Cloud-Native AI for Autonomous Vehicles in Smart Cities with CNNs and Sign Language Support
DOI:
https://doi.org/10.15662/IJRAI.2023.0603004Keywords:
Cloud-native AI, Autonomous vehicles, Smart cities, Convolutional neural networks, Real-time perception, Route optimization, Traffic management, Sign language support, Inclusive mobility, Predictive analyticsAbstract
This paper proposes a cloud-native AI framework for autonomous vehicles (AVs) operating in smart city environments, leveraging convolutional neural networks (CNNs) for real-time perception, decision-making, and predictive analytics. The framework integrates data streams from IoT sensors, traffic management systems, and vehicle networks to enable dynamic route optimization, collision avoidance, and traffic flow regulation. Additionally, AI-powered sign language support is incorporated to enhance accessibility for hearing-impaired pedestrians and city operators, promoting inclusive urban mobility. By deploying the system in a cloud-native architecture, it achieves scalability, low-latency processing, and seamless integration with existing smart city infrastructures. Experimental simulations demonstrate improvements in AV navigation accuracy, safety, and responsiveness while maintaining efficient resource utilization. The study highlights the potential of combining cloud-native AI, CNN-based perception, and accessibility features to create intelligent, inclusive, and resilient urban transportation ecosystems.
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