A Novel Deep Feature Fusion Strategy for Accurate and Real-Time ADAS Scene Understanding

Abstract

Advanced Driver Assistance Systems (ADAS) play a vital role in enhancing vehicle safety by assisting drivers in tasks such as braking, lane correction, speed maintenance, and acceleration control. Traditionally, these systems relied on rule-based approaches and predefined sensor thresholds, where specific conditions triggered actions such as warnings or braking. However, such systems lacked adaptability and were unable to effectively handle complex, dynamic real-world driving scenarios, resulting in limited accuracy and poor generalization. To overcome these limitations, the proposed system adopts a data-driven approach using both Machine Learning (ML) and Deep Learning (DL) techniques. The system incorporates algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest Classifier (RFC), along with a hybrid Convolutional Neural Network (CNN-1D) combined with a Random Forest (RF) - DualStream-ConvRF model. The CNN1D model is used to extract deep feature representations from sensor data, which are then combined with original features and fed into the RF classifier to improve prediction accuracy. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance in the dataset. The system is implemented using modern data analysis and deep learning frameworks, along with a graphical user interface for user interaction. Experimental results demonstrate that the proposed hybrid model achieves higher accuracy compared to traditional approaches, providing more reliable ADAS predictions. The project is completed through data preprocessing, model training, evaluation using metrics such as accuracy, precision, recall, and F1-score, and integration into a secure and userfriendly system, making it an efficient and scalable solution for intelligent driving assistance