This paper introduces an open-source solution for real-time object awareness in the context of intelligent vehicles, designed to seamlessly adapt to simulation environments. The system transcends traditional vehicle detection, offering a comprehensive framework that includes object classification, precise location, and rotation estimation. Leveraging a multi-camera setup, our approach provides a comprehensive understanding of the surrounding environment, extending beyond vehicle recognition to include various other objects. What sets our solution apart is its capability to efficiently reformat and transition to simulation environments like Blender, enabling integration and testing within virtual contexts. The core components of our system encompass data collection, preprocessing, deep learning model selection, Use of Look up table, model training, efficient inference, and post- processing techniques. Designed for open-source collaboration, this solution is positioned for continuous improvement, adaptation to evolving needs, and addressing emerging challenges in the field of intelligent transportation and related domains. This paper represents a foundational step towards establishing an accessible and adaptable real-time object awareness system, encouraging innovation and research in the realm of intelligent vehicles and their applications, both in the real world and in simulated environments