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Vladimir Haltakov Profile picture
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13 Apr, 4 tweets, 2 min read
Self-Driving Cars Thread of Threads 🧠 🚙

I plan many threads on self-driving cars and how to get into the industry.

I will link all of the individual threads that will be focused on a particular topic below.

🧵
I recently gave a talk on AI for self-driving cars for a @DeepLearningAI_ Pie & AI event hosted by @Jeande_d. You can check out the recording on YouTube.



I will be posting threads summarizing the talk and link them below 👇
Learn about the 5 levels of autonomous driving.

The basics software stack of a self-driving car

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Vladimir Haltakov Profile picture
14 Apr
Useful online courses on self-driving cars 🧠 🚙

Here is a list of useful courses if you want to learn about software for self-driving cars.

Some of the courses are paid, but all platforms offer regular discounts and financial aids if you can't affor them.

Thread 👇
Udacity Self-Driving Car Nanodegree

This program offers hands on experience on all kind of relevant topics like perception, localization, planning and control. It takes a lot of time, but it is worth it.

In the end, your code is run on an actual car!

udacity.com/course/self-dr…
Udacity Introduction Course by Apollo

This is a beginner course explaining the different components of a self-driving car. Apollo is the self-driving division of Baidu.

udacity.com/course/self-dr…
Read 8 tweets
Vladimir Haltakov Profile picture
13 Apr
How Does a Self-Driving Car Work? 🔧 🧠 🚙

This is classical self-driving car software stack. Nowadays, all steps in the pipeline are dominated by machine learning.

Read below for details on each step 👇 Image
Sensors 🎥

There are 3 main sensors for environment perception 360° around the car:
▪️ Cameras
▪️ Lidars
▪️ Radars

Each sensor has different advantages and disadvantages, so combining all 3 is the best strategy to achieve maximum robustness. Image
Perception 🖼️

The perception module processes all the sensor raw data to detect different objects, drivable space, lane boundaries, measure distance etc.

Fusing the information from different sensors usually increases the quality of the data significantly. Image
Read 8 tweets
Vladimir Haltakov Profile picture
12 Apr
There are different ways to detect concept drift for computer vision, depending on the application.

You will need to set up monitoring of your model in production and continuously evaluate it.

1/6
You can do spot checks on production data by labeling some small amounts and comparing the performance to the performance on the test dataset.

This is of course a very expensive and error prone approach (see Simpson's paradox).

2/6
You can evaluate some high-level statistics about the performance of your application. Usually the CV model is not the last part of the pipeline.

For example, a car recognizing speed limits may check if the driver actually drives at a speed close to the speed limit.

3/6
Read 6 tweets
Vladimir Haltakov Profile picture
12 Apr
The 5 Levels of Autonomous Driving 🧠 🚙

There is a lot of talk about level 2, 3, 5 self-driving cars, but what do these levels mean? Which level is Tesla at? What about Waymo?

Let me walk you through the different levels in this thread 👇
Level 0️⃣ - No Automation

Just old fashioned cars here - there are no systems assisting during driving. The human driver needs to do all the work.

▪️ Driving -🧍
▪️ Monitoring -🧍
▪️ Fallback -🧍
Level 1️⃣ - Hands-on

The car takes over part of the driving.

Example: adaptive cruise control - the car accelerates and breaks automatically, but the driver needs to steer.

L1 cars have been around for more than 20 years!

▪️ Driving -🧍/🚙
▪️ Monitoring -🧍
▪️ Fallback -🧍
Read 12 tweets
Vladimir Haltakov Profile picture
22 Feb
How high can you score against my new @DilemmaBot at Prisoner's Dilemma? 💪

Just mention it in a tweet and it will start playing with you.

For now, I won't reveal the strategy it's playing, but I'll write about that later and will publish the code as well.
Here some explanation how the game of Prisoner's Dilemma works. The bot will be playing 10 rounds of it.

@apoorv__tyagi already beat it

Read 4 tweets
Vladimir Haltakov Profile picture
17 Feb
What are Convolutional Neural Networks? 🏞️ ⏭️ ⛰️

CNNs are an important class of deep artificial neural networks that are particularly well suited for images.

If you want to learn the important concepts of CNNs and understand why they work so well, this thread is for you!

🧵👇
What is a CNN? 🤔

A CNN is a deep neural network that contains at least one convolutional layer. A typical CNN has a structure like this:
▪️ Image as input
▪️ Several convolutional layers
▪️ Several interleaved pooling layers
▪️ One/more fully connected layers

Example: AlexNet
A good example - AlexNet

Throughout the thread I will be giving examples based on AlexNet - this is the net architecture that arguably started the whole deep learning revolution in computer vision!

I've written more about AlexNet here:
Read 21 tweets

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