Writing

**Abstract: Driving the Future of Automotive Engineering: Practical Applications of AI in Design, Optimization, and Smart Mobility**

The automotive industry is evolving rapidly, with increasing demands for lighter vehicles, improved safety, efficient manufacturing, reduced development costs, and more sustainable mobility solutions. Meeting these demands requires engineers to evaluate multiple design constraints while making decisions within increasingly complex vehicle architectures. Traditional engineering approaches rely heavily on experience, manual iterations, and repeated computer-aided design and engineering (CAD/CAE) analysis, which can make the development process time-consuming.

This session will explore how artificial intelligence (AI) can support automotive engineering by improving design exploration, component packaging, and engineering decision-making. Drawing on research into AI-assisted automotive packaging optimization, I will discuss how Artificial Neural Networks (ANN) and Genetic Algorithms (GA) can work together to predict design feasibility and explore alternative component configurations while considering factors such as space utilization, weight, manufacturing cost, thermal requirements, accessibility, and assembly constraints.

The session will also examine the practical opportunities and challenges of integrating AI into existing engineering workflows, including data quality, simulation limitations, model reliability, and the importance of physical validation. Rather than viewing AI as a replacement for engineering expertise, the discussion will focus on how engineers can use AI as a supporting tool to evaluate more alternatives, reduce repetitive work, and make informed design decisions.