We present an extensive driver assistance system capable of executing two essential tasks. The first module is used in assisting the driver with road safety alerts (RSA); it scans the road environment and detects any significant entities including but not limited to vehicles, pedestrians, and traffic lights. Further, alerts are issued when the estimated physical distance from detected entities is less than a set threshold. For this module, we also propose the usage of a compute-accelerated Swin Transformer model and evaluate its efficacy against other state-of-the-art models by considering relevant metrics like inference time and mAP. The second module pertains to driver alertness detection (DAD) for identifying signs of fatigue. It scans the driver’s face and monitors a live video feed to ensure that the driver shows no signs of micro-sleep. When either module detects a behavioural anomaly, it will alert the driver with text-based messages and non-disruptive audio messages. We propose such a state-of-the-art safety system being integrated into the advanced driver assistance systems (ADAS’s) seen in modern vehicles.


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    Titel :

    Real-Time GPU-Accelerated Driver Assistance System


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Ravi, Pooja (Autor:in) / Shukla, Aditya (Autor:in) / Muruganantham, B. (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    2023-11-18


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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