Quantum Informed Multi-Agent Boosted Solution for Seatbelt Monitoring Systems

Traditional approaches for seatbelt monitoring typically focus on binary seatbelt fastening detection. As such, they lack the ability to assess nuanced safety factors like belt routing, belt tension, and driver posture. These weaknesses thwart complete seatbelt compliance monitoring, especially in high-risk, dynamic contexts, such as fleet management for heavy vehicles. This paper introduces a novel Quantum Informed, Multi Agent Boosted Solution (QIMABS) for seatbelt monitoring that leverages a multi-agent quantum framework to deliver real-time, feature-rich safety assessment. The proposed architecture incorporates quantum micro, macro, and hyper agents, each devised to gauge specific seatbelt features individually, holistically, and over time. By integrating quantum data encoding, spatiotemporal superposition, and quantum informed decision-making mechanisms, the proposed solution is capable of processing sequential observations to provide a comprehensive seatbelt compliance score. Our solution holds promising potential to enhance accuracy, scalability, and adaptability in seatbelt compliance monitoring, laying a foundation for future advancements in quantum-enabled safety systems.