Fast maneuver recovery from aerial observation: trajectory clustering and outliers rejection
<|reference_start|>Fast maneuver recovery from aerial observation: trajectory clustering and outliers rejection: 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.<|reference_end|>
@article{de moura2024fast,
    title={Fast maneuver recovery from aerial observation: trajectory clustering
  and outliers rejection},
    author={Nelson de Moura (ASTRA), Augustin Gervreau-Mercier (ASTRA), Fernando
  Garrido (ASTRA), Fawzi Nashashibi (ASTRA)},
    journal={2024 IEEE Intelligent Vehicles Symposium (IV), ITS-IEEE, Jun 2024,
  Jeju, South Korea},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02863},
    primaryClass={cs.AI cs.CV cs.RO}
}