A Holistic Approach to the Energy-Efficient Smoothing of Traffic via Autonomous Vehicles

Hayat, Amaury; Gong, Xiaoqian; Truong, Sydney; McQuade, Sean T.; Kardous, Nicolas; Keimer, Alexander; You, Yiling; Albeaik, Saleh; Vinistky, Eugene; Arnold, Paige; Monache, Maria Laura Delle; Bayen, Alexandre M.; Seibold, Benjamin; Sprinkle, Jonathan; Work, Daniel B.; Piccoli, Benedetto

doi:10.1007/978-3-030-84474-5_10

Cited by 6 publications

(1 citation statement)

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“…As expected, the driving speed is reduced for all approaches when the scenario becomes more crowded due to natural traffic effects. The effect is generally worse for non-platooning approaches due to larger safety gaps between vehicles, resulting in stopand-go traffic [51]. For all platooning approaches, the speed is similar to the smallest speed window as options are limited.…”

Section: E System-level Metrics (Macroscopic)mentioning

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: E System-level Metrics (Macroscopic)mentioning

confidence: 99%