Connected cars are becoming more common. With the development of multi-access edge computing (MEC) for low-latency applications, it will be possible to manage the cooperative adaptive cruise control (CACC, also known as platooning) of such vehicles from the edge of cellular networks. In this paper, we present a controller that carries out platooning from the network edge by adapting to varying network conditions. We incorporate a mechanism in the controller that allows vehicles to switch to automated cruise control when delays exceed safety thresholds, and switches back to platooning when the delays are sufficiently low to support it. We also formulate the problem of maintaining a low-latency connection in the presence of high mobility through migration and propose a Q-Learning algorithm to solve this problem. We finally propose an Asynchronous Shared Learning scheme that enables multiple migration agents to cooperate, in order to expedite the convergence of migration policies. Compared to state-of-the-art migration techniques, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, and ameliorates statistical dispersion.

Driving under influence: Robust controller migration for MEC-enabled platooning / Ayimba, Constantine; Segata, Michele; Casari, Paolo; Mancuso, Vincenzo. - In: COMPUTER COMMUNICATIONS. - ISSN 0140-3664. - 2022, 194:(2022), pp. 135-147. [10.1016/j.comcom.2022.07.014]

Driving under influence: Robust controller migration for MEC-enabled platooning

Segata, Michele;Casari, Paolo;
2022-01-01

Abstract

Connected cars are becoming more common. With the development of multi-access edge computing (MEC) for low-latency applications, it will be possible to manage the cooperative adaptive cruise control (CACC, also known as platooning) of such vehicles from the edge of cellular networks. In this paper, we present a controller that carries out platooning from the network edge by adapting to varying network conditions. We incorporate a mechanism in the controller that allows vehicles to switch to automated cruise control when delays exceed safety thresholds, and switches back to platooning when the delays are sufficiently low to support it. We also formulate the problem of maintaining a low-latency connection in the presence of high mobility through migration and propose a Q-Learning algorithm to solve this problem. We finally propose an Asynchronous Shared Learning scheme that enables multiple migration agents to cooperate, in order to expedite the convergence of migration policies. Compared to state-of-the-art migration techniques, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, and ameliorates statistical dispersion.
2022
Ayimba, Constantine; Segata, Michele; Casari, Paolo; Mancuso, Vincenzo
Driving under influence: Robust controller migration for MEC-enabled platooning / Ayimba, Constantine; Segata, Michele; Casari, Paolo; Mancuso, Vincenzo. - In: COMPUTER COMMUNICATIONS. - ISSN 0140-3664. - 2022, 194:(2022), pp. 135-147. [10.1016/j.comcom.2022.07.014]
File in questo prodotto:
File Dimensione Formato  
Platooning_EXT.pdf

embargo fino al 02/10/2024

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Creative commons
Dimensione 2.1 MB
Formato Adobe PDF
2.1 MB Adobe PDF   Visualizza/Apri
1-s2.0-S0140366422002626-main (1).pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.73 MB
Formato Adobe PDF
1.73 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/351263
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 3
social impact