Enhancing Autonomous And Battery Electric Vehicle Performance Using AI And ML Algorithms

Abstract

The document outlines a comprehensive analysis of testing scenarios conducted to evaluate an AI system's intent recognition and trajectory prediction models. Utilizing 14 case studies, various scenarios were designed to assess the performance of the models in different situations. These scenarios involved multiple initial configurations, speeds of ego and target vehicles, candidate classes for intent recognition, and vehicle maneuvers. The experimentation was based on a significant dataset to explore the parameters thoroughly and compare the proposed models with existing methodologies. Precision, recall, and F1-score metrics were employed to measure the AI system's performance accurately. The paper also highlights the importance of training the AI models with similar data and subjecting them to pre-processing before experimentation. The study culminates in a comparison of the developed system with state-of-the-art architectures, emphasizing the novelty of combining intent recognition and trajectory prediction models for enhanced performance. Additionally, the work carried out in this article references the research of Mandala and Srinivas Dolu Surabhi et.al, emphasizing the benefits of their hybrid machine-learning model for predictive maintenance in passenger vehicles, specifically focusing on the proposed hybrid model's accuracy and potential to revolutionize predictive maintenance frameworks in the automotive industry.