From Pixels to Pavement: Generative AI Use Cases in Automotive Industry

In a nod to its massive appeal, ChatGPT racked up 100 million users within two months of its launch, even beating the wildly successful game changer - the automatic rice cooker in 1950s Japan. (And, yes, ChatGPT can even tell you how to cook perfect rice). In other milestones, it has taken only six months for GPT-3 based models to increase from 1 to 300,000, even as MarketsandMarkets estimates market size to burgeon from $11.3 billion in 2023 to $51.8 billion by 2028. Add to this, the massive growth in deal making - from 65 deals worth $271 million to over 110 deals worth $2.6 billion - emphasizing the sharp increase in valuation of Generative AI firms.

The technology roadmap will be as eventful, shifting focus from text and coding to images and video and, in due course, fully autonomous Generative AI systems. An almost infinite number of use cases will emerge, with the sheer ubiquity and power of this technology underlining the need for strong ethical and regulatory safeguards.

A World of Infinite Possibilities

Generative AI tools comprising large language and image AI models have burst open a world of possibilities for the content creation industry. Among them, automated content generation, improved quality, variety, accuracy and relevance of content, and enhanced content personalization. Generative AI models will leave no area which involves content creation - be it marketing, software, design, entertainment, or interpersonal communications - untouched.

Markets and Markets recently conducted several roundtables in EU and US and found that from initially supporting productivity gains in areas like content creation to realizing improved operational & resource efficiencies in fields like predictive maintenance to eventually assisting with breakthrough innovations in the space of drug and product development, the use cases of Generative AI will only increase in tandem with their expanding capabilities.

Generative AI and the Future of the Automotive Industry

Generative AI is widely held to be the key to unlocking a truly autonomous vehicle (AV) future. In April 2023, Great Wall Motor-backed Chinese technology start-up Haomo.AI launched DriveGPT, an autonomous driving support platform based on a generative large-scale model (LLM). The platform combines reinforcement learning from human feedback (RLHF) with actual manual driving data to enhance cognitive decision-making capabilities in autonomous driving.

Generative AI models will support three critical levers of AV R&D: simultaneous generation of multiple scenarios, prediction of future vehicle trajectories, and advancement of decision reasoning chains. By deploying algorithms to produce new content, such as images, videos, and even text, Generative AI can create virtual environments and simulate real-world scenarios, allowing AVs to learn and adapt in a safe and controlled environment.

AV development requires accurate and reliable sensor data for training purposes. Generative AI generates massive volumes of synthetic data representative of real-world driving scenarios, eliminating the need for expensive and time-consuming field tests. Moreover, real-time decision making in AVs is based on a wide range of inputs, including sensor data, traffic patterns, and pedestrian behaviour. By generating vast amounts of data, Generative AI can help create more sophisticated and practical algorithms that can be used to train decision-making models.

Beyond AVs, Generative AI will play a central role in pushing the boundaries on vehicle personalization. Faraday Future Intelligent Electric (FF)'s recently launched Generative AI Product Stack, for instance, represents a use case of a full stack generative AI software that powers the cockpit domain to personalize services and experience for the driver. Typical features will include intelligent search, text queries, translations, and recommending video/audio entertainment choices.

Generative AI will further extend the idea of personalization by learning from and predicting user preferences. For instance, route prediction and customized marketplace and service recommendations along a route, albeit without the need for the user to input a destination. Imagine the joys of being suggested a destination based on time of day or a route with your favourite coffee bar - with minimal effort on your part.

A standout use case in future will be in-car personal assistants with generative AI - Siri on Steroids or Alexa on Acid, as it were. In essence, an intelligent personal assistant with conversational and other support capabilities much like what SoundHound seems to have showcased with its generative AI voice assistant for automotive solution.

Elsewhere, generative models will be effectively applied across marketing and advertising functions to create more meaningful customer engagement. For instance, Jasper, a Generative AI tool built on GPT-3, can crank out sales emails, blogs, social media posts, and other customer-focused marketing content. Image generation models like DALL-E 2 are finding traction in advertising. For car companies that have traditionally spent disproportionately large sums of money on marketing with little to show for it, Generative AI holds promise of better tracking and making such spend work for them.

Beyond marketing, Generative AI applications across manufacturing and supply chains will support cost optimization, yielding significant savings that go straight to the bottom line.

And, finally, there's the matter of product development. The automotive industry typically spends upwards of $1 billion over several years on new product development, with no guarantee that the gamble will pay off. With its exceptional abilities in data synthesis and analysis, pattern detection and outcome prediction, generative models could whittle the interval between design, development, and delivery. In fact, they could slash the time required for platform/architecture development and new electric vehicle (EV) manufacture by three to six months at a minimum.

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A Winning Combination: Human Imagination & Technological Innovation

The World Economic Forum's Future of Jobs Report 2023 estimates that by 2027, almost 43% of tasks will be performed by machines. Financial, banking, office administration, business, insurance, marketing, management, sales, automotive, IT, health, retail, media, sports, travel…not a single industry will remain untouched. Writers, designers, poets, artists…no creative pursuit will remain insulated.

In April this year, Boris Eldagsen was chosen a winner in the "creative photo" category at the Sony World Photography Awards. He refused the award, stating his prize-winning entry was AI generated. His gesture was meant to trigger debate - can AI-generated images be considered art? What value does technology have without human imagination and creativity? Will such powerful technology empower or enfeeble human ingenuity?

As boundaries blur and the human experience transforms, the conversations about Generative AI - its profound rewards and risks - will only get more complex. Engaging successfully with this brave new world will require a winning combination of human imagination with technological innovation.

This article is written by Sarwant Singh with contributions from MarketsandMarkets recent report on Generative AI Market and from Rounak Singh and Sushmit Chakraborthy

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