To improve the transportation system and to make it effective, the ultimate resolution was to distribute the self-driving vehicles between multiple users. During unwanted times, without any human efforts, autonomous vehicle owners distribute their vehicles to other users. But, it requires the user’s places, locations and route information being published, which raises severe privacy issues. In this project we have developed a privacy preserving dynamic scheduling system for continuous-sharing of self-driving vehicles. Initially, we find the attainable user for each of the Autonomous Vehicles (AV) by designing a matching scheme. Then, we developed a scheme using different ways of assigning the requester was implemented to the AV on variable system attributes. More conscientiously, using a set of IDs or Intermediate Destination locations, our scheme enables a semi-trusted matching server to map the requester and the owners. Also, the provider and the requester can distribute their specification of the trip and route, if the service can be given to the requester effectively. All of the calculations for the verification of availability of the given service is purely done in the semi-trusted server. And finally, the different scheduling schemes are evaluated based on their effectiveness for this system.


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    Titel :

    Privacy-Preserving Dynamic Task Scheduling for Autonomous Vehicles


    Weitere Titelangaben:

    Algorithms for Intelligent Systems


    Beteiligte:
    Mathur, Garima (Herausgeber:in) / Bundele, Mahesh (Herausgeber:in) / Lalwani, Mahendra (Herausgeber:in) / Paprzycki, Marcin (Herausgeber:in) / Muthurajkumar, S. (Autor:in) / Karthikeyan, C. Ajay (Autor:in) / Pradeep, K. (Autor:in) / Hariharan, A. (Autor:in)


    Erscheinungsdatum :

    14.02.2022


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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