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... A taxonomy on the objectives that may be considered for platooning is provided in [30]. In [31] the authors present infrastructure-aided platoon management strategies for highways and urban areas, while in [32] the authors propose both a centralized and decentralized high level platoon coordinator, which updates the parameters of the low-level platoon controller based on traffic conditions. While most platoon coordination methods focus on traffic flow management and managing platoon merging and splitting requests, our proposed coordinator is specifically designed to mitigate the effects of a malicious at-tack on the communication channels. ...
This paper presents a novel distributed vehicle platooning control and coordination strategy. We propose a distributed predecessor-follower CACC scheme that allows to choose an arbitrarily small inter-vehicle distance while guaranteeing no rear-end collisions occur, even in the presence of undetected cyber-attacks on the communication channels such as false data injection. The safety guarantees of the CACC policy are derived by combing a sensor-based ACC policy that explicitly accounts for actuator saturation, and a communication-based predictive term that has state-dependent limits on its control authority, thus containing the effects of an unreliable communication channel. An undetected attack may still however be able to degrade platooning performance. To mitigate it, we propose a tailored Kalman observer-based attack detection algorithm that initially triggers a switch from the CACC policy to the ACC policy. Subsequently, by relying on a high-level coordinator, our strategy allows to isolate a compromised vehicle from the platoon formation by reconfiguring the platoon topology itself. The coordinator can also handle merging and splitting requests. We compare our algorithm in simulation against a state of the art distributed MPC scheme and we extensively test our full method in practice on a real system, a team of scaled-down car-like robots. Furthermore, we share the code to run both the simulations and robotic experiments.
... A platoon at a minimum consists of two vehicles where one vehicle closely follows the one in front of it. Here, each vehicle can be driven autonomously or drivers are assisted to keep a safe distance and stay inside the lane limits, including autonomous braking [27]. As shown in Fig. 3, a standard platoon is made up of a PL and a PM, with the PL leading and the PM following using VANET. ...
The Internet of Things (IoT) facilitates vehicle communication using wireless networks to improve safety, mobility, and efficiency in transportation. Autonomous vehicles (AVs) can use IoT to form a platoon and travel cooperatively to a common destination as connected autonomous vehicles (CAVs). In our previous work, we demonstrated platoon negotiation and formation between two vehicles or IoT nodes using Dedicated Short Range Communication (DSRC)-based messages only. This paper extends these algorithms to support multi-vehicle platoon negotiation and formation using DSRC messages for AVs. To achieve this, once two vehicles negotiate and form a platoon, the platoon member (PM) sends a platoon-complete negotiation to the platoon leader (PL) after the string stability is achieved. Once PL receives this message, it makes itself available to receive negotiations from nearby vehicles who are willing to join its platoon. We modified our platoon-ready, pre-negotiation, negotiation resolver, and platoon joiner algorithms from our prior work. Also, PL maintains the PM vehicle IDs and their position so that it can assign a local leader to the future vehicles joining the platoon. Now, the vehicle willing to platoon negotiates with PL to check if their destinations match. If a common destination is found, the new vehicle further negotiates with PL in a series of transactions to join the existing platoon. During these negotiations, PL assigns the last joined PM as a local leader to this newly joined vehicle to follow. Then, PL adds the new PM vehicle ID and its position to the list. Assigning a local leader not only increases the range of the platoon but also decreases the delay in the message exchange. We demonstrated the above algorithms in the CARLA simulator by extending them to support IoT connectivity and platooning. We validated the algorithms by conducting experiments with three-vehicle platooning scenarios.
... Other studies divide the highway into sections and coordinate platooning within those sections with local centralized controllers. Krupitzer et al. [37] extend their centralized platoon coordination system with subsystems that individually coordinate vehicles within sections in a regional planner-like approach. While only limited details are provided, their approach uses Vehicle-to-Infrastructure (V2I) communication for exchanging vehicles' desired driving speed and route data with the coordination system, which forms platoons among vehicles with a similar route or destination. ...
Platooning is a promising cooperative driving application for future intelligent transportation systems. In order to assign vehicles to platoons, some algorithm for platoon formation is required. Such vehicle-to-platoon assignments have to be computed on-demand, e.g., when vehicles join or leave the freeways. In order to get best results from platooning, individual properties of involved vehicles have to be considered during the assignment computation. In this paper, we explore the computation of vehicle-to-platoon assignments as an optimization problem based on similarity between vehicles. We define the similarity and, vice versa, the deviation among vehicles based on the desired driving speed of vehicles and their position on the road. We create three approaches to solve this assignment problem: centralized solver, centralized greedy, and distributed greedy, using a Mixed Integer Programming solver and greedy heuristics, respectively. Conceptually, the approaches differ in both knowledge about vehicles as well as methodology. We perform a large-scale simulation study using PlaFoSim to compare all approaches. While the distributed greedy approach seems to have disadvantages due to the limited local knowledge, it performs as good as the centralized solver approach across most metrics. Both outperform the centralized greedy approach, which suffers from synchronization and greedy selection effects.Since the centralized solver approach assumes global knowledge and requires a complex Mixed Integer Programming solver to compute vehicle-to-platoon assignments, we consider the distributed greedy approach to have the best performance among all presented approaches.
... The platooning in smart cities have some benefits, such as efficient roads usage, time-saving through route optimization, and traffic minimization in peak times. In [40] a traffic management solution for combining platooning on highways and urban areas has been presented. Also, the differences between these two type of platooning has been analysed. ...
Platooning is an application where a group of vehicles move one after each other in close proximity, acting jointly as a single physical system. The scope of platooning is to improve safety, reduce fuel consumption, and increase road use efficiency. Even if conceived several decades ago as a concept, based on the new progress in automation and vehicular networking platooning has attracted particular attention in the latest years and is expected to become of common implementation in the next future, at least for trucks.
The platoon system is the result of a combination of multiple disciplines, from transportation, to automation, to electronics, to telecommunications. In this survey, we consider the platooning, and more specifically the platooning of trucks, from the point of view of wireless communications. Wireless communications are indeed a key element, since they allow the information to propagate within the convoy with an almost negligible delay and really making all vehicles acting as one. Scope of this paper is to present a comprehensive survey on connected vehicles for the platooning application, starting with an overview of the projects that are driving the development of this technology, followed by a brief overview of the current and upcoming vehicular networking architecture and standards, by a review of the main open issues related to wireless communications applied to platooning, and a discussion of security threats and privacy concerns. The survey will conclude with a discussion of the main areas that we consider still open and that can drive future research directions.
... Recently, researchers begin increasingly to work on the efficient assignment of vehicles to platoons; however, obeying individual constraints of drivers have been left out of scope. In [105], we present our concept for a platooning coordination approach. In contrast to existing approaches (see the overview in [106]), we integrate individual preferences of drivers, focus on multi-objective solutions, and provide individualised decisions which platoon to join. ...
Context
Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions.
Objective
The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system.
Method
In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes.
Results
This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems.
Conclusion
The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
... We previously discussed the concept of such an approach (cf. [101] Samuel Kounev is a professor and chair of software engineering at the University of Würzburg. His research is focused on the engineering of dependable and efficient software systems, systems benchmarking and experimental analysis; as well as autonomic and self-aware computing. ...
In the recent past, platooning evolved into an attractive cooperative driving technology, broadly discussed in research and practice. Vehicles in platoons use cooperative adaptive cruise control to drive at close distances to each other. Platooning (i) increases the capacity of the street by a factor of 2; (ii) reduces the fuel consumption and emissions by up to 20%; and (iii) has social implications as it increases driver comfort and safety. As platooning research progresses, platooning coordination becomes a major research focus. The coordination of platoons, including the assignment of vehicles to platoons, the management of inter- and intra-platoon interactions, and the coordination of interactions with other vehicles is an important step towards an effective usage of platooning in practice. Based on a literature review of 1,600 papers, this survey provides an overview of state of the art in platooning coordination research for both cars and trucks. In this paper, we present a novel taxonomy for platooning coordination and classify existing approaches. We use the results of the literature review to discuss challenges and outline avenues for future work such as multi-objectiveness and individualisation.
... First, platooning options have to be searched. Available platoons and potential platooning participants are identified via vehicle-to-vehicle or vehicle-toinfrastructure communication (e.g., [12]). Here, literature divides between spontaneously joining the next platoon and identification of the best available platoon [13]. ...
... In addition, platooning for commercial vehicles does not only concern the single citizen, but rather the commons, as pollution and congestion affect the society as a whole. In [12], we propose a concept for a self-organized approach for platooning coordination that takes a balancing of objectives on the three levelsvehicle, platoon, and global -into account and tries to achieve this by relying on the principles of self-adaptive systems [82]. Our study poses the basis for research in this direction by reviewing the literature on objectives and influencing factors which must be taken into account in the design of coordination algorithms for efficient assignment of vehicles to platoons; hopefully making the initial design phase simpler. ...
The technical maturity of autonomous driving enables the discussion of beneficial use cases to leverage its full potential. In this paper, we target one such use case: Platooning is the efficient convoying of vehicles by making use of self-driving capabilities and inter-vehicle communication. Many advantages arise from grouping vehicles in platoons with a small inter-vehicle distance, such as energy savings, congestion reduction, and safety improvements. However, due to the diversity of involved stakeholders, numerous objectives have to be balanced to leverage the full potential of platooning. Furthermore, these objectives also depend on various factors that influence their optimization. The vast majority of existent literature only targets a subset of related objectives and underlying factors. This paper provides an overview which categorizes objectives and influencing factors. Additionally, metrics for the evaluation of objective attainment are proposed.
... Especially the lead vehicle experiences reduced fuel savings and, in some platooning approaches, its driver has to drive manually whereas other vehicles can follow in a self-driving mode; hence, those drivers do not have to control their vehicles. Consequently, the coordination of platooning, including the assignment of vehicles to platoons, is a challenging task as it represents a multi-level, multiobjective optimization problem [8]. As each of the vehicles act as a self-adaptive system [9], the coordination requires incentives and compensation to convince vehicles to participate in platooning. ...
... Some approaches rely on communicating with vehicles within the communication range and autonomously decide which platoon to join. We follow another approach [5,8]: A central recommendation system supports the platooning process and provides recommendations for suitable platoons. However, we assume that vehicles reason autonomously which platoon to join or when to leave a platoon. ...
... Further, the assignment of the platoons, i.e., the integrated parts of the subsystems, is not static, but rather dynamic. Consequently, it is possible to self-improve the assignment, e.g., to reflect individual preferences or constraints better or optimize the air drag reduction [8]. Accordingly, platooning coordination represents an example for a SISSY system from the vehicular traffic domain [4]. ...
The demand for passenger and freight transportation has grown sharply over the last decades and will triple by 2050. This also dramatically impacts the environment as traffic is one of the primary sources of CO2 emission. Platooning, which is defined as driving automated vehicles in convoys with minimal inter-vehicle distance enabled by vehicular communication, offers several benefits like energy savings due to slipstream effects, homogenization of traffic flows, increased capacity of streets, as well as improved safety due to communication. However, as the vehicles at inner positions of a platoon experience higher benefits than the first and last vehicle, the compensation of the effects of different positions in a platoon have to be taken into account when integrating vehicles in a platoon. In this paper, we discuss several strategies on how to incentivize vehicles to participate in platooning based on directly or indirectly compensating vehicles for fewer benefits resulting from the integration into a platooning system. Our considerations integrate ideas from research about altruism, social sciences, organ donation, task scheduling on computers, as well as professional cycling sports. Our experiments show that the time spent in a position with negative effects is split equally among all vehicles when using our mechanisms. Additionally, we found that characteristics of the environment – e.g., the number of lanes or traffic density – impact the performance of the compensation mechanisms. We further provide a discussion of the identified challenges and on how to apply our proposed ideas to other systems which require self-integration.
... In this way, we can enhance the scalability of computing services for vehicles, and reduce the cost of using cloud resources. Platooning is basically one of the key technologies of autonomous driving in which a fleet of vehiclescomposed of a head vehicle "leader" and a number of following vehicles "followers"with only a few meters between them [6]. This feature has brought a number of advantages to the transportation industry including significant safety, energy efficiency, and cost benefits. ...
Intelligent connected vehicles equipped with wireless sensors, intelligent control system, and communication devices are expected to commercially launch and emerge on road in short-term. These smart vehicles are able to partially/fully drive themselves; collect data from sensors, make and execute decisions based on that data; communicate with other vehicles, pedestrians, and nodes installed on the road; and provide infotainment and value-added services, such as broadband transmission of ultra-high definition video, files/apps downloading and uploading, online gaming, access to social media, audio/video conference streaming (office-in-car), live TV streaming, etc.; and so on. In addition, it is also possible for autonomous vehicles to form a "platoon" on road; maintaining close proximity in order to reduce the consumption of fuel and/or emission of gas, decrease costs, increase safety, and enhance the efficiency of the legacy transportation system. These emerging vehicular applications demand a large amount of computing and communication capacity to excel in their compute-intensive and latency-sensitive tasks. Based on these facts, the authors of this paper presented a visionary concept -- "platoon-assisted vehicular cloud" -- that exploits underutilized resources in platoons to augment vehicular cloud aiming to provide cost-effective and on-demand computing resources. Moreover, the authors presented three potential scenarios and explained the exploitation of platoon resources and roadside infrastructure to facilitate new applications. Besides system design, the paper did also summarize a number of open research challenges with the purpose of motivating new advances and potential solutions to this field.
... Platooning does not only offer fuel-saving through slipstream effects, but it also helps to organize the traffic through the homogenization of travel velocities and improving the capacity of streets. Usually, platooning is proposed for highways; however, in [2], we propose a hybrid concept for platooning in cities. The platooning concept is envisioned to create virtual air traffic lanes, which are dynamically planned with the help of platooning derived rules and allow the safe coordinated flight of UAVs. ...
In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.
