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Ajánlatkérések feldolgozásának a folyamatfejlesztése egy légi áruszállítással foglalkozó cégnél, légitársasági képviseletként, az Air Hungaro Cargo Kft.-nél
No full textA Globális válsághelyzetek hatásai, megoldásai vezetői döntések és számvitel szempontjából
No full textA macskák gyerek és felnőtt szemmel – Macskakarakterek gyermeki és felnőtt értelmezéseinek összevetése animációs filmekben
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Adversarial Localization Algorithms in Indirect Vehicle-to-Vehicle Communication
Get PDFCommunicating autonomous vehicles (CAVs) can obtain direct measurements from their sensors or indirectly receive them via Vehicle-to-Vehicle (V2V) communication. As the CAVs are expected to share a part of their measurements, it can naturally pose a privacy threat by possibly revealing the route of the sender vehicle. Consequently, we shall assess the risks of sharing a dataset that is a mixture of direct and indirect measurements. However, a wide variety of papers focus on localization attacks for direct measurements; incorporating indirect measurements opens a new horizon for these researches. In this paper, we analyze a couple of localization algorithms for mixture datasets with applicable performance metrics. We have evaluated the algorithms in an Eclipse SUMO-based simulation. We consider these results as the baseline of future research
