Authors: Sushil Panda
Abstract: The proliferation of software-defined vehicles (SDVs) has necessitated the development of sophisticated fault detection mechanisms capable of processing high-dimensional, multimodal sensor data in real-time. This paper presents a comprehensive analysis of Convolutional Neural Network (CNN) architectures for fault detection in SDVs, examining their theoretical foundations, implementation strategies, and performance characteristics. Through extensive experimentation and comparative analysis, we demonstrate that CNN-based approaches achieve superior performance compared to traditional rule-based and statistical methods, with accuracy improvements of 15-25% and false positive rates reduced by up to 40%. Our technical contribution includes a novel ensemble architecture combining 1D-CNNs with attention mechanisms for temporal sensor data analysis, achieving 94.7% accuracy in fault classification. The paper provides detailed mathematical formulations, algorithmic implementations, and empirical validation across multiple vehicle subsystems, establishing CNNs as the state-of-the-art solution for fault detection in modern automotive systems.
