VERONICA: AI Driven Edge System for Comprehensive Bike Safety and Assistance Through Multi-Source Data Fusion

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 14 | 01 | Page :
    By

    Sanket Khandu Sadgir,

  • Aditya Sanjay Salve,

  • Vaibhav Savliram Bodke,

  • Sahil Jaimal Pathania,

  • Ranjana P. Dahake,

  1. , Dept. of Computer Engineering, MET Institute of Engineering, Nashik, Maharashtra, India
  2. , Dept. of Computer Engineering, MET Institute of Engineering, Nashik, Maharashtra, India
  3. , Dept. of Computer Engineering, MET Institute of Engineering, Nashik, Maharashtra, India
  4. , Dept. of Computer Engineering, MET Institute of Engineering, Nashik, Maharashtra, India
  5. , Dept. of Computer Engineering, MET Institute of Engineering, Nashik, Maharashtra, India

Abstract

Road safety for bike riders remains a significant concern, with accident rates highlighting the need for advanced solutions to ensure rider protection and awareness. This paper presents “VERONICA: AI Driven Edge System for Comprehensive Bike Safety and Assistance Through Multi-Source Data Fusion”, a voice-activated, continuously operating assistance system designed to provide real-time, intelligent solutions for various riding scenarios. VERONICA integrates accident detection, low-traffic route navigation, traction control advisories, and weather updates into a single, user-friendly platform. By leveraging advanced voice recognition, natural language understanding (NLU), and IoT technologies, VERONICA delivers proactive guidance and timely alerts tailored to user needs. The system employs a robust architecture, including microcontrollers, sensors, and APIs, to monitor and respond to environmental. This paper explores the technical challenges, architecture, and implementation strategies involved in creating VERONICA, with a focus on ensuring reliability, responsiveness, and scalability for practical deployment in real-world scenarios. The results demonstrate that VERONICA significantly enhances rider safety and convenience, making it a transformative step forward in biking technology.

Keywords: Accident detection, Low-traffic route navigation, weather updates, NLU.

How to cite this article:
Sanket Khandu Sadgir, Aditya Sanjay Salve, Vaibhav Savliram Bodke, Sahil Jaimal Pathania, Ranjana P. Dahake. VERONICA: AI Driven Edge System for Comprehensive Bike Safety and Assistance Through Multi-Source Data Fusion. Research & Reviews: A Journal of Embedded System & Applications. 2025; 14(01):-.
How to cite this URL:
Sanket Khandu Sadgir, Aditya Sanjay Salve, Vaibhav Savliram Bodke, Sahil Jaimal Pathania, Ranjana P. Dahake. VERONICA: AI Driven Edge System for Comprehensive Bike Safety and Assistance Through Multi-Source Data Fusion. Research & Reviews: A Journal of Embedded System & Applications. 2025; 14(01):-. Available from: https://journals.stmjournals.com/rrjoesa/article=2025/view=232463


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Ahead of Print Subscription Review Article
Volume 14
01
Received 16/06/2025
Accepted 08/09/2025
Published 18/11/2025
Publication Time 155 Days
256

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