Fast maneuver recovery from aerial observation: trajectory clustering and outliers rejection

TL;DR

This paper presents a data-driven trajectory clustering method for road user behavior analysis, effectively classifying and filtering trajectories of cars, pedestrians, and cyclists without map data across various traffic scenarios.

Contribution

It introduces a novel clustering approach that separates well-defined trajectories from outliers without relying on map information, applicable to multiple road environments.

Findings

Effective separation of eccentric trajectories from representative ones

Successful classification of cars, pedestrians, and cyclists

Applicable across different intersection and roundabout scenarios

Abstract

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.