Feudalistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability

Cooperative intelligent transportation systems (C-ITSs) such as platooning rely on a robust and timely network that may not always be available in sufficient quality. Out of the box hybrid networks only partly eliminate shortcomings: mutual interference avoidance, data load balancing, and data dissemination must be sophisticated. Lacking network quality may lead to safety bottlenecks that require that the distance between the following vehicles be increased. However, increasing gaps result in efficiency loss and additionally compromise safety as the platoon is split into smaller parts by traffic: maneuvers, e.g., cut-in maneuvers bear safety risks, and consequently lower efficiency even further. However, platoons, especially if they are very long, can negatively affect the flow of traffic. This mainly applies on entry or exit lanes, on narrow lanes, or in intersection areas: automated and non-automated vehicles in traffic do affect each other and are interdependent. To account for varying network quality and enable the coexistence of non-automated and platooned traffic, we present in this paper a new concept of platooning that unites ad hoc—in form of IEEE 802.11p—and cellular communication: feudalistic platooning. Platooned vehicles are divided into smaller groups, inseparable by surrounding traffic, and are assigned roles that determine the communication flow between vehicles, other groups and platoons, and infrastructure. Critical vehicle data are redundantly sent while the ad hoc network is only used for this purpose. The remaining data are sent—relying on cellular infrastructure once it is available—directly between vehicles with or without the use of network involvement for scheduling. The presented approach was tested in simulations using Omnet++ and Simulation of Urban Mobility (SUMO)

L-Platooning: A Protocol for Managing a Long Platoon with DSRC

Vehicle platooning is an automated driving technology that enables a group of vehicles to travel very closely together as a single unit to improve fuel efficiency and driver safety and reduces CO2 emission. The significant benefits of platooning attracted huge interests from academia and industry, especially from logistics companies for utilizing platoons of "long-body" trailer trucks because of the huge cost savings. In this paper, we demonstrate that existing DSRC-based platooning solutions, however, fail to support formation of such "long" platoons consisting of typical trailer trucks because of the limited communication range of DSRC. To address this problem, we propose L-Platooning, the first platooning protocol that enables seamless, reliable, and rapid formation of a long platoon. We introduce a novel concept called Virtual Leader that refers to a vehicle that acts like a platoon leader to extend the coverage of the original platoon leader. A virtual leader election algorithm is developed to effectively designate a virtual leader based on the novel metric called the Virtual Leader Quality Index (VLQI) which quantifies the effectiveness of a vehicle serving as a platoon leader. We also develop mechanisms for L-Platooning to support the vehicle join and leave maneuvers specifically for a long platoon. Through extensive simulations using the combination of Veins (Plexe) and SUMO, we demonstrate that L-Platooning enables long-body trailer trucks to form a long platoon effectively and maintain the desired inter-vehicle distance precisely. We also show that L-Platooning handles seamlessly the vehicle join and leave maneuvers for a long platoon.Comment: Published in IEEE Transactions on Intelligent Transportation System

Where to Decide? Centralized vs. Distributed Vehicle Assignment for Platoon Formation

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