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

A Realistic Cyclist Model for SUMO Based on the SimRa Dataset

Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on simulation tools such as SUMO which allow them to study the effects of construction changes before implementing them. Similarly, studies of vulnerable road users, here cyclists, also use such models to assess the performance of communication-based road traffic safety systems. The cyclist model in SUMO, however, is very imprecise as SUMO cyclists behave either like slow cars or fast pedestrians, thus, casting doubt on simulation results for bicycle traffic. In this paper, we analyze acceleration, velocity, and intersection left-turn behavior of cyclists in a large dataset of real world cycle tracks. We use the results to derive an improved cyclist model and implement it in SUMO.Comment: Accepted for the 20th Mediterranean Communication and Computer Networking Conference (MedComNet 2022

Multi-Technology Cooperative Driving: An Analysis Based on PLEXE

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version of PLEXE, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naive approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments

Investigating strategies for building platoons of cars

Wir untersuchen das Problem der Formierung von Platoons und versuchen, Reisezeit und Kraftstoffverbrauch von Fahrzeugen durch geeignete Platoon-Zuordnungen zu optimieren. Das generelle-Platooning Konzept, bei dem Fahrzeuge mit sehr geringem Sicherheitsabständen hintereinander fahren und sogenannte Road-Trains formen, wurde bereits im Detail untersucht, unter anderem in realen Testläufen mit echten Kraftfahrzeugen. Pilotprojekte haben gezeigt, dass Platooning sowohl signifikant den Kraftstoffverbrauch senken als auch die Ausnutzung der Kapazität von Straßen steigern kann. Während sich aktuelle Studien weitgehend damit beschäftigen, die Verlässlichkeit der benötigten Kommunikationsprotokolle zu verbessern, um konstante Abstände einzuhalten und ein sicheres Verhalten zu erreichen, wird ein wichtiger Aspekt vernachlässigt: Das Problem der Zuordnung von Kraftfahrzeugen zu Platoons. Diese Zuordnung allerdings stark von den Möglichkeiten einzelner Fahrzeuge (zum Beispiel maximale Beschleunigung oder Geschwindigkeit) und den Präferenzen des Fahrers (zum Beispiel gewünschte Reisegeschwindigkeit und dem Kompromiss zwischen Reisedauer und Kraftstoffverbrauch) ab. Um das Problem der Zuordnung zu untersuchen, formulieren wir ein Optimierungsproblem und entwickeln zwei Lösungsansätze, einen zentralisierten und einen verteilten. Mit Hilfe einer umfangreichen Simulationsstudie zeigen wir, dass unsere Ansätze nicht nur Platoons formen, sondern auch die individuellen Anforderungen der Fahrzeuge und Fahrer berücksichtigen. Dabei hat sowohl die Wahl des Ansatzes als auch die Bereitschaft zur Abweichung von individuellen Präfenzen einen großen Einfluss auf die Platoon-Zuordnungen. Anhand der ausgewählten Metriken führt der verteilte Ansatz zu einem besseren Ergebnis ...We study the problem of platoon formation, trying to optimize traveling time and fuel consumption based on-car-to platoon assignments. The general concept of platooning, i.e., cars traveling in form of a road train with minimized safety gaps, has been studied in depth and we see first field trials on the road. A number of projects already convinced the public that platooning helps substantially reducing fuel consumption, along with emissions, and offers better road utilization. Currently, most research focuses on improved reliability of the necessary communication protocols to achieve perfect string stability with guaranteed safety measures. One aspect, however, remained unexplored: the problem of assigning cars to platoons. Based on the capabilities of individual cars (e.g., max. acceleration or speed) and preferences of the driver (e.g., min/max. traveling speed, preference on travel time vs. fuel consumption), the assignment decision will be different. We formulate an optimization problem and develop a set of protocols (centralized and distributed) to support platoon formation. In an extensive series of simulation experiments, we show that our protocols not just help forming platoons, but also take care of the individual requirements of cars and drivers. The selection of the formation approach as well as the willingness to compromise influences the platoon assignments. Considering the selected metrics, a better overall performance can be achieved using the distributed approach, e.g., longer platoons can be formed and more fuel can be saved.vorgelegt von Julian Heinovski angefertigt in der Fachgruppe Distributed Embedded Systems (CCS Labs), Heinz Nixdorf Institut, Universität Paderborn ; Betreuer: Prof. Dr.-Ing. habil. Falko Dressler, Gutachter: Prof. Dr.-Ing. habil. Falko Dressler, Prof. Dr.-Ing. Heiko HamannTag der Abgabe: 17.09.2018Universität Paderborn, Masterarbeit, 201

Centralized Model-Predictive Control with Human-Driver Interaction for Platooning

Cooperative adaptive cruise control presents an opportunity to improve road transportation through increase in road capacity and reduction in energy use and accidents. Clever design of control algorithms and communication systems is required to ensure that the vehicle platoon is stable and meets desired safety requirements. In this paper, we propose a centralized model predictive controller for a heterogeneous platoon of vehicles to reach a desired platoon velocity and individual inter-vehicle distances with driver-selected headway time. In our approach, we allow for interruption from a human driver in the platoon that temporarily takes control of their vehicle with the assumption that the driver will, at minimum, obey legal velocity limits and the physical performance constraints of their vehicle. The finite horizon cost function of our proposed platoon controller is inspired from the infinite horizon design. To the best of our knowledge, this is the first platoon controller that integrates human-driven vehicles. We illustrate the performance of our proposed design with a numerical study.Comment: 15 pages, 5 figure