Driving Assistance Using IOT

Abstract

In order to address the ongoing problem of traffic accidents in developing nations, this study suggests a real-time, affordable driving assistance system that makes use of edge-based machine learning, sensor fusion, and the Internet of Things (IoT). In situations where traditional vision-based assistance is ineffective, the system\'s integration of live video, radar, and GPS data allows for reliable detection of environmental hazards, lane boundaries, and vehicle position. Without relying on cloud infrastructure, processing is done locally on a Jetson Nano or Raspberry Pi, offering real-time alerts and map-based visualisation. High accessibility, scalability, and alignment with the UN Sustainable Development Goals (SDG) for safer and more intelligent transportation are the goals of the design and implementation discussed here.

Introduction

Road traffic injuries are a major global problem, causing over 1.35 million deaths annually, especially in rapidly motorising countries. While modern luxury vehicles benefit from Advanced Driver Assistance Systems (ADAS), most affordable and older vehicles lack such technologies, leaving drivers vulnerable to accidents caused by human limitations such as fatigue, distraction, and slow reaction times. This technological divide is particularly serious in nations like India, where vehicle ownership is rising but cars remain largely low-cost and outdated.

The project aims to democratise access to intelligent driver assistance by developing a low-cost, modular, and easily deployable system that uses embedded AI and IoT technologies. With advancements in machine learning, inexpensive sensors, and edge computing, it is now possible to provide safety features previously limited to high-end vehicles. The system is intended to be robust, accurate, and suitable for diverse environmental and infrastructure conditions common in resource-constrained regions.

Existing ADAS solutions are expensive, proprietary, difficult to retrofit, and dependent on cloud connectivity, limiting their accessibility. In contrast, the proposed system uses a front-facing camera, millimetre-wave radar, and GPS integrated into an edge device such as a Jetson Nano or Raspberry Pi. It performs real-time object detection, lane tracking, and geolocation using models like YOLOv5 and CNN-based lane detectors, with data fused to enhance reliability in poor visibility.

The system presents alerts through a simple in-vehicle display, supports modular upgrades, and relies on open-source frameworks for flexibility and affordability. Its methodology involves continuous sensor input, parallel processing, real-time inference, sensor fusion, and synchronized data handling, ensuring fast and dependable hazard detection suitable for real-world driving conditions.

Conclusion

An important step toward democratizing automotive safety in underserved markets and cost-sensitive environments is the Driving Assistance Using IoT solution. The project creates a tangible and useful assistance system that effectively bridges the gap between high-end ADAS and the daily requirements of traditional vehicle users by skillfully combining inexpensive sensors, local machine learning inference, and smooth user interfaces. Validation under a variety of driving conditions, environmental scenarios, and vehicle types highlights the design\'s efficacy and adaptability, supporting more general goals for improving road safety, reducing accidents, and advancing the Sustainable Development Goals. As technology and market conditions change, the modular architecture allows for future expansion with more sensors, communication features, and analytics tools. The democratization of advanced driver assistance has important business and social ramifications. Reductions in accident-related medical expenses, lost productivity, and psychological trauma are all directly correlated with increased road safety. Large-scale implementation of this technology could reduce the strain on public health and emergency response systems. From the standpoint of the industry, retrofittable, scalable, and customizable solutions create new avenues for aftermarket services, insurance incentives, and product development. Although consumer confidence in automated safety systems is still developing, adoption is probably going to quicken as dependability increases and practical advantages become more apparent. Ongoing innovation will collect real-world performance data and user feedback necessary for improvement through pilot deployment in fleet cars, taxi services, and urban driving scenarios. Adoption and regulatory alignment will be accelerated through industry collaboration with government road safety agencies, insurance providers, and auto aftermarket suppliers. Academic partnerships will guarantee that the technology continues to benefit from cutting-edge research in human-computer interaction, sensor fusion, and machine learning. This work lays the groundwork for future innovation as global automotive trends shift toward increased autonomy and connected vehicle systems. This technology could save countless lives, prevent innumerable accidents, and promote fair access to lifesaving advancements in transportation safety globally with careful scaling and persistent effort.

References

[1] Redmon, J., Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv:1804.02767. [2] Ghosh, S., Ghosh, A. (2021). Real-Time Lane Detection and Driver Assistance. IJARCCE, Vol.10, Issue 5, Pages 45-52. [3] Ullah, I., Kwak, K. S. (2019). Vehicle Collision Avoidance System Using Raspberry Pi and Advanced Image Processing. Electronics, 8(2), 125. [4] Kim, S., Lee, D., Park, S. (2022). Real-Time Vehicle Detection Using YOLO and Edge Computing for Autonomous Driving. Sensors, 22(4), 1435. [5] Janai, J., Güney, F., Behl, A., Geiger, A. (2020). Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art. Foundations and Trends in Computer Graphics and Vision, 12(3-4), 185-307. [6] Project Report: Driving Assistance Using IoT, Sri Sai Ram Institute of Technology, May 2025. [7] World Health Organization (2023). Global Status Report on Road Safety 2023. WHO Publications.

Copyright

Copyright © 2025 Buvaneswaran R I, Krishnaraj B, Sridhar P, Mrs. A. Ambika. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.