Revolutionizing Automotive Engineering with Artificial Neural Networks: Applications, Challenges, and Future Directions

Abstract

Artificial neural networks (ANNs) have emerged as the technology that provides solutions to key issues arising in the field of automobile engineering regarding autonomous driving, predictive maintenance, energy control, and vehicle protection. This paper aims to present various uses of ANNs in car industry concerning data handling for continuous decision-making and adaptation. Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Generative Adversarial Networks (GANs) are all explored in relation to their ANN specific relevance to automobiles. The identified limitation also responds to issues associated with the integration of ANN such as data dependency, the computational load required, and questions related to the ethical use of AI decision making. This paper compares ANN techniques in an automotive context, explaining where they excel and where they could use improvement in terms of the tasks they are applied to. The strategies for phased implementation of the ANN framework, the performance evaluation for each stage of implementation, and the optimization methodologies are discussed below. Future direction highlights the future development of transformers, energy efficient models and raising concerns of ethical regulatory frameworks with regards to ANN driven systems. Thus, by such barriers overcoming, ANNs have a potential to significantly influence the further development of automotive engineering and make automobiles safer, more efficient and environmentally friendly. This study advances the discussion around intelligent mobility and provides the foundation on which future research in the field can build from.