Paper Details

Name: Shaffy, Aditya Sinha, Md Shadab Alam, Syed Ubaid Ullah, Naman Kumar & Sachin Kumar Sachan
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 591-601
DOI: https://doi.org/10.5281/zenodo.19065117

ABSTRACT
With the rapid advancements in artificial intelligence (AI), autonomous vehicles have emerged as a transformative innovation in transportation. Understanding the evolution of AI- powered recognition systems and identifying emerging trends in this domain is crucial for guiding future research and development. This paper proposes a comprehensive approach to analyzing AI technologies used in autonomous vehicles, focusing on perception, decision-making, and predictive modeling. By systematically collecting data from multiple sources, we preprocess and structure it for analysis. Using advanced machine learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and sensor fusion algorithms, we extract insights into how AI enables vehicles to recognize objects, predict behaviors, and navigate complex
environments. Visualization techniques such as heat maps, trajectory graphs, and feature importance charts will demonstrate the prominence of various AI methods over time and their impact on safety and efficiency. Our findings highlight key trends in AI-powered perception systems, including object detection, lane recognition, and predictive modeling, while also identifying challenges like handling edge cases and ensuring robustness under adverse conditions. This study not only showcases the effectiveness of AI in autonomous vehicle recognition but also provides a roadmap for leveraging these technologies to shape the future of mobility.

General Terms—Autonomous Vehicles, Artificial Intelligence, Object Recognition, Computer Vision, Sensor Fusion, Predictive Modeling, Deep Learning