by Doug Casey, Casey Research:
In today’s essay, tech expert Jeff Brown shares powerful insight into a life-changing technology…and explains why now is the time to start considering investments that will benefit from it…
On a fall day in October of 2011, on a campus secured by the U.S. military, I sat in one of the early versions of Google’s self-driving car.
It was packed with computing equipment, sensors, storage devices, and GPS equipment. On the outside of the car was a large light imaging, detecting, and ranging (LIDAR) array for determining distance to objects, as well as radar devices and cameras. It definitely looked like a prototype.
Today, a mere five years later, cars with self-driving capabilities not only look like ordinary cars… they are already on the road and driving themselves. You just might not have realized it yet.
As of April 2016, Google has 23 Lexus SUVs and 34 other prototype vehicles driving every day. In total, Google’s self-driving cars have logged more than 1.5 million miles.
During those miles there have been only a handful of minor collisions, and in almost every case, the collision was caused by another driver, or when a Google employee was manually driving the car. The car itself was not to blame.
2.4 Million Miles a Day
Tesla has also been an innovator in this space. Its cars can self-drive on highways… they can self-park… they can even be “summoned” from the garage when you are ready to leave your house.
But even more important is how clever Tesla has been in its approach…
Since 2014, it has been building sensors, radar, and cameras into its cars to enable self-driving capabilities. By installing these devices, it has essentially made a network of vehicles capable of collecting real world data.
This data is being used to improve Tesla’s self-driving technology… daily.
Tesla has collected 780 million miles of data during the last 18 months (including 130 million miles in self-driving mode). And it is adding another million miles of data every 10 hours.
By applying artificial intelligence and machine learning to all this data, Tesla is creating a classic “network effect.” Once one of its cars learns something, the entire Tesla network learns it, too.
And it increases exponentially… The more Teslas on the road, the more miles driven, the more data collected, the more improvements to the technology. So much so that Tesla has announced a fully autonomous car will be released by 2018.
Uber, the largest on-demand ride hailing service in the world, has also been heavily investing in self-driving technology. An Uber self-driving vehicle was even seen on the streets of Pittsburgh this May.
The innovative company has already made incredible progress. In May 2015, it hired 40 top researchers and scientists away from Carnegie Mellon’s National Robotics Engineering Center. It was a huge coup, and it put Uber on track to have its entire service supported by driverless cars by 2030.
Not to be left out of the action, Apple launched project Titan to develop a self-driving autonomous electric vehicle.
It also made a $1 billion investment into Uber’s primary competitor in China, Didi Chuxing, which controls more than 87% of the Chinese ride hailing market. While the company has not explained the investment, I believe it was meant to set up Didi Chuxing as a distribution channel for future Apple self-driving vehicles.
Old Names in a New Game
The traditional car manufacturers, while late to the game, have picked up their pace during the last 12 months.
Volvo plans to deliver 100 fully self-driving cars to customers in 2017. It also worked with Daimler (parent company of Mercedes Benz) to test a dozen self-driving trucks. The vehicles spent a week self-driving across Europe to test the technology and to demonstrate something called “platooning.”
The self-driving trucks were connected wirelessly. As a result, they could travel closely together in a line, or platoon, much more closely than if humans were driving.
Platooning can reduce fuel consumption by up to 15%, or about $7,000 of fuel savings per truck per year.
Meanwhile, GM made a $1 billion investment in Lyft, Uber’s primary U.S. competitor, this January. It also acquired a San Francisco startup, Cruise Automation, for more than $1 billion in March. Cruise Automation has been working on an aftermarket technology that converts ordinary cars to self-driving.
And there are many other startups out there…
A new company on the scene is Ottomotto. It was founded by two engineers that worked on the Google self-driving car program and Google Maps. Its strategy is to develop the hardware and software to retrofit existing trucks to become self-driving vehicles.
Another startup that caught my eye is nuTonomy, an MIT spinout, which closed a $16 million Series A round this May.
It is a pure software company and is unique in that it is focused on what is called “level 4,” or a fully autonomous vehicle from the very start. nuTonomy has been working closely with the government of Singapore and is planning to have vehicles operating commercially by the end of this year.
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