Secure AI-Augmented Traffic Signal Prediction for Autonomous Driving Efficiency with Citrix-Enabled Security Monitoring and SAP Integration
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Abstract
Efficient and safe navigation of autonomous vehicles (AVs) requires intelligent prediction of traffic signals while ensuring data security and system reliability. This paper presents a secure AI-augmented framework for traffic signal prediction designed to enhance autonomous driving efficiency. The framework integrates advanced machine learning models to forecast traffic signal states in real time, enabling proactive route planning and reduced vehicle idle time. Citrix-enabled security monitoring is incorporated to safeguard data integrity, detect anomalies, and prevent cyber threats in vehicular networks. Additionally, SAP integration provides robust enterprise-grade data management and analytics capabilities, supporting scalability and seamless interoperability with existing transportation management systems. Experimental evaluation demonstrates that the proposed approach achieves high prediction accuracy, reduces latency in traffic signal processing, and maintains stringent security compliance, thereby contributing to safer and more efficient autonomous driving ecosystems.
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