The Influence of Electrode for the Electrochemical Dyeing of Cotton Fabric

Hou, Yinyan; Jiang, Yingze; Huang, Guinian; Niu, Yanping; Hu, Yujiang; Wei, Jiaqi; Xiong, Wei

doi:10.3233/atde220505

Cited by 1 publication

(1 citation statement)

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“…Thus, the platooning benefits for the individual vehicles can be optimized without giving up to much of the own requirements instead of system-level aspects such as traffic flow. Therefore, solving the challenge of vehicle-toplatoon assignments with respect to these aspects is the next important step towards large-scale deployment of platooning [18].…”

Section: Introductionmentioning

confidence: 99%

Poster: Scalable Simulation of Platoon Formation Maneuvers with PlaFoSim

Heinovski

Buse

Dressler

2021

2021 IEEE Vehicular Networking Conference (VNC)

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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.

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Section: Introductionmentioning

confidence: 99%