Schleiß, Philipp

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  • Publication
    Uncertainty-aware RSS
    ( 2023)
    Carella, Francesco
    ;
    Oleinichenko, Oleg
    ;
    Schleiß, Philipp
    In this preliminary work, the authors present a potential solution on the issue of real time parameter estimation within a safety critical application. When computing the frontal safety distance, each vehicles type requires, in principle, a different safety distances depending on its capability to brake at a greater or lower rate. In order to account for different braking capabilities, an object detection and recognition algorithm must be employed, and thus, some classification uncertainty is introduced in the system. We propose to employ such a solution, in order to maximise the utility of the system by accounting for different vehicle types, while considering the uncertainty, in order to preserve safety.