This paper introduces a novel in-vehicle navigation system that tackles the well-documented distraction risks of traditional navigation interfaces, known to increase driver reaction times by up to 1.5 seconds. Unlike existing systems that force drivers to look away from the road, this proposed method leverages Augmented Reality (AR) technology. By utilizing a Heads-Up Display (HUD), the system overlays navigational instructions directly within the driver's field of view. This approach minimizes distraction and ensures focus remains on the road, potentially leading to a 30% reduction in reaction times to navigation cues compared to traditional in-dash displays. Further differentiating itself, the system incorporates AI-sourced navigation data. This offers a significant leap in accuracy and real-time functionality compared to static map data employed in conventional systems. AI-powered navigation can achieve update frequencies as high as every 30 seconds, depending on the specific model and data source, compared to the typical refresh rate of 5–10 minutes for traditional map data. This real-time advantage translates to more accurate route optimization, particularly in congested areas where traffic conditions can change rapidly. Uniquely, the proposed system prioritizes driver safety by integrating a comprehensive suite of features. These features include accident alerts triggered by gyroscope sensors, proximity sensors to ensure proper helmet wear, and even gas sensors to deter drunk driving. By combining these functionalities with traditional GPS and GSM technology for accurate positioning and emergency messaging, this project has the potential to significantly improve driver focus, safety awareness, and create a more holistic and safer in-vehicle experience.


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    Titel :

    Augmented Reality Navigation: A Vision for Safer Roads and Seamless Driving Experience


    Beteiligte:
    Prusty, Rakesh (Autor:in) / Ashar, Heli (Autor:in) / Iyer, Venkatesh (Autor:in) / Vijayan, K. (Autor:in)


    Erscheinungsdatum :

    17.04.2024


    Format / Umfang :

    4348847 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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