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Hyundai’s New AI Cruise Control Learns How You Like to Drive

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Hyundai Smart Cruise Control
Hyundai’s new Smart Cruise Control technology uses artificial intelligence to learn your driving style
Photo: Hyundai

Adaptive cruise control systems automatically adjust a vehicle’s speed to maintain a safe following distance in traffic. However, our individual driving styles and preferences vary widely, and the technology isn’t designed for fine-tuning. As a result, driving dynamics with adaptive cruise control systems like Hyundai’s Smart Cruise Control can make some people uncomfortable and reluctant to use the system.

To make Smart Cruise Control more customizable and user-friendly, Hyundai has enhanced it with artificial intelligence. This AI system, known as SCC-ML, uses machine learning to constantly collect and incorporate information about how you prefer to drive.

First, cameras and sensors send driving information to the system’s central computer. The key inputs are how closely you prefer to follow, your preferred acceleration speed and how quickly you respond to surrounding traffic conditions.

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Hyundai Smart Cruise Control
Photo: Hyundai

Then, SCC-ML deploys an algorithm to process the information and detect your individual driving preferences and patterns. It also takes into account how your preferences differ at different speeds, situations, and conditions.

Equipped with all this information, SCC-ML has the ability to identify more than 10,000 different individual driving patterns. That way, when you activate this system, its automatic responses more closely track with what you would do if you were in control — creating a more comfortable, customized experience behind the wheel.

Hyundai hastens to point out that SCC-ML only cooperates with driver preferences up to a point. The AI technology won’t learn unsafe driving styles, so if you like to aggressively tailgate and stomp on the gas, you might be out of luck.

The automaker hasn’t indicated an availability timeline for this technology yet, saying only that it’s “planned for implementation” in upcoming models.

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