The two types of self-driving technology every investor needs to know

Editor’s note: Tesla’s ‘Robotaxi’ event on October 10 marks the start of a new era for autonomous transportation. It is one that will have far-reaching consequences for the entire economy.

By making autonomous vehicles a reality, Tesla opens the door to tectonic disruption – and brings with it vast new opportunities. My InvestorPlace colleague Luke Lango thinks he’s seen one, so I encourage you to do so too Register here for his 10 a.m. broadcast on Monday, October 7.

Tomorrow’s AV technologies are about to make their way into today, and the shift will be like nothing we’ve ever seen before. That’s why I’ve invited Luke here today to tell you more about those technologies… and to preview his October 7 broadcast.

My friends with kids in college now tell me that their kids pretty much grew up thinking they would never have to drive again because of self-driving cars.

My friends with kids in high school tell me the same thing now.

The people I know with high schoolers, though… those kids have always done that known they should sign up for a driver’s ed.

Silicon Valley can only tell us that self-driving cars are still “five years away from five years away” before we doubt their story.

Still, I have been optimistic about autonomous vehicles for some time. After all, developments in the sector are promising.

And here’s the thing… Maybe those kids who are now in high school don’t have to learn to drive.

These self-driving technological developments are quickly moving from promising to reality.

You’re probably aware of the rapid expansion of autonomous taxi services in Phoenix and San Francisco. But did you know that Waymo started offering driverless rides to some people in Austin Today?

You may have heard about the rollout of autonomous driving in Texas and Arizona. But did you know that California Governor Gavin Newsom vetoed a bill that would ban self-driving trucks from driving on his state’s roads? Monday?

And with the upcoming launch of Elon Musk’s robotaxi just a week away – on Thursday, October 10 – I believe the stage is cleared for self-driving cars to transform the $11 trillion transportation services industry. (Register for my October 7 morning broadcast on that event – and how you can position your portfolio to benefit from it – by going here.)

That’s all great information to know. But it doesn’t mean much if we don’t understand how these vehicles actually work.

After all, concept the technology behind a growing megatrend is critical… before we can begin benefit of it.

Therefore, to possibly make the “Age of Avs” a great success, we must first understand how self-driving cars work…

A sensor trifecta

At its core, a self-driving car is controlled by a combination of sensors – the ‘hardware stack’ – and AI-powered software – the ‘software stack’.

In short: the car’s sensors collect information about the environment. Then the AI software processes that data to determine whether the vehicle should accelerate, brake, change lanes, turn, etc.

This all needs to happen almost immediately.

Typically, the hardware stack consists of three sensors: cameras, radarAnd lidar. A typical self-driving car uses all three sensors, as each has strengths and weaknesses that complement the others nicely.

Let’s go through them one by one…

Cameras collect visual data. They capture high-resolution images of the vehicle’s surroundings, similar to how our own eyes work. These cameras recognize different signs, lane markings and traffic lights – and can distinguish between different objects, such as pedestrians, cyclists and vehicles. They are very good at providing detailed visual information, helping the car understand the context of its surroundings. But they typically perform poorly in poor visual environments, such as low light or bad weather.
An AV radar sensors emit radio waves that bounce off objects and return to the sensor, providing information about the distance, speed and movement of obstacles in the car’s vicinity. These sensors work well in all weather conditions (a nice addition to cameras), but they offer limited resolution and detail (where cameras excel).
Lidar – which stands for light detection and ranging – is essentially radar powered by lasers. These sensors emit laser pulses that bounce off surrounding objects and return to the sensor. By measuring the time it takes for the light to return, lidar can create a high-resolution 3D map of the vehicle’s surroundings. This provides accurate depth perception, allowing the car to understand the exact shape, size and distance of surrounding objects. However, lidar does not capture color or texture information (like cameras do).

In other words, AVs use cameras to see things. Radar senses how fast those things are moving. And lidar helps calculate the exact position of those things.

In this sense, it is easy to see how these three sensors work together within a self-driving car.

The era of autonomous vehicles has arrived

Self-driving cars use so-called ‘sensor fusion’ to combine camera, radar and lidar data, creating a complete, accurate and reliable model of their environment.

For example, if someone crosses the road in front of an AV:

The camera recognizes it as a person.
The radar tracks the pedestrian’s speed to predict possible collisions.
The lidar measures the exact distance, shape and movement of the pedestrian.

Together, these sensors allow the car to make informed decisions such as slowing down, stopping or rerouting, ensuring safe and efficient navigation.

But it can only make these decisions using its software stack.

An AV uses a variety of software and methods to provide real-time information about its environment. There are essentially five components in this software stack: perception, localization, prediction, planningAnd control.

In short:

The perception software uses sensor fusion, object classification and semantic segmentation to create a comprehensive view of a car’s surroundings.
The localization software uses highly detailed maps and location data to accurately place the car in its environment.
The prediction software uses machine learning models to predict how things in the environment might behave in different scenarios.
The planning software uses the outcomes of the perception, localization and prediction software to determine an optimal path for the car.
The control software carries out the planned action and controls the car’s steering, acceleration, braking, etc.

Together, these hardware and software stacks create the technological backdrop for self-driving cars.

The last word

Of course, every company tackles the self-driving problem differently. But this is the general framework that most people follow.

Therefore, if you want to invest in the autonomous vehicle supply chain, it makes sense to look for stocks that supply the crucial components of AVs.

Find the strongest camera, radar and lidar providers. Focus on the most promising software games.

They will likely be the biggest winners in the age of AVs.

If you’re hoping to be positioned for an era of AV-driven market gains, click here to save your seat for my special video briefing on Monday, October 7 at 10am Eastern. There we will talk about the rapidly unfolding era of AVs.

This upcoming broadcast is all about preparing for Tesla’s Robotaxi launch later this week (which we expect to be huge). Although it is about much more than that upcoming debut.

In this broadcast, I will detail all the recent groundbreaking developments in the autonomous vehicle industry, including how robotaxis will completely transform transportation, save millions of lives, and potentially put up to $30,000 a year in passive income in your pocket. .

Plus, I’ll show you how to get my playbook on the best AV stocks to buy now.

Click here to reserve your seat.

And I’ll see you there.

Honestly,

Greetings,

Luke Lango

Editor, Hypergrowth investing

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