Scalable Cloud and AI Integration for Intelligent Transportation Safety Systems

Vinod Battapothu

doi:10.15662/IJRAI.2024.0706035

Authors

Vinod Battapothu Independent Researcher, India Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0706035

Keywords:

Cyber-Physical Systems (CPS), Cloud-Enabled CPS (C-CPS), Intelligent Transportation Systems, Autonomous Land Vehicles (ALVs), Uncrewed Aerial Vehicles (UAVs), Drones as a Service (DaaS), AI-Integrated Safety Systems, LiDAR and Vision Sensing Fusion, Cloud-Based Model Training, Traffic Safety Enhancement, Edge-to-Cloud CPS Architectures, Hazard Event Detection, Real-Time Autonomous Control, Scalable CPS Infrastructure, AI-Driven Traffic Analytics, Connected Vehicle Ecosystems, Adaptive Safety Mechanisms, Distributed Sensing and Actuation, Operational Risk Mitigation in CPS, Intelligent Mobility Platforms

Abstract

Cyber-Physical Systems (CPSs) integrate connected sensing (e.g., LiDAR, cameras), control, computing, wireless communication, and actuation components with physical objects and processes to achieve interactive and adaptive capacity. CPSs can improve safety for autonomous systems, such as Autonomous Land Vehicles (ALVs) and Uncrewed Aerial Vehicles (UAVs), by identifying and avoiding hazardous events during operation.

However, the recent surge of application scenarios, traffic data requirements, and the number and size of interactive CPS participants have led to significant challenges in improving and ensuring operational safety for these intelligent CPSs. These challenges have prompted scholars and practitioners to integrate cloud infrastructure and Artificial Intelligence (AI) services with CPSs to create new cloud-enabled Cyber-Physical Systems (C-CPSs). These C-CPSs can reduce the validation and training cost of intelligence models and intelligently respond to unsafe events for traffic safety enhancement. In the context of Intelligent Transportation Systems, the combination of C-CPSs, Drones as a Service (DaaS), and ALV safety is addressed.

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