K. Anusha Reddy, Pabbathi Upender, Gunjolu Sravani, Vanam Sathya Sri Asritha 03 04(1) Autonomous vehicular communication systems play a vital role in intelligent transportation by supporting continuous data exchange among vehicles roadside infrastructure and centralized networks These systems depend on key parameters such as Random Access Memory RAM storage capacity transmission rate and trust factor to ensure efficient and reliable communication In dynamic environments accurate evaluation of communication unit capability is essential for maintaining performance safety and optimal resource utilization Conventional assessment methods rely on manual configuration checks and thresholdbased monitoring where parameters are evaluated individually Such approaches fail to capture the complex relationships among multiple factors making them unsuitable for realtime vehicular scenarios and leading to inaccurate capability estimation To improve automation machine learning models including Decision Tree Regressor DTR Orthogonal Matching Pursuit Regressor OMPR and KNearest Neighbors Regressor KNNR are used to predict capability scores However these models have limitations in handling nonlinear interactions noise sensitivity and generalization To address these issues a hybrid Deep Fuzzy Regression DFR model is proposed integrating Deep Fuzzy Encoding DFE with Random Forest Regressor RFR and Linear Regression LR through ensemble learning The system follows a pipeline of preprocessing feature handling training and evaluation using MAE MSE RMSE and R Results show that DFR provides accurate and reliable capability assessment for realtime vehicular communication systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026