How Does a Self-Driving Car Work? ๐Ÿ”ง ๐Ÿง  ๐Ÿš™

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 ๐Ÿ‘‡
Sensors ๐ŸŽฅ

There are 3 main sensors for environment perception 360ยฐ around the car:
โ–ช๏ธ Cameras
โ–ช๏ธ Lidars
โ–ช๏ธ Radars

Each sensor has different advantages and disadvantages, so combining all 3 is the best strategy to achieve maximum robustness.
Perception ๐Ÿ–ผ๏ธ

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.
Localization ๐Ÿ—บ๏ธ

Most self-driving cars use a HD map. The map provides information about the geometry of the road, traffic rules and position of interesting objects (e.g. traffic lights).

Localization in the map is done using GPS and landmarks detected by the sensors.
Prediction ๐Ÿ”ฎ

The goal of this module is to predict the actions and trajectories of other traffic participants.

Will the car in front break?
Will the car cut in front of me?
Will the pedestrian cross the street?

This is crucial for the car to plan its own trajectory!
Planning ๐Ÿ”€

In this step the car plans its own trajectory and actions. It needs to consider all other traffic participants, their intentions and the surrounding infrastructure.

One of the main objectives when planning is to maximize safety, but also to drive naturally.
Control ๐Ÿš™

The final step is controlling the throttle, breaks and steering so that the car actually drives the planned trajectory.

Here, it is important to have a smooth and natural control and not a steering wheel that twitches all the time.
If you liked this thread and want to read more about self-driving cars and machine learning give me a follow! ๐Ÿ‘

I have many more threads like this planned ๐Ÿ˜ƒ

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More from @haltakov

15 Apr
Open-Source Self-Driving Car Simulators ๐Ÿ•น๏ธ ๐Ÿš™

You want to play around with self-driving car software and gather some experience? Check out these open-source self-driving car simulators!

Details below ๐Ÿ‘‡
CARLA

CARLA is a great software developed by Intel. You can use it to work on any step of the pipeline, model different sensors, maps, traffic. It also integrates with ROS.

carla.org
Deepdive from Voyage

Another great simulator by Voyage - the self-driving company that was recently acuired by Cruise. It is built on the Unreal Engine and supports lots of features.

deepdrive.voyage.auto
Read 8 tweets
14 Apr
Useful online courses on self-driving cars ๐Ÿง  ๐Ÿš™

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 ๐Ÿ‘‡
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-drโ€ฆ
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-drโ€ฆ
Read 8 tweets
13 Apr
Self-Driving Cars Thread of Threads ๐Ÿง  ๐Ÿš™

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.

๐Ÿงต
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 ๐Ÿ‘‡
Learn about the 5 levels of autonomous driving.

Read 4 tweets
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
12 Apr
The 5 Levels of Autonomous Driving ๐Ÿง  ๐Ÿš™

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 ๐Ÿ‘‡
Level 0๏ธโƒฃ - No Automation

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

โ–ช๏ธ Driving -๐Ÿง
โ–ช๏ธ Monitoring -๐Ÿง
โ–ช๏ธ Fallback -๐Ÿง
Level 1๏ธโƒฃ - 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!

โ–ช๏ธ Driving -๐Ÿง/๐Ÿš™
โ–ช๏ธ Monitoring -๐Ÿง
โ–ช๏ธ Fallback -๐Ÿง
Read 12 tweets
22 Feb
How high can you score against my new @DilemmaBot at Prisoner's Dilemma? ๐Ÿ’ช

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.

Read 4 tweets

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