[0:12] What's up everybody? Koiwire here with a
[0:14] special edition of CNN 10. Today we're
[0:16] taking a deep dive into the world of
[0:18] artificial intelligence and self-driving
[0:20] cars. Today we're going to Curiosity Lab
[0:23] in Peach Tree Corners, Georgia to
[0:25] experience this tech firsthand. This is
[0:27] my first time in a Whimo. Let's go.
[0:31] You could call this one of the most
[0:32] advanced smart cities in the world.
[0:35] Tomorrow's technologies being tested and
[0:37] created today. The color printer was
[0:40] invented here in Technology Park. The
[0:42] Haze modem, too. Now, autonomous or
[0:45] self-driving vehicles are being steered
[0:48] to new frontiers. Air package delivery
[0:51] systems via drones. Mobile delivery
[0:54] robots tested and perfected. Where are
[0:57] we? What is this place? I feel like I
[0:59] stepped into the future.
[1:00] >> This is Curiosity Lab. We are a
[1:02] nonprofit just inside the city of Peach
[1:04] Tree Corners. And what we do is we help
[1:06] companies test and deploy their
[1:08] technology on public infrastructure. So
[1:10] that meant always sounds super cool, but
[1:12] it is. I mean, think about all the stuff
[1:14] that public infrastructure entails,
[1:16] right? Roadways, intersections, even
[1:18] airspace. Um, a lot of different
[1:19] technologies work on those things.
[1:21] >> And that's very important because we're
[1:22] living at a time where we're seeing more
[1:23] and more of it, hearing more and more
[1:25] about it. They might be taxi cabs that
[1:28] are flying through this city. So how
[1:31] well all do you help companies prepare
[1:33] us for that type of future?
[1:34] >> Sure. So when it comes to an environment
[1:37] like what is the ideal environment for
[1:38] testing? So you always start in a closed
[1:40] environment, right? There's there's no
[1:42] things coming in in terms of variables.
[1:43] You want to make sure the product works.
[1:45] So in the case of a vehicle on a
[1:46] roadway, that would be like a closed
[1:48] track. But we also know that from there,
[1:50] how do you get into an urban
[1:51] environment? How do you get into a real
[1:53] city? And you've got to have that middle
[1:55] ground. We have a closed three mile loop
[1:57] inside a 500 acre ecosystem so that
[2:00] cars, technology, drones, whatever it
[2:02] is, can test in a real environment where
[2:04] there's real pedestrians, real cars,
[2:06] real people, real intersections. So you
[2:08] can start to get the data and learn how
[2:10] these technologies work in real time
[2:12] with real elements so that you know,
[2:13] hey, we're ready to get into the city.
[2:15] >> I just saw a robot fly by. I don't know
[2:18] what it was, but
[2:19] >> oh my gosh, I think it might have been
[2:20] Cheetah. She's a cute little thing.
[2:22] Basically, she's a helping hand
[2:24] essentially. She is a a robot that
[2:26] follows a subject in front of it. So,
[2:27] it's not a self-driving one. It's not
[2:29] like it's doing delivery, but it's more
[2:31] like, you know, a robotic dog that
[2:32] follows you everywhere.
[2:33] >> You don't need to carry a backpack
[2:34] anymore.
[2:34] >> It's exactly what it is. It's a rolling
[2:36] robotic backpack. You open up the uh vat
[2:39] inside of it, and you know, they have
[2:40] different inserts. I mean, you can make
[2:41] it follow you like a tailgate. Um, but,
[2:43] you know, it also increases
[2:44] accessibility for things like disabled
[2:47] folk or even elderly and, you know,
[2:49] walkable communities if you're at the
[2:50] airport and you need help carrying
[2:52] stuff. Um, so it's cool. The fun fact
[2:54] there is that its design was based off
[2:57] of the droids in Star Wars. So they're
[2:59] they're quite cute, I think.
[3:01] >> Now you're speaking my language. Where's
[3:04] my lightsaber? I feel cars whizzing
[3:06] behind me. So tell me what's happening
[3:08] here. What are we seeing? What are we
[3:09] learning from what we're seeing?
[3:11] >> Yeah, absolutely. So we talk about, you
[3:13] know, the roadway. Um that is a very
[3:16] loaded thing to think about. There's so
[3:18] many things going on at all different
[3:19] times on the roadway. And as drivers,
[3:21] you know, we learn to get used to these
[3:23] things. You learn when someone's going
[3:24] to cut in front of you. You learn how to
[3:26] interact with different lights and
[3:28] different signals and different signs.
[3:29] You know, what we're doing here is we're
[3:30] using uh cameras and we're putting
[3:32] analytics over those cameras to show you
[3:34] what's happening in here. So, um this
[3:36] can be a forum of AI, right? So, it
[3:38] could be doing things like object
[3:39] detection. As you can see here, it's
[3:41] looking at vehicle vehicle. It could
[3:42] clock a, you know, a bicyclist or a
[3:45] pedestrian, which we're seeing a couple
[3:46] pedestrians come right there. So, it
[3:49] says unknown and that's a part of
[3:51] testing, right? Is that, you know, it's
[3:52] still learning. It says person now as
[3:54] it's getting closer to the camera.
[3:56] >> Um, but that's part of it, right? Is
[3:57] there are times where a truck comes
[3:58] through and it and you know, it might
[4:00] say person and you're like, well, that's
[4:01] definitely not a person. But that's why
[4:03] you need the testing environment so they
[4:04] can train their AI models to do better.
[4:06] >> Okay. So, we've spoken about the cities,
[4:08] the roadways, the intersections of
[4:10] tomorrow.
[4:12] Also, there will be the cars of tomorrow
[4:14] that work better with that. And I
[4:16] understand there's a garage where we can
[4:18] maybe check out one of these cars. Put
[4:19] me to work.
[4:20] >> All right, let's do it. Let's go.
[4:22] >> The company testing out their fleet of
[4:24] autonomous vehicles at Curiosity Lab
[4:26] during our visit is called May Mobility.
[4:29] These cars use similar technology to the
[4:31] brands we may be familiar with like
[4:33] Whimo. But this company is particularly
[4:35] interested in making AVs that are more
[4:38] accessible for passengers who use
[4:40] wheelchairs, scooters, or need other
[4:42] accommodations.
[4:43] >> In order for an autonomous vehicle to
[4:45] work, you have to have a bunch of
[4:47] different technology, right? So, you've
[4:49] got to have the hardware, and I'll walk
[4:51] through that in a minute. And you have
[4:52] to have the software, which is pretty
[4:54] much the brains of the technology. So
[4:56] the AI component, you're using
[4:58] human-like reasoning, which is basically
[5:01] the software component, and combining it
[5:03] with all the things you're gathering
[5:04] from the hardware so that the technology
[5:07] can make thousands and thousands and
[5:09] thousands of scenarios and decisions in
[5:11] real time.
[5:12] >> Mhm.
[5:13] >> So you'll see here different
[5:15] technologies. A lot of people say this
[5:16] looks like a cup holder. It is not. It's
[5:20] actually technology.
[5:22] And you'll see cameras here and LAR
[5:24] sensors. We have a total of nine cameras
[5:27] all around the vehicle. And then we have
[5:29] five LARs and radars. The other big guy
[5:32] here, the top hat, is basically um our
[5:35] largest LAR. And that is able to look
[5:38] out further away so that we can see
[5:41] what's happening in real time.
[5:43] >> While autonomous vehicles are relatively
[5:46] new, lidar has actually been around for
[5:48] quite some time. It was developed in the
[5:50] 1960s and it was actually used on the
[5:53] Apollo space missions to map out the
[5:55] surface of the moon. It uses lasers,
[5:59] shoots them out everywhere to measure
[6:00] distances and create an incredibly
[6:02] accurate picture of an environment.
[6:05] Here's how it works. The system fires
[6:07] millions of laser pulses
[6:10] every second in every direction. They
[6:12] bounce off surrounding objects to create
[6:14] a detailed 3D picture of its
[6:16] surroundings. That includes everything
[6:18] from buildings to other cars, people,
[6:20] and animals. It's similar in a way to
[6:23] echolocation, the system used by bats,
[6:25] whales, and dolphins to navigate and to
[6:27] hunt prey. But this system swaps the
[6:30] sound waves for light. You may have seen
[6:32] the rapidly spinning mechanisms on some
[6:34] of these autonomous cars. Well, that
[6:36] allows them to have a 360° field of view
[6:40] to help eliminate blind spots and safely
[6:42] navigate the world around them.
[6:45] Pop quiz hot shot. Self-driving cars
[6:48] often use lidar to scan surroundings.
[6:50] What does lidar stand for? Light
[6:51] detection and ranging. Laser
[6:53] identification and radar. Light
[6:55] identification and response. Looking
[6:57] into dangers around the route. Answer is
[7:00] light detection and ranging. Lidar is a
[7:03] remote sensing method used to examine
[7:05] the surface of the earth. So what you
[7:08] want to do is you want to use a
[7:10] combination of historical data, right,
[7:12] which it's accumulated over time based
[7:15] on different locations. So for example,
[7:18] let's say you're in Georgia, hot
[7:20] weather, right? You're in Phoenix,
[7:24] Arizona, hot but dry weather, and you're
[7:27] in Michigan where you have snow. So, you
[7:30] want to be able to take all of those
[7:32] elements and have the vehicle learn from
[7:34] all the different weather conditions so
[7:36] that when it's a location, if we happen
[7:38] to get snow in Georgia this year, the
[7:41] vehicle will know what to do.
[7:43] >> But I think about, you know, my
[7:45] daughters when they're old enough to be
[7:48] going places,
[7:50] I think that I would feel comfortable
[7:51] with them being in a vehicle all to
[7:54] themselves. The tech is only going to
[7:56] get better, right? And you would think
[7:58] as there are more and more autonomous
[8:00] vehicles on the roadways that are now
[8:03] communicating with each other, they're
[8:05] like computer brains communicating with
[8:07] other drivers. And so you would think
[8:10] that would be even safer than kind of
[8:13] guessing and wondering what a human
[8:15] might do.
[8:16] >> There's a lot of um transparency and
[8:19] visibility into a vehicle. you have
[8:21] cameras everywhere and um so you you can
[8:25] see what's happening and so for them in
[8:28] a lot of ways they're probably a lot
[8:30] safer you know in the vehicle we all
[8:32] know that you know last year I think
[8:35] there were over 40,000 deaths in the US
[8:38] from um accidents road accidents it's
[8:41] things like you know falling asleep
[8:43] texting and driving intoxication so all
[8:46] of those things can happen you know with
[8:49] with your
[8:50] right for the future. So, and I would
[8:53] argue that for the future and maybe even
[8:55] today that these vehicles are for, you
[8:57] know, safe and hopefully that's their
[8:59] future. And then you've got to think
[9:01] too, there's others as well. I mean, you
[9:03] have kids, right? But there's um people
[9:06] with disabilities, great for them. And
[9:09] then, you know, people who are elderly,
[9:11] right? They can come in and use those
[9:12] vehicles. In um Minnesota, we have um
[9:16] one of our biggest groups of riders are
[9:19] seniors and the middle school kids
[9:22] because they
[9:23] >> not seniors in high school.
[9:24] >> No, senior citizens. Yes, that's senior
[9:26] middle schoolers and middle schoolers
[9:28] because you know a lot of times they
[9:30] don't have a way to get home um you know
[9:33] after after school activities and so
[9:36] this is a way for them to do it safely.
[9:38] >> What do you think? I want you to press
[9:40] pause for a moment. Discuss with your
[9:41] friends if a self-driving car service
[9:44] was widely available in your city or
[9:45] town. Maybe they already are. Would you
[9:47] use them? Would you trust AI over
[9:49] getting your own driver's license? What
[9:51] are the risks? What are the benefits?
[9:53] Thanks for taking a ride with me today
[9:54] on this special edition of CNN 10. I
[9:56] learned a lot today. I hope you did,
[9:58] too. Be kind, stay curious, and rise up.