REVEALED: The Key To Autonomous Driving
AV17 was a huge success.
I gained firsthand information regarding the challenges and opportunities facing today’s players in the autonomous vehicle market, while meeting key figures in the industry.
But the best part? The majority of attendees of this conference were engineers. There were no calculated marketing pitches, just raw insights coming straight from the people that build these machines on a daily basis.
And this year, I noticed a common challenge perplexing many of the engineers — which is an opportune way for us to invest in the autonomous vehicle space…
Autonomous vehicles seem difficult. But in reality, they’re comprised of many simple parts all working together to perform a common task.
Take a look at the pictures below of one vehicle on display at the conference. As you can see, there are multiple sensors located on top of the car, as well as others in the front and rear.
These vehicles use sensors like LIDAR and RADAR technology to map the surrounding area including buildings, other vehicles, road signs, lane lines, pedestrians, etc. This information is then relayed through specialized software that determines how the vehicle will react.
These sensors will play a major role in putting autonomous vehicles on the road. But what makes these sensors even more important is their ability to collect data.
That’s because data is the key to self-driving cars – an inside piece of information that our investment strategy exploits.
There are two reasons why data is so important…
Reason #1 Why Data Is So Important: Teaching The Car To Drive Itself
Right now we are in the early stages of testing autonomous vehicles. If you live in California or certain parts of Michigan, you may have seen self-driving cars already on public roads.
Most of the time, these cars are either empty or being driven by a single engineer. They’re not transporting passengers or even cargo. And there’s usually not even a specific destination in mind. The car is just driving for the sake of driving. Why?
It’s because these cars need to “learn” now to drive on public roads. They need to “learn” how to react in different situations — like what to do at a crosswalk, a bus stop, when a basketball goes rolling across the road, and in the thousands of other situations that human drivers experience on a daily basis.
And in order for these machines to “learn” how to react, they drive around collecting massive amounts of “experience” which is translated into data. This data is then stored and analyzed in order to update the procedures of the vehicles when it encounters similar situations.
Here’s an example I heard at the conference: Say an autonomous vehicle is following a bus that is halfway on the shoulder picking up passengers. This is a situation that the vehicle has no data for, so it stops behind the bus and waits for it to move.
However, you and I would likely make sure the next lane is clear and then go around. In cases like this, the vehicle makes a note of the unusual situation which is sent to programmers who can update the procedures.
And this is done for the thousands of different situations that drivers like you and I experience every day. But these programmed procedures aren’t just based off one instance. The same situation is re-enacted thousands of times in order to get the algorithm right — which requires a lot of data…
Reason #2 Why Data Is So Important: On The Road Connectivity
This refers to how autonomous vehicles will communicate with other autonomous vehicles on the road in the future. This makes the data collected after cars learn to drive just as important as the data being collected now — which is why the companies that I’ll get to in a moment are great long-term investments.
Here’s the premise…
In order to increase safety and efficiency, a common goal among producers is to link data between cars. This will allow cars to essentially talk to each other — sharing information on matters such as road conditions, traffic, and intent.
Here’s an example: All the cars on the highway are autonomous, and roadwork is closing one lane ahead. An entire lane of cars needs to merge. How do autonomous vehicles anticipate the intentions of other vehicles?
That’s where inter-car connectivity comes in. In the future, the merging cars will be able to transmit data to each other regarding their intentions. But this will need to be done for every possible situation that can occur while driving, which will require even more data…
From an investment standpoint, there are multiple ways to take advantage of the massive data demand that autonomous vehicles will bring…
Telecom Companies- Going back to car connectivity, the speed at which cars will need to communicate with each other is extraordinary. Although the example I gave may have seemed like merging in bumper to bumper traffic, some situations are much quicker — like changing lanes on the highway or crossing the double yellow line to pass a stopped bus. Right now, Verizon (NYSE:VZ), is best suited to accommodate this quick transfer of data after acquiring Straight Path Communications, making them a leader in the 5G technology space.
Big Data Platforms- These are the companies that are equipped to handle the massive amounts of data being gathered from autonomous vehicles. Companies like Hortonworks (NYSE:HDP) are currently providing a platform for autonomous car producers to analyze data and make the appropriate changes to vehicle procedures. As the number of miles driven grows, companies like HDP should become more valuable.
Data Center REITs- This has been one of the hottest sectors of the market over the last year. These companies house the massive computers that are able to store and analyze this data necessary for autonomous driving and many other technologies. And even better, REITs are required to pay out 90% of income to shareholders, which equates to large dividend payments. Right now, my favorite stock is CoreSite Realty (NYSE:COR). They provide smaller data centers focused on high connectivity — perfect for data transfer between autonomous cars with a 3.11% dividend.
I’ll have more on the autonomous vehicle space in the coming days. But for right now just remember, big data is the key to putting these cars on the road.
Here’s to keeping your edge,