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AI co-driver and AR integration for dynamic emergency navigation and collision avoidance

  • Vassilis Charissis*
  • , Soheeb Khan
  • , Samar AlTarteer
  • , Ramesh Lagoo
  • *Corresponding author for this work

    Research output: Contribution to conferencePaperpeer-review

    11 Downloads (Pure)

    Abstract

    This paper presents an ongoing project investigating the integration of an AI co-driver to enhance realtime decision-making and improve driver safety in high-risk environments. Contemporary urban and motorway driving imposes heavy cognitive demands, often intensified by information overload from multiple in-vehicle displays. To address this, the project proposes an AI Co-Driver combined with a full-windshield Augmented Reality (AR) Head-Up Display (HUD), designed to reduce cognitive strain and heighten situational awareness. The system prioritises and filters essential traffic and environmental data, projecting it directly into the driver’s line of sight and reinforcing it with spatial audio cues. A VR driving simulator study involving 20 participants demonstrated notable eductions in collisions, quicker reaction times, and increased driver confidence when the system was active. While the results highlight strong potential for mitigating vehicular infobesity and supporting the gradual acceptance of autonomous technologies, further work is needed to optimise clarity, trust, and perceived safety in demanding scenarios.
    Original languageEnglish
    Number of pages5
    DOIs
    Publication statusE-pub ahead of print - 27 Mar 2026
    EventIEEE 44th International Conference on Consumer Electronics - Raffles Hotel, Dubai, United Arab Emirates
    Duration: 3 Feb 20265 Feb 2026
    https://icce.org/2026/

    Conference

    ConferenceIEEE 44th International Conference on Consumer Electronics
    Abbreviated titleIEEE ICCE 2026
    Country/TerritoryUnited Arab Emirates
    CityDubai
    Period3/02/265/02/26
    Internet address

    Keywords

    • augmented reality
    • artificial intelligence
    • collision avoidance
    • navigation
    • user experience
    • emergency vehicles
    • human-machine interaction

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