AI helps tailor the driving experience to the individual.

Autonomous vehicles, connected ecosystems, and smart factories are only the beginning. Generative AI is pushing the auto industry beyond predictions into a bold era of creativity — from EV design to real-time diagnostics and showroom automation. Here is how GenAI is reshaping innovation across the automotive value chain.

Let us start with what is familiar: AI and machine learning are already transforming industries, particularly the automotive sector. Predictive maintenance, autonomous driving, and connected vehicles dominate the conversation. But there is a deeper frontier — Generative AI (GenAI).

What is generative AI?

At a broader level, gen AI refers to AI systems that can produce original content—such as text, images, video or software—based on patterns it has learned from large datasets.

Gen AI is powered by advanced machine learning (ML) techniques, especially deep learning, which mimics how the human brain recognizes and processes information. Many generative systems rely on large language models (LLMs) and natural language processing (NLP), which allow them to understand prompts and generate text that resembles human communication.

Generative AI models learn to predict what content should come next. Areas that currently see the most use of generative AI include product development, customer engagement, operational efficiency and technology modernization.

Predicting Electric Vehicle Efficiency

One of the main challenges in using generative design software for EV efficiency is predicting performance targets such as aerodynamics, weight, and powertrain efficiency. Even if generative design software can generate and evaluate multiple design options, it is up to the designers to interpret the results and decide which design is the most efficient. Engineering simulation comes to help here (more on this later).

Electric Vehicle Production

Another challenge is to ensure the design will be manufacturable. Design criteria and manufacturing methods may not always align, but designers can achieve both with the right approach. A paper by EPFL and Neural Concept shows how generative design software can generate realistic designs. This introduces the subject of AI (figure below).

Autonomous driving:

AI is essential to the development and refinement of autonomous driving technology. By processing vast amounts of sensor data from cameras, radar, and LiDAR, AI enables vehicles to navigate complex environments, making real-time decisions for safe driving. AI’s machine learning capabilities allow autonomous vehicles to continuously improve their performance, adapting to varied road conditions and enhancing overall driving efficiency. This not only improves safety but also optimises energy use by selecting smoother routes and avoiding sudden accelerations or stops.

Personalised user experience and vehicle customisation:

AI helps tailor the driving experience to the individual. By learning a driver’s behaviours and preferences—such as how they accelerate, brake, or manage corners—AI can adjust the vehicle’s performance settings for optimal efficiency and comfort. AI can also optimise interior systems like climate control based on external weather conditions, user preferences, and battery levels. For instance, by managing heating or cooling more efficiently, AI can maintain passenger comfort while minimising the impact on energy consumption.

 By - Aaradhay Sharma