The Birth of Autonomous Vehicles

Despite billions of dollars and over a decade of work, we have yet to see an AV team that is stronger than level 3, or even level 4 — but in a geofenced area with limitations, never mind level 5+ fully autonomous vehicles.

In fact, as of early 2020, over $16 billion has been spent on AVs. (1)

For example, despite raising billions in funding and 12 years of operations, Google’s AV team Waymo has yet to deliver fully autonomous vehicles and mass-scale adoption.

Many other teams have given up or been absorbed by a larger entity.

Some of the remaining active AV teams are:

  • Z00X — It started in 2014 and was bought by Amazon in 2020. Their goal is to create vehicles to be a robo-taxi. It is bi-directional. Amazon also envisions the robotaxis being used to deliver packages.
  • Cruise — They started as a division of GM, but now they have other investors. If you look at you can see what they are doing.
  • Waymo — Started as a Google project, and like Cruise, they have taken outside investors recently.
  • Tesla — It is difficult to know where they really are: beyond Advanced Lane Detection? For years they have said they would be delivering fully autonomous self-drive by the end of the year, however, one of their most recent updates took away radars and several features from some of their models.

There are a few teams who are creating the technology that would be put on a car to make them “AV”.

Argo is a great example.

Udacity has one, too.

The Race to Trucks — for revenue and to make a profit on their investments.

Many of the AV teams discussed above have failed to raise many revenues, much less profit.

Surviving teams have turned their focus and money to trucks. Why?

  • Less variables. Transport trucks largely only drive on highways, predictable routes, and face less unexpected variables than cars during their trips.
  • To have a big truck to go from one warehouse to another warehouse is far easier than training autonomous vehicles to drive in all conditions and all possibilities
  • More profitable for logistics companies. Logistics companies will not have to pay for a truck driver, or worry about regulations that limit the amount of hours a driver can drive without breaks. The drives would also be more consistent and predictable for coordination

It is a smart decision. Embark, Daimler, Einride, TuSimple, Volvo, Tesla, and Waymo are looking to be the leader in the truck AV market.

But they have failed for autonomous cars and public transportation

How Come?

They used the technology that had. Lidar. Radar. All sorts of sensors. And put them to work. Silicon Valley loves it. Look at the start of this company’s URL.

This is a great radar company in Israel. It shows what all of the teams have been doing for the last 11 years.

And look what has happened?


It is easy to see.

Lidar, radars etc, they can all “see” objects…what’s important is to know what to do about it.

Let us start again with a completely different approach:

AI — Artificial Intelligence

There are many forms of AI. But each of them tries to:

  • Understand how people do something or get to some goal, then AI…
  • Use specific approaches to get a better or more reliable answer using a different approach than a person would use.
  • The result, we hope, will be a faster and more accurate answer while keeping the same goal as a person would want.

Example: Playing Chess.

There is a company in London, UK, called DeepMind. They started in 2010. Google bought them in 2014. They do pure research.

Using a variant of AlphaGo Zero (their Software & Hardware), DeepMind:

  • Taught the rules of chess to two parallel copies of AlphaGo Zero. (This is the technology part of it. It knew nothing but the rules).
  • 8 hours later, AlphaGo Zero was playing chess at Grand Master level.

The final report was published in “Science” in 2018. (

The key take-away is that it was not the software and computers simply turned on some software that played chess at Grand Master level. It was that the software that got smarter and smarter, not the programmers.

It is a very different concept to understand — some software and fast computers who, on their own, learned to play against humans, and win. In some way, a human wrote some software that teaches itself to be the best players in the world, in a few hours.

That is Artificial Intelligence, and those who learn to do it have the tools to go right to Level 5 in an AV.

Example: Predict how a car behind us will behave.

Coming down to the world of Autonomous Vehicles, let us show you a piece of our (Turing Motor’s) technology — pure AI — that a driving software can predict the action of that car behind us.

A person: Experienced driver on a freeway can and will behind them and notice a car coming up to in our left-hand lane. It will create a strategy almost instantly.

No software can do that — as we have seen so dramatically on the streets of San Francisco and other towns. It is the reason that every AV team has failed.

But Artificial Intelligence can. Here is a piece of our description how our driving model.

Turing’s AV will be able to drive better than the best professional driver.

Why? That is impossible.

Because a human can know more and faster than any computer. But they can have only a few objects in the brain. It is simply how our brains are built.

But Autonomous Vehicles can keep 25, 50 objects in its “brain.” It is never distracted. Never talking with a friend. So the process of AI may be very different, but the result is better.

And it starts at Level 5 as it should.

In the next few days we are going to write several articles about specific parts of our technology — almost all AI.

“Any technology, sufficiently advanced, is indistinguishable from magic”

Arthur C. Clarke

In the next few weeks, we will be writing articles about the details of AV technology created on a platform of AI.

We are the first of the next generation — success with AI.

This article was written by Ned Goodhue. Ned is the Founder and CEO of Turing Motor Company. Ned is passionate about all things AI. Ned is also passionate about aviation, travelling, understanding different cultures, theatre and reading.

Feel free to leave a comment, whether you agree or disagree, we encourage a collaborative discourse. Thank you.

-Turing Motor Company


Algorithm — A well-defined steps that a computer can do that the author wants. It can be a result or an action.

Analytics — a tool or solution

Attribute — a quality or characteristic of a person, place, or thing

Bombe — the machine that Alan Turing made at Bletchley Park to break the code of the Germany’s Enigma code. The nickname of Bombe was “Victory.”

Calculable — It is possibility to come up with an accurate answer in time to be useful.

Incalculable — The answer will be either wrong or it arrives too late to be useful.

Predictive — foretelling the future. Especially useful in insurance, in a war, or predicting what that car over there will do. This is sometime called Predictive Analytics.

Proactive — A car can be smart enough to tell us something is go wrong before if fails. That goes with parts, too.

Reactive — waiting until something fails.

Restore — to bring (one of our used cars) back to perfect condition. We will do it with all our cars every 3–4 years.


  1. Baldwin, R. (2020, February 10). Self-Driving-Car Research Has Cost $16 Billion. What Do We Have to Show for It? Car and Driver; Car and Driver.