Securing traffic flows in internet of vehicles (IoV) environments for connected and automated vehicles (CAVs) is a critical task as it should be done in real-time to allow vehicles’ controllers engagement on time. In this paper, the security of CAV communication at merging points is studied, the insecure vehicle communication is analysed in terms of the possible security threats and consequences, and security goals are then identified to protect the environment. We present a network topology that improves the availability of the system and propose a high-level design of a vehicle authentication protocol based on public key cryptography to authenticate vehicles. Simulation and analysis of the cryptographic functions are done to choose the best fit for vehicle communication, where Rivest-Shamir-Adleman (RSA)-2048 algorithms provide faster and more efficient computations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Security Analysis of Merging Control for Connected and Automated Vehicles




    Publication date :

    2022-06-05


    Size :

    513797 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Cooperative Ramp Merging Control for Connected and Automated Vehicles

    Manjiang, Hu / Jin, Huang / Tianchuang, Meng et al. | SAE Technical Papers | 2020


    Cooperative Ramp Merging Control for Connected and Automated Vehicles

    Tianchuang, Meng / Biao, Xu / Xiaohui, Qin et al. | British Library Conference Proceedings | 2020


    Automated on‐ramp merging control algorithm based on Internet‐connected vehicles

    Wang, Yunpeng / E, Wenjuan / Tang, Wenzhong et al. | Wiley | 2013

    Free access

    Automated on-ramp merging control algorithm based on Internet-connected vehicles

    Wang, Yunpeng / E, Wenjuan / Tang, Wenzhong et al. | IET | 2013

    Free access

    Optimal Control-Based Eco-Ramp Merging System for Connected and Automated Vehicles

    Zhao, Zhouqiao / Wu, Guoyuan / Wang, Ziran et al. | IEEE | 2020