GNG-based Clustering of Risk-aware Trajectories into Safe Corridors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359519" target="_blank" >RIV/68407700:21230/22:00359519 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-15444-7_9" target="_blank" >https://doi.org/10.1007/978-3-031-15444-7_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-15444-7_9" target="_blank" >10.1007/978-3-031-15444-7_9</a>

Result language

angličtina

Original language name

GNG-based Clustering of Risk-aware Trajectories into Safe Corridors

Original language description

Personal air transportation on short distances is a promising trend in modern aviation, raising new challenges as flying in low altitudes in highly populated environments induces additional risk to people and properties on the ground. Risk-aware planning can mitigate the risk by preferring flying above low-risk areas such as rivers or brownfields. Finding such trajectories is computationally demanding, but they can be precomputed for areas that are not changing rapidly and form a planning roadmap. The roadmap can be utilized for multi-query trajectory planning using graph-based search. However, a quality roadmap is required to provide a low-risk trajectory for an arbitrary query on a risk-aware trajectory from one location to another. Even though a dense roadmap can achieve the quality, it would be computationally demanding. Therefore, we propose to cluster the found trajectories and create a sparse roadmap of safe corridors that provide similar quality of risk-aware trajectories. In this paper, we report on applying Growing Neural Gas (GNG) in estimating the suitable number of clusters. Based on the empirical evaluation using a realistic urban scenario, the results suggest a significant reduction of the computational burden on risk-aware trajectory planning using the roadmap with the clustered safe corridors.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

ISBN

978-3-031-15443-0

ISSN

2367-3370

e-ISSN

2367-3389

Number of pages

11

Pages from-to

87-97

Publisher name

Springer, Cham

Place of publication

—

Event location

Prague

Event date

Jul 6, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000892398300009