constructed in the simulation environment to make a fair comparison and gain important insights. More importantly, the findings from this research may provide references for studies on other innovative intersections which share similar design characteristics. [ABSTRACT FROM AUTHOR]

In this short paper, we study platooning control of drones using only the information from a camera attached to each drone. For this, we adopt real-time objection detection based on a deep learning model called YOLO (you only look once). The YOLO object detector continuously estimates the relative position of the drone in front, by which each drone is controlled by a PD (Proportional-Derivative) feedback controller for platooning. The effectiveness of the proposed system is shown by indoor experiments with three drones. [ABSTRACT FROM AUTHOR]

A vehicle platoon is a group of vehicles that can travel very close to each other safely at high speed. This paper deals with a system in which vehicles arrive at a station according to a Poisson process. Longer platoons are preferred when minimizing energy consumption, as the more vehicles in the platoon, the lower the total energy consumption. However, forming long platoons increases transport delay as vehicles have to wait longer. We consider a platoon formation policy with a time limit, time-limited policy: Platoon formation process begins with vehicles arriving when the station is empty; It lasts until an exact predetermined time limit has passed. This paper analyzes the platoon size and the energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of reducing the use of radio resources for vehicle-to-vehicle communications in an autonomous platooning scenario. Achieving reliable communications, which is a key element allowing for the tight coordination of platoon vehicles' motion, might be challenging in a case of heavy road traffic. Thus, in this paper, we propose to reduce the number of intra-platoon transmissions required to facilitate the safe autonomous control of vehicle mobility, by analyzing the impact of cars' behaviors (in terms of acceleration changes) on the evolution of the inter-vehicle distance errors within the platoon. We derive formulas representing the relation between the platoon leader's acceleration changes and the evolution of the distance error, velocity difference, and the accelerations for the first pair of vehicles. Furthermore, we propose a heuristic algorithm for selection of the intra-platoon messaging period for each platoon vehicle that minimizes the use of radio resources subject to the safety constraint, represented as the fraction of the total time when emergency braking is activated. The presented simulation results indicate that the proposed approach is capable of ensuring safe platoon operation and simultaneously providing a significant reduction in the use of resources, compared with conventional fixed-period transmission. [ABSTRACT FROM AUTHOR]

The convergence of sensing, computing, communication and control elements drives the traditional point-to-point control systems towards networked control systems. Sampled-data control systems, which focus on the significant interplay between sampling and control, play a critical role in modern networked control systems, including intelligent transportation systems, smart grids, and advanced manufacturing systems. This paper presents a survey of methods and trends in non-uniform sampled-data control systems, where sampling and control actions are performed in an aperiodic manner. First, some fundamental issues of both continuous- and discrete-time sampled-data control systems are discussed. Next, main methods in both continuous-time and discrete-time domains are elaborated, respectively. Then, event-triggered sampling, under which sampling is executed only when the system needs attention, is examined. Typical triggering mechanisms in the existing literature are reviewed and classified into four types according to different threshold functions. Furthermore, two applications in terms of automated vehicle platoons and islanded microgrids are provided to demonstrate that sampled-data control methods are capable to support relevant practical application scenarios. Finally, several challenging issues are envisioned to direct future research. [ABSTRACT FROM AUTHOR]

A novel control technique for the platooning of aerial vehicles is here introduced, and its stability is analyzed. The controller applies a missile guidance law that was initially adapted for path-following and subsequently extended to platooning. The positions of all agents within a platoon employing this controller are estimated by exploiting cooperative localization, and these estimated positions are fed back into the controller. Using simulation, the agents within a platoon are demonstrated to follow their desired path and avoid collision, even in environments with intermittent Global Positioning System signals and limited sensing ranges.

Currently, research on automated vehicles is strongly related to technological advances to achieve a safe, more comfortable driving process in different circumstances. The main achievements are focused mainly on highway and interurban scenarios. The urban environment remains a complex scenario due to the number of decisions to be made in a restrictive context. In this context, one of the main challenges is the automation of the relocation process of car-sharing in urban areas, where the management of the platooning and automatic parking and de-parking maneuvers needs a solution from the decision point of view. In this work, a novel behavioral planner framework based on a Finite State Machine (FSM) is proposed for car-sharing applications in urban environments. The approach considers four basic maneuvers: platoon following, parking, de-parking, and platoon joining. In addition, a basic V2V communication protocol is proposed to manage the platoon. Maneuver execution is achieved by implementing both classical (i.e., PID) and Model-based Predictive Control (i.e., MPC) for the longitudinal and lateral control problems. The proposed behavioral planner was implemented in an urban scenario with several vehicles using the Carla Simulator, demonstrating that the proposed planner can be helpful to solve the car-sharing fleet relocation problem in cities. [ABSTRACT FROM AUTHOR]

Truck platooning consists of one or several trucks driving very closely behind the platoon leader with the help of technology. Platooning reduces fuel consumption, carbon emissions and congestion while increasing road safety and the productivity of trucks and drivers. This article focuses on the advance planning of platoons. First, we study platoon formation from a system-wide optimization perspective. We formalize the underlying optimization problem and we propose exact and approximate solution approaches that appear to perform very well in instances of practical size. Second, we posit that truck platooning is much more likely to develop efficiently among multiple operators. This involves a shift in business relations between freight operators through cost sharing. We make use of cooperative game theory to study cost allocations among players. Our analysis shows that a compromise is needed among existence, stability and computational efficiency. However, we propose cost allocation rules for cooperative platooning games that perform very well in practice with regard to their stability. Finally, we propose an illustrative example based on the settings of the Port of Rotterdam and we provide a series of insights. • We study platoon formation from a system-wide optimization perspective. • We formalize the optimization problem and propose exact and approximate solution approaches. • We make use of cooperative game theory to study cost allocations among players. • We propose an illustrative example based on the Port of Rotterdam. [ABSTRACT FROM AUTHOR]

Due to the battery capacity limitation of battery electric vehicles (BEVs), the importance of minimizing energy consumption has been increasing in recent years. In the mean time, for improving vehicle energy efficiency, platooning has attracted attention of several automakers. Using the connected and automated vehicles (CAVs) technology, platooning can achieve a longer driving range while preserving a closer distance from the preceding vehicle, resulting in the minimization of the aerodynamic force. However, undesired behaviors of human-driven vehicles (HVs) in the platooning group can prohibit the maximization of the energy efficiency. In this paper, we developed a speed planner based on the model predictive control (MPC) to minimize the total platooning energy consumption, and HVs were programmed to maintain a long enough distance from the preceding vehicle to avoid collision. The simulations were performed to determine how HV influences the efficiencies of the platooning group, which is composed of CAVs and HVs together, in several scenarios including the different positions and numbers of the HVs. Test results show that the CAVs planned by our approach reduces energy consumption by about 4% or more than 4% compared to that of the HVs. [ABSTRACT FROM AUTHOR]

Two-lane highways refer to roadways consisting of two lanes in the cross section, one for each direction of travel. Occasionally, passing lanes may be added to one or two sides of the roadway extending the cross section to three or four lanes at those locations. In this entry, two-lane highways strictly refer to roads in rural areas meeting the previous definition and do not include urban and suburban streets.

Convoying or platooning with a fleet of autonomous vehicles, which is denoted as autonomous convoying in this paper, has attracted increasing attention from the research communities, governments, and private sectors in recent years. Autonomous convoying offers immense opportunities due to its potential in enhancing logistical efficiency as well as reducing road incidents/accidents by eliminating human errors due to stress and fatigue. While humans can make complex decisions, involving humans in decision-making processes often causes delays as compared with those of automated machines. Indeed, human errors cause approximately 90% of road accidents and fatalities. Efficient platooning techniques can also reduce fuel consumption and carbon footprints. This paper presents a concise survey on current research and development initiatives in autonomous convoying while critically discussing the underlying techniques and technologies developed in this domain. Implications of autonomous convoying toward different industries are also analyzed and discussed.