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

Carletti, Carlos Mateo Risma; Casetti, Claudio; Härri, Jérôme; Risso, Fulvio

doi:10.1109/wimob58348.2023.10187883

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Research on the LDM can be divided into two main branches: research that built on top of the existing standardized structure, and research that did not. Regarding the former, the most notable works are from Hideki Shimada et al [7], who followed ETSI guidelines to implement the LDM; Nicole El Zoghby et al [8], who increased the Field of View (FoV) of communicating vehicles by fusing the fourth layer of shared LDMs; Fatima Almheiri et al [9], who paved the way to higher levels of AD by extending the LDM with additional layers for information gathered externally; Carlos Mateo Risma Carletti et al [10], who extended the standard LDM with their Platoon LDM, a shared database system that efficiently manages data within autonomous vehicle platoons, reducing redundancy and computational demands. A greater amount of research has instead focused on modifying and redefining the standardized LDM.…”

Section: Introductionmentioning

confidence: 99%

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

Taddei,

Visintainer,

Stoffella

et al. 2024

Sustainability

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Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process.

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