空中観測からの高速マニューバ復元：軌道クラスタリングと外れ値除去
Fast maneuver recovery from aerial observation: trajectory clustering and outliers rejection
空中観測データから歩行者・自転車・車の軌道をクラスタリングし、地図情報なしで代表的な軌道クラスを抽出するとともに、外れ値や不完全な軌道を除去する手法を提案した。
著者: Nelson de Moura, Augustin Gervreau-Mercier, Fernando Garrido, Fawzi Nashashibi
分類: cs.AI, cs.CV, cs.RO
原文アブストラクト
The implementation of road user models that realistically reproduce a credible behavior in a multi-agentsimulation is still an open problem. A data-driven approach consists on to deduce behaviors that may exist in real situation to obtain different types of trajectories from a large set of observations. The data, and its classification, could then be used to train models capable to extrapolate such behavior. Cars and two different types of Vulnerable Road Users (VRU) will be considered by the trajectory clustering methods proposed: pedestrians and cyclists. The results reported here evaluate methods to extract well-defined trajectory classes from raw data without the use of map information while also separating ''eccentric'' or incomplete trajectories from the ones that are complete and representative in any scenario. Two environments will serve as test for the methods develop, three different intersections and one roundabout. The resulting clusters of trajectories can then be used for prediction or learning tasks or discarded if it is composed by outliers.
