Platooning is a popular vehicular application for autonomous driving on which the Platoon Leader (PL) manages all maneuvers using context information from Vehicle-to-Vehicle (V2V) messages. However, redundant context information from nearby vehicles in the platoon can increase computational costs for the PL. To solve this issue, vehicular micro-clouds can be formed to enable collective data processing and aggregation, thus reducing the PL's perception workload. The proposed solution, called Platoon Local Dynamic Map (P-LDM), creates a single database of context information, distributing the data aggregation load among all members of the platoon. Simulation results evaluate the effectiveness of the proposed solution and compare it to typical Cooperative Perception mechanisms.

Platoon-Local Dynamic Map: Micro cloud support for platooning cooperative perception / RISMA CARLETTI, CARLOS MATEO; Casetti, Claudio; Härri, Jérôme; Risso, Fulvio. - (2023), pp. 405-410. (Intervento presentato al convegno VN4RRSR’23: International Workshop on Vehicular Networks for Risk Reduction and Safety Related Systems tenutosi a Montreal, QC (CAN) nel 21-23 June 2023) [10.1109/WiMob58348.2023.10187883].

Platoon-Local Dynamic Map: Micro cloud support for platooning cooperative perception

Carlos Mateo Risma Carletti;Claudio Casetti;Fulvio Risso
2023

Abstract

Platooning is a popular vehicular application for autonomous driving on which the Platoon Leader (PL) manages all maneuvers using context information from Vehicle-to-Vehicle (V2V) messages. However, redundant context information from nearby vehicles in the platoon can increase computational costs for the PL. To solve this issue, vehicular micro-clouds can be formed to enable collective data processing and aggregation, thus reducing the PL's perception workload. The proposed solution, called Platoon Local Dynamic Map (P-LDM), creates a single database of context information, distributing the data aggregation load among all members of the platoon. Simulation results evaluate the effectiveness of the proposed solution and compare it to typical Cooperative Perception mechanisms.
2023
979-8-3503-3667-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2982322