Making Sense of Sensing in Self Driving Cars

Understanding key fundamentals of Unscented Kalman Filters

Autonomous vehicles are getting quite the limelight in recent times. Its research and usage has a tremendous influence in our everyday lives, and will change the way we live and work! The study of self-driving cars — in particular — has the potential to change the very nature of transport and commuting. That includes superior mobility, reduced infrastructure costs, giving respite to traffic related chaos, and of course — lesser accidents!

In this blog post, I hope to help you get a good insight on how some of the components behind self driving cars work. More specifically —

How do self driving cars navigate and make sense of their surroundings?

The ability of a self driving car to sense its surroundings, is of paramount importance. The car needs to be able to sense the presence of other cars and objects on the road, in order to navigate and transport the passenger safely and observe traffic rules.

We’ll begin with learning some of the essential pieces behind sensor fusion, that’s used by self driving cars to track other objects on the road. Here are the contents of this tutorial:

Part 1: (What you’re reading right now):

What are Kalman Filters?

Learn how to develop a process model

So let’s begin!!

What are Kalman filters? When to use them?

Here are the 3 important scenarios where Kalman filters are useful .

Indirect Measurements

Consider the case of monitoring the temperature of the internal combustion engine of a rocket. But the powerful rocket propellant burns with extreme intensity at 5500 degrees Fahrenheit.

This is not an easy task, since a sensor placed inside the chamber would melt. Instead, it needs to be placed on a cooler surface close to the chamber. The problem you’re facing here is that you want to measure internal temperature of the chamber but you can’t. Instead, you have to measure external temperature. In this situation, you can use a Kalman filter to find the best estimate of the internal temperature from an indirect measurement. This way, you’re extracting information about what you can’t measure from what you can.