The 2023 IEEE Vehicular Networking Conference (VNC) seeks to bring together researchers, professionals, and practitioners to present and discuss recent developments and challenges in vehicular networking, connected & automated driving, and other applications. Topics of interest include, but are not limited to:

Connectivity

    Radio technologies for vehicular networks (e.g., channel measurements, propagation models, antenna design)
    V2X communication technologies, including DSRC, C-V2X, 5G/6G, and emerging technologies like VLC, mmWave, THz, or massive MIMO, beamforming
    Networking, transport and QoS management for vehicular networks
    In-vehicle communication and networking systems

Architecture and System Design

    Heterogeneous vehicular networking (e.g., multi-radio, multi-channel, multi-application, multi-technology, multi-operator) and coexistence of access technologies
    Architectures and system designs for connected automated driving incl. cloud and edge computing and IoT integration
    Security, privacy, liability, and dependability of vehicular networks
    Integration of V2C communications with on-board systems and networks

Tools & Methods

    Platforms for simulation, emulation, prototyping, measurement, field-trials, and real-world deployment of vehicular networks
    Optimization techniques (e.g., linear programming, machine learning) with unique benefits for vehicular applications
    Modeling, design, and analysis of vehicular networks and applications

Applications

    Cooperative perception and cooperative driving
    Innovative vehicular network applications and their communication requirements
    Vulnerable road user protection
    Impact assessments of vehicular networks on transportation (e.g., safety, efficiency, comfort, privacy, and environmental sustainability)

All submitted papers will be peer-reviewed. The Best Paper Award will be selected and handed out by our Best Paper committee. Conference content will be submitted for inclusion into IEEE Xplore as well as other Abstracting and Indexing (A&I) databases.