The very first self-driving car used Neural Networks to detect lane lines, segment the ground, and drive. It was called ALVINN and was created in 1989.

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Autonomous Land Vehicle In a Neural Network

Great Scott! Neural Networks were already used back in 1989! The approach was End-To-End: you feed an image to a neural network that generates a steering angle. Et voila.

In 2021, it’s even crazier. Deep Learning has taken over the major subfields of autonomous driving.

In this article, I’d like to show you how Deep Learning is used, and where exactly.

In order to do this, I will go through all 4 pillars of…


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Cruise Automation is a self-driving car company whose goal is to deploy safe self-driving cars at scale.

Founded in 2013, they develop self-driving cars that can take passengers from A to B in a safe and comfortable way. I got interested in their system and watched a few videos and interviews to get back to you with a concise analysis.

What’s behind their system? What algorithms are in use? Can they compete with Waymo and Tesla to provide autonomous driving at scale?

Let’s take a look at Cruise Automation…

📩 Before we start, I invite you to join the mailing list by leaving you email below! This is the most efficient way to understand autonomous tech in depth and join the industry faster than anyone else.

How it started

Cruise was founded around 2013 by Kyle Vogt and Dan…


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(source)

Bon Voyage — From French, it translates to Good Journey in English. Voyage means to travel and the company we are going to review today, ‘Voyage’ offers the same and has become one of the most relevant self-driving car company in the US in just a few years.

Voyage has been built by an Udacity Alumni named Oliver Cameron. He’s both an alumni, and the person behind Udacity’s self-driving car Nanodegree program. (Check my 2020 Review of the course here).

In one of his intervention on Lex Fridman’s MIT channel, he talks about how he created Voyage (most images will…


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Robotic OS has been widely used for years as the go-to solution for any company or student building an autonomous robot. As I am teaching more and more people about self-driving cars and Computer Vision, I thought it would be interesting to try and see how we can break the frontier between an online course and the real world…

Most online courses on Computer Vision will tell you how to work on the image, very few will talk about the camera. …


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Deep Learning and Computer Vision have been insanely popular in autonomous systems and are now used everywhere. The field of Computer Vision grew intensely over the last decade, especially for obstacle detection.

Obstacle detection algorithms such as YOLO or RetinaNet provide 2D Bounding Boxes giving the obstacles’ position in an image using Bounding Boxes. Today, most object detection algorithm are based on monocular RGB cameras and cannot return the distance of each obstacle.

To return the distance of each obstacle, Engineers fuse the camera with LiDAR (Light Detection And Ranging) sensors that use laser to return depth information. …


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The autonomous tech world has 2 giants: Tesla & Waymo.

In my last article, I broke down Tesla’s computer vision system.
👉 You can find that post here.

Today, we’ll take a look at what Waymo, Google’s self-driving car division, is doing under the hood.

Waymo has driven more than 20 million miles on public roads in over 25 cities. They also drove tens of billions of miles in simulations (as we’ll see later in the article). Additionally, Waymo is operating a taxi service in the United States, transporting passengers—for real—without a driver.

Given their growing presence in the real…


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Photo by Vlad Tchompalov on Unsplash

When we look at the computer vision world, we can see a real gap between reality and online courses.

For instance, you might learn in an online course how to run a YOLO network, but a real-world use case might asks for 7 YOLO networks in distributed GPUs and a HydraNet architecture. What the heck is this? We didn’t learn all of this on Coursera!

So today, I decided to dissect a video from Andrej Karpathy, AI Lead at Tesla, and explain to you what’s happening inside Tesla’s software. …


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The Udacity Self-Driving Car Engineer Nanodegree has been a very popular way for students to learn about autonomous driving technologies and find a job in the field. Thousands of students enrolled, and some got out with the job.

I am one of them, and today, I will share with you my review of the program.

There is just this one thing: I enrolled 3 years ago and graduated 2 and a half years ago! The program changed a bit, I looked for students who just graduated to see the changes and made my review accordingly.

Questions I will answer:

  • Is the Nanodegree worth it?


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RADARs is a sensor that we use since the 1930s. Originally, it was used by the military to detect ennemy aircrafts. It has been a tremendous sensor during wars, used by every side. Today, it’s a technology used in vehicles for Adaptive Cruise Control, and in autonomous vehicles for obstacle detection.

Let’s see how RADARs work, what are the strengths and weaknesses of this sensor, and how it can be used to detect objects positions, classes, and velocities.

Let’s take a look at Radio Detection And Ranging.

Before that

I invite you to join the Autonomous Thinkers and receive daily content on…


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Today, we’ll talk about something that is barely covered in the Autonomous Tech world…
It’s also something I’m not familiar with, so I’ll just be able to talk about it. Technology has enabled us to build self-driving cars, but actually, the most impactful changes will be around flying cars, and sea navigation.

This article is based on an email I wrote to my mailing list — The original email is called “Startup Idea — Autonomous Boats”; it’s a concept where I explore the necessary steps we’ll need to build a startup around a specific topic.

📩 Join the mailing list…

Jeremy Cohen

AI & Self-Driving Car Engineer —I teach people how to join the Autonomous Tech world! https://www.thinkautonomous.ai

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