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Journal article
Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
Future internet, Vol.17(6), p.254
06/09/2025
Web of Science ID: WOS:001515549000001
Abstract
Uneven roads pose significant challenges to vehicle stability, passenger comfort, and safety, especially in snowy and mountainous regions. These problems are often complex and challenging to resolve with traditional detection and stabilization methods. This paper presents a dual-method approach to improving vehicle stability by identifying road irregularities and dynamically adjusting the balance. The proposed solution combines YOLOv8 for real-time road anomaly detection with a GY-521 sensor to track the speed of servo motors, facilitating immediate stabilization. YOLOv8 achieves a peak precision of 0.99 at a confidence threshold of 1.0 rate in surface recognition, surpassing conventional sensor-based detection. The vehicle design is divided into two sections: an upper passenger seating area and a lower section that contains the engine and wheels. The GY-521 sensor is strategically placed to monitor road conditions, while the servomotor stabilizes the upper section, ensuring passenger comfort and reducing the risk of accidents. This setup maintains stability even on uneven terrain. Furthermore, the proposed solution significantly reduces collision risk, vehicle wear, and maintenance costs while improving operational efficiency. Its compatibility with various vehicles and capabilities makes it an excellent candidate for enhancing road safety and driving experience in challenging environments. In addition, this work marks a crucial step towards a safer, more sustainable, and more comfortable transportation system.
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Details
- Title
- Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
- Publication Details
- Future internet, Vol.17(6), p.254
- Resource Type
- Journal article
- Publisher
- MDPI
- Number of pages
- 29
- Grant note
- University of West Florida, Pensacola, FL, USA
- Copyright
- © the author(s)
- Identifiers
- WOS:001515549000001; 99381798263706600
- Academic Unit
- Center for Cybersecurity and AI; Hal Marcus College of Science and Engineering
- Language
- English