Application of Systems Engineering in context of building 3D Environment for Autonomous Vehicle Systems in an industrial context for Central Car Computing

Abstract

This paper outlines a robust framework for the creation of intelligent 3D traffic environments driven by advanced data insights. The primary objective is to enhance traffic process efficiency using virtualization and comprehensive data integration. Smart 3D environments offer a sophisticated means to replicate real-life traffic scenarios digitally, thereby supporting well-informed decision-making, optimizing traffic flows, and enabling predictive maintenance strategies. By incorporating real-time sensor data and leveraging innovative AI models, these environments build a dynamic and precise digital representation of traffic systems, enabling continuous monitoring and actionable data insights. The presented framework incorporates essential elements such as sensor networks, IoT-based systems, and AI-driven algorithms to construct an all-encompassing digital counterpart of the actual traffic environment. This digital replica facilitates the simulation of various traffic conditions, allowing for the identification and mitigation of inefficiencies before they impact real-world systems. Additionally, the implementation of these intelligent environments enables the seamless integration of novel technologies, including autonomous vehicles and connected infrastructure, into current traffic systems. Moreover, this paper emphasizes the importance of the Systems Modelling Language (SysML) for effectively organizing and managing the complex data sets required for the development and ongoing maintenance of these environments. The study’s findings illustrate that intelligent 3D traffic environments contribute significantly to improved operational efficiency, resource optimization, and adaptability within dynamic traffic ecosystems. This research provides valuable insights for engineers and scholars aiming to apply intelligent 3D modelling solutions in the domain of traffic management and urban planning.