Burger King has turned Tesla Autopilot confusion into an opportunity to boost its ad campaign after the system started erroneously detecting the fast-food company’s signboards as stop signs.
Tesla launched an Autopilot update in April this year with a new feature called “Traffic Light and Stop Sign Control,” which helps its Tesla cars to identify traffic signs such as stop signs. However, it turns out that Autopilot was not clever enough to distinguish the difference between a Burger King sign and a stop sign. Tesla vehicles would thus stop at locations where Burger King has placed signs next to the road.
Burger King was quick to capitalize on the discovery by launching an ad campaign complete with hashtags such as #AutopilotWhopper and #freewhopper. The ad campaign features a video in which a man is driving a Tesla car, and as he approaches the Burger King sign, the vehicle starts detecting the fast-food sign as a traffic sign. It even automatically slows down so that it can come to a stop as it approaches the sign.
“Just as I come over this hill there’s a Burger King sign that’s down the hill, and it’s going to try to stop,” the driver points out.
The fast-food company even came up with captions such as “smart cars are smart enough to brake for a whopper” in the video demonstrating the Autopilot error. Burger King also encouraged other Tesla owners to post similar experiences using the hashtags.
The Autopilot error will provide a chance for improvements in the system
Burger King’s fast reaction to the Tesla Autopilot was impressive, and it is a classic example of how to take advantage of a situation in your favor. But on the more technical side, the error highlights some of the imperfections that exist with AI-based technology, proving that it is not always 100% accurate. However, this is not the first error that has been made by the Autopilot. It also highlights the learning-curve involved with AI-based technologies.
Tesla will likely improve the system just as it has done with previous errors. It is the discovery of such errors in autonomous systems that contribute to changes that make the systems smarter over time.