INNOVATIVE TURN INDICATOR SYSTEM: VOICE-ASSISTED TECHNOLOGY FOR SAFER AND SMARTER DRIVING
Abstract
The rapid evolution of automotive technologies has paved the way for improved vehicle safety, especially in the realm of driver assistance systems. This research focuses on the design and development of a dual-mode turn indicator system for automobiles, incorporating both manual and voice-assisted mechanisms. The proposed system aims to enhance driving convenience, reduce driver distraction, and improve road safety by offering hands-free operation of turn indicators. The study explores the system's design, integration with existing automobile components, and testing under various real-world scenarios. The results indicate that the system effectively improves the ease of use of turn signals while maintaining safety standards.
Keywords
References
Similar Articles
- Richard P. Hollingsworth, Centering Legacy-to-Cloud Modernization: Architectural Evolution, Cloud-Native Strategies, and Governance Implications in Enterprise Software Systems , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Dr. Amelia R. Foster, AI-Driven Cloud-Native Intelligence for Cost-Efficient, Secure, and Domain-Specific Decision Systems: An Integrative Research Study Across Hybrid Cloud Optimization, Healthcare Analytics, Edge-IoT, and E-Learning , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Lucas J. Reinhardt, Dr. Hannah C. Doyle, Dr. Noor A. Rahman, Internet of Things–Enabled Intelligent Marketing Ecosystems: An Integrative Research Study on Digital Transformation, Artificial Intelligence, Customer Experience, and Cybersecurity , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Wei Zhang, Liang Chen, Advanced Process Optimization Framework for Enhancing Biogranule Development Using Static Mixers in Aerobic Textile Wastewater Treatment Systems , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Jean Paul Kazungu, Jean Pierre Ntayagabiri, Jeremie Ndikumagenge, M. Kokou Assogba, QUANTITATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE IN HOSPITAL MANAGEMENT: SYSTEMATIC REVIEW OF REAL-WORLD IMPLEMENTATIONS AND OUTCOMES (2019–2024) , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Dr. Ren Takahashi, Dr. Mei Kobayashi, A Scalable Cloud Transition Model For Enhancing Operational Agility In Enterprise Information Systems , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Dr. Eleanor Whitmore, Cloud-Native Smart Health Platforms: Scalable Machine Learning Deployment for Cardiovascular Prediction through Heroku, Salesforce, and Urban Data Ecosystems , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Michael R. Thompson, Architecting Scalable Leader Selection and Community-Aware Coordination in Distributed Systems: A Submodular and Network-Theoretic Perspective , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 12 (2025): Volume 02 Issue 12
You may also start an advanced similarity search for this article.