Artificial intelligence can help design more appealing cars | MIT Sloan (2024)

From curb appeal in real estate to smooth edges on smartphones, consumers gravitate toward products that are pleasing to the eye. This is especially true in the automotive industry, where product aesthetics have been linked to roughly 60% of purchasing decisions.

“People buy cars based on aesthetics. Styling can make a difference,” saida professor of marketing at MIT Sloan. Styling is also expensive: Carmakers invest more than $1 billion to design the average car model and up to $3 billion for major redesigns.

A recent paper Hauser co-authored demonstrates that machine learning models can not only predict the appeal of new aesthetic designs but also generate designs that are aesthetically pleasing or aesthetically innovative. (And, once trained, the models can run on a standard corporate laptop.)

The paper was co-authored by Yale School of Management professor Alex Burnap and Kellogg School of Management professor Artem Timoshenko.

“The models are a tool for designers to get new ideas and try them out,” Hauser said. “They are capable of generating new images that are highly aesthetically pleasing and that can be evaluated quickly.”

Unappealing cars do not sell

The Pontiac Aztek is an infamous example of how car buyers prioritize aesthetics.

Product aesthetics have been linked to roughly 60% of purchasing decisions in the automotive industry.

General Motors released the Aztek in the summer of 2000, building the crossover SUV on the same platform as the Buick Rendezvous. With multiple features for fans of the outdoors, the Aztek generally earned high customer satisfaction scores — apart from its exterior styling.

Here, the Aztek flopped. A profile noted that the vehicle had an intentionally aggressive, “in your face” design and wasn’t for everyone. It has been routinely derided as one of the ugliest cars of all time, and GM stopped making the SUV in 2005.

The Aztek sold half as many units as the Rendezvous, which was subsequently redesigned and rereleased as the Buick Enclave — which sold at a 30% higher manufacturer’s suggested retail price. The Enclave is still manufactured today, more than 15 years after its initial launch.

The Aztek offers a clear lesson, Hauser said: “If two cars are equally reliable and effective, consumers will buy the one that’s more attractive.”

Using AI to predict —and generate —aesthetically pleasing models

Today’s carmakers make big investments to avoid releasing the next Aztek.

Traditionally, this process has relied on theme clinics. These are events where carmakers bring hundreds of targeted consumers to a single location to judge designs. Theme clinics can cost $100,000 each, and carmakers need to hold hundreds each year to make sure they put the right designs into production.

Here, predictive modeling has an obvious appeal: Carmakers that can weed out the designs most likely to earn low scores on aesthetics won’t bother advancing these options beyond the initial design stage. With fewer designs that need to be tested in theme clinics, development timelines will get shorter and costs will decrease.

Working with GM as a research partner, Hauser and his co-authors developed two models:

  • A generative model that creates new car designs based on prompts from designers about viewpoints, colors, body type, and image.
  • A predictive model that forecasts how consumers will rate designs with respect to aesthetic appeal or innovativeness.

Research began with the predictive model, built on a deep neural network. This model achieved the desired results, with a 43.5% improvement over the baseline — and an improvement over more conventional machine learning models.

“Our model was able to indicate the designs that were good and the designs that were bad,” Hauser said. “But as we got more and more into the process, we realized the real leverage was in creating new designs.”

The generative model produced images that consumers deemed to be aesthetically appealing and even suggested designs that were later introduced to the marketplace. The researchers also found that the model can be applied to nonautomotive products.

Augmenting the design experience

As is the case with other successful applications of artificial intelligence, the models aren’t meant to replace human designers. For starters, the generative model doesn’t just spit out designs automatically; it needs an experienced designer to define the parameters first, Hauser said.

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In addition, automotive design is an inherently iterative and asynchronous process. Designers iterate through design-concept generation, testing, evaluation, and redesign. The finished product —an amalgamation of tens of thousands of decisions — gets rated by consumers and critics alike, based on descriptors such as sporty, rugged, luxurious, and so on.

Hauser and his co-authors view artificial intelligence as an augmentation of the design process akinto computer-assisted modeling in furniture design, fashion, and other industries where aesthetics plays a prominent role.

“There are a number of different ways you can cut a dress,” he said. “A machine learning model can give designers ideas about what customers will think is aesthetically pleasing, but a designer isn’t going to produce exactly what the machine puts out.”

Read next: Machine Learning, explained

For more info Sara Brown Senior News Editor and Writer sbrown1@mit.edu

Artificial intelligence can help design more appealing cars | MIT Sloan (2024)

FAQs

What is the advantage of artificial intelligence in automobiles? ›

FAQs About AI in the Automotive Industry

AI enhances safety through advanced driver assistance systems (ADAS) such as lane departure warnings, autonomous emergency braking, and adaptive cruise control, which help in preventing accidents by alerting drivers to potential hazards and taking preventive actions.

How is artificial intelligence used in self-driving cars? ›

How is AI used in self-driving cars? A. AI in self-driving cars is used for sensing, decision-making, predictive modeling, and natural language processing. This enables them to detect objects, predict behavior, plan routes, and communicate with passengers, ensuring a safe driving experience.

How is AI used in car design? ›

AI helps designers by showing them how to combine the best parts of different cars and create something that stands out in both style and function. Making Cars Slick & Efficient: It's not just about good looks, though. AI is also helping make cars more aerodynamic.

How can AI help in design? ›

AI can help you streamline your design process, making it more efficient and cost-effective. For example, AI-powered tools like Microsoft Designer and Canva can create designs automatically based on your requirements.

What are the disadvantages of AI in cars? ›

Disadvantages: A critical look at autonomous driving
  • Technical Developments. A system participating in road traffic must transmit, evaluate and calculate large amounts of data in real time for accurate predictions. ...
  • Expensive. ...
  • Surveillance. ...
  • Rebound Effects. ...
  • Mixed Traffic. ...
  • Vehicle Communication. ...
  • Legal Aspects. ...
  • Job Loss.

What is the use of AI in electric cars? ›

The applications of AI turn electric vehicles into a fascinating consumer option (Ahmed et al., 2021) as it integrates driver assistance systems and autonomous driving, facilitates EV charging, improves energy management and optimization, enhances battery management, enables predictive maintenance, promotes intelligent ...

How do smart cars use AI? ›

AI is the brain behind the smart car. It plays a central role in tasks like image recognition, natural language processing for voice commands, predictive maintenance, and autonomous driving algorithms. AI enables the car to learn from data, adapt to different driving scenarios, and continuously improve its performance.

Are self-driving cars example of strong AI? ›

Self-driving cars and virtual assistants, like Siri, are examples of Weak AI.

Can AI control a car? ›

AI technologies power self-driving car systems. Developers of self-driving cars use vast amounts of data from image recognition systems, along with machine learning and neural networks, to build systems that can drive autonomously.

How does AI make cars safer? ›

The Rise of AI in Vehicle Safety

These sensors collect real-time information on vehicle performance, driver behavior, and environmental conditions. By processing this data, AI systems can predict potential failures and identify risk factors that may lead to accidents, long before they become imminent threats.

How do AI cars make decisions? ›

These futuristic vehicles can detect their surroundings using sensors like cameras and radar. Using state-of-the-art artificial intelligence (AI) software algorithms, they are able to meticulously plan routes and make independent driving decisions in real-time, all thanks to the incredible power of onboard computers.

What is the power of AI in design? ›

In conclusion, AI's role in the design field is not just transformative but revolutionary. By automating tasks, providing deep insights, and enhancing creativity, AI is changing how designers work while also expanding what they can achieve.

How does AI help creativity? ›

AI algorithms can create abstract patterns, morph images, or generate unique compositions that artists can incorporate into their work. Music Composition: Musicians can harness AI to compose melodies, harmonies, and even entire pieces of music.

Can AI design better than humans? ›

The researchers also surveyed 100 urban designers, who were unaware of whether the plans they were asked to choose between were generated by human planners or AI. The AI won substantially more votes for some of its spatial designs, but for other plans, there was no clear preference among survey participants.

What is the advantage of artificial intelligence in transportation? ›

AI for transportation can help reduce the risk of road accidents and enhance safety by informing driver with real-time updates about traffic conditions and potential hazards. AI helps improve fuel efficiency by assisting divers in making informed decisions about when and how to accelerate and brake.

What are the advantages of artificial intelligence technology? ›

The following are the primary advantages of AI:
  • AI drives down the time taken to perform a task. ...
  • AI enables the execution of hitherto complex tasks without significant cost outlays.
  • AI operates 24x7 without interruption or breaks and has no downtime.
  • AI augments the capabilities of differently abled individuals.

Why is AI driving good? ›

Safety Improvements

Every five seconds, a car crash occurs, and 98% of those crashes are from Human error. Self-driving cars are designed to mitigate this risk. The advanced AI and sensor technology in autonomous vehicles, such as Lidar, can detect potential road hazards and adjust course in real time.

What is the benefit of the use of industrial robots in the production of cars? ›

Added efficiency and output.

As machinery can be programmed to work continuously, production can continue around the clock, boosting productivity proportionately. The automotive industry, for example, has seen a 50% increase in productivity through robotic process automation (RPA) since 2009.

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