Let's talk a bit about sensors like radar and lidar, how they work, and why they're important for self-driving cars.
We've all heard about the word radar, but what does it actually mean?
Radar is an acronym for RAdio Detection And Ranging. This means it can detect objects and figure out at what range, or distance, they are.
And yes, it's similar to the radio technology we use to listen to music or news.
Radar is great for self-driving cars because it's an active sensor.
The reason it's active is because it produces the very thing it measures: radio waves.
Radar is like an echo or a submarine's sonar. It emits a sound and measures how strong it is when it bounces back off objects.
Small changes in the sound are analyzed to determine nearby objects, their size, and distance.
Radar is like sonar, the only difference being that sonar uses sound to detect objects, while a radar uses radio waves.
Radio waves work on the same principle. The radar emits them, they hit an object, and then they bounce back to the sensor.
The sensor can then compare the sent wave with the received wave to reach conclusions about the environment.
Radio waves are a type of electromagnetic energy. Don't worry about the term. You've experienced electromagnetic energy your whole life.
In fact, every color you've ever seen belongs to the visible spectrum of electromagnetic energy.
Radio waves are part of the electromagnetic spectrum but on the invisible side of it. They're there, even though our eyes can't detect it.
When a wave hits an object and bounces back, we say that the radio wave is reflected.
When it comes to reflection, there's one thing that influences it more than anything else, and that's frequency.
Higher frequency means a stronger signal. Normal radio, for example, broadcasts great music at a frequency near
Radars emit radio waves that are at least
1000 times stronger than that, with frequencies measured in gigahertz(GHz).
There's another sensor that self-driving car makers are working on improving, and that's lidar.
Lidar is an acronym for LIght Detection And Ranging. It's similar to radar but uses electromagnetic waves that are at a higher frequency.
Because of that, the waves are actually made out of light. This light, however, is not visible to the human eye.
Lidar can be very powerful. It's used consistently to measure long distances, like that from the Earth to the Moon.
Lidars are usually mounted to the top of cars and spin
360°. This has a few advantages.
It's cost-effective because only one sensor is needed, while it still provides a wide range of coverage.
Lidar doesn't work well in unfavorable weather conditions such as rain and fog because the light source gets weaker.
Radar, on the other hand, is unaffected by such conditions.
Lidar is more accurate than radar. It can even detect small gestures like a cyclist gesturing to make a left.
Radar wouldn't be able to pick that up.
In the end, both systems have strengths and weaknesses.
A good self-driving car comes down to its ability to combine information from all sensors and have intelligent software to make decisions.
After the car has collected data from sensors, it combines it to come to a logical conclusion about its state and the environment.
Combining all of this data is known as sensor fusion.
Before taking any action, the self-driving car needs to check its database to see if it's encountered a similar situation before.
The car compares the current data from its sensors with data gathered throughout millions of other miles traveled by itself and other cars.
All of this information has been previously processed with techniques such as machine learning.
The next step is figuring out what the car should do next. This process is called path finding.
The main problem with path finding is that the car is not alone on the road.
Self-driving cars need to predict the behavior of all drivers or participants in traffic.
That means detecting pedestrians or cyclists that a human driver might not be able to see.
Getting prediction to work right is tricky because it could lead to a freezing robot problem.
That's when the robot is too conservative and chooses not to take any action, in which case you might get stuck in traffic for hours.
For example, self-driving cars might see a pedestrian going on the road, estimate their speed and trajectory, and stop.
A human would be able to see that it's a driver going to the side of their car to open the door. False alarms like these could be dangerous.
To counteract that, researchers are trying to make cars understand human behavior and copy their behavior in certain situations.
This allows for better predictions and safer roads.
We're not sure when self-driving cars will hit the road everywhere.
But when they will, they'll make our old cars obsolete.