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How Tesla Self Driving Car Works?

Want to learn how self-driving cars work and how the future is being built today using AI? Understand the tech behind the 'Tesla Auto Pilot'. Book your session today.

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Description

With the advancements in the automobile industry, there is a need for auto-pilot cars just like airplanes to minimize the chances of accidents due to human errors. The automobile industry is looking to save lives, reduce injuries, and spread mobility equitably. We can do this by addressing the major challenges of systems engineering and design, implementation of more artificial intelligence, sensor improvements, and education of the end consumer.


With the launch of Tesla cars, now it has been possible to drive an autopilot car. Tesla cars come with the hardware needed in the future for full self-driving in almost all circumstances. The system is designed to be able to conduct short and long-distance trips with no action required by the person in the driver’s seat.


In this session, we are going to learn how does a self-driving car scans its surroundings and navigates through a dense road filled with cars, bikes, pedestrians, etc.? Together let's dive into the world of self-driving cars and autopilots and see how the future is being built today using machine learning artificial intelligence.


What you’ll learn in this session?

  1. What is an autopilot?
  2. What are the features of autopilot?
  3. What is computer vision?
  4. Computer vision applications
  5. What is YOLO(You only look once)?
  6. What is Deep Learning?
  7. Deep Learning applications


Who can attend this session?

  1. Anyone who is curious to know about the technology behind the Tesla Autopilot cars
  2. Someone who is interested in Deep Learning and Computer Vision
  3. Students who want to learn the concept of YOLO
  4. Professionals working in the Data Science and Machine Learning domain.


The mystery behind Tesla autopilot car - What is an autopilot?


Autopilot is an optional driver assistance system for Tesla vehicles that you must purchase separately. It's made up of premium safety and convenience features. You can easily buy Autopilot as one of two packages - Autopilot or Full self-driving capability - purchasable directly through your Tesla Account.


Features include the ability of Tesla autopilot cars to accelerate, steer and brake automatically in their lane. Currently, Autopilot car requires driver’s supervision and does not make your vehicle "fully" autonomous.


What are the features of autopilot?


Here are some of the main features of the tesla autopilot car -


  1. Auto Lane Change: It assists the vehicle to move to an adjacent lane on the highway when Autosteer is engaged.
  2. Navigate on Autopilot (beta): This feature actively guides your Tesla autopilot vehicle from a highway on-ramp to off-ramp, including navigating interchanges, suggesting lane changes, automatically engaging the turn signal, and taking the exit.
  3. Auto Park: Helps to park your Tesla vehicle automatically in a parallel or perpendicular direction, with a single touch.
  4. Summon: Moves your Tesla vehicle in and out of a tight space using the app or key.
  5. Smart Summon: Helps your Tesla vehicle to navigate for more complex environments and parking spaces, maneuvering around objects to find you in a parking lot.
  6. Traffic and Stop Sign Control (Beta): Identifies stop signs and traffic lights and slows your Tesla vehicle to a stop on approach, with your active supervision.


What is Computer Vision?


Computer Vision may be defined as a field that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos. 


Let’s assume, we have a two-dimensional image, a computer vision ai system must recognize the objects and their characteristics such as textures, shapes, sizes, colors, among other things, to provide a description of the image as complete as possible.

In this session, we’ll cover how this case study can be one of the next computer vision projects.




Computer Vision Applications 


Here are some of the most popular computer vision examples in real-life practice -

 

  1. Autonomous vehicles
  2. Google Translate app
  3. Facial recognition
  4. Healthcare
  5. Real-time sports tracking
  6. Agriculture
  7. Manufacturing


What is YOLO?


Traditional detection systems use classifiers or localizers to perform object detection. They used to apply models to an image at multiple locations and scales. High scoring regions of the image were considered detections.


YOLO(You only look once) uses a totally different approach. YOLO algorithm applies a single neural network to the full image. This neural network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.


YOLO object detection works by taking an image and drawing lots of bounding boxes. Then it chooses boxes containing any image and then passes it through a process that tells which object is who. The final result is an image with drawn bounding boxes and name of each object.


Applications of YOLO Algorithm-


YOLO is a very fast and robust algorithm. It can be used in several areas to solve real-world problems.

  1. Making of self-driving cars
  2. Agriculture
  3. Crime detection
  4. Fuel stations


Deep Learning Definition


Deep Learning is nothing but a part of machine learning concerned with algorithms inspired by the structure and function of the brain known as an artificial neural network.


You must be wondering how self-driving cars work? These cars work on the principle of deep reinforcement learning and artificial neural networks. 


Deep learning algorithms attempt to draw similar conclusions as humans can by analyzing the data with a given logical structure. To achieve this, lstm deep learning uses the concept of a multi-layered structure of algorithms known as a neural network. In this session, we’ll discuss deep learning vs machine learning. We’ll also cover CNN deep learning models and deep learning techniques. You can add this case study as one of your deep learning projects in your portfolio.


Deep learning applications -


Deep learning is now a part of our everyday lives: from search engines to autopilot cars that demand high computational power. Here are some of the real-life deep learning examples.

  1. Self-driving cars
  2. Sentiment Analysis
  3. Virtual Assistant
  4. Healthcare
  5. Social Media


We hope you’re ready to learn how Tesla is using these technologies to build their autonomous cars. See you in the session!

How Tesla Self Driving Car Works?

Chapter 1

Introduction

1.1 Introduction
1.2 Bad Boy Billionaire
1.3 Tesla Auto Pilot
Chapter 2

Autopilot

Chapter 3

Computer Vision - YOLO

Chapter 4

Extra : Introduction to Goolgle Colab

NoteBook

How Tesla Self Driving Car Works?

Chapter 1

Introduction

1.1 Introduction
1.2 Bad Boy Billionaire
1.3 Tesla Auto Pilot
Chapter 2

Autopilot

Chapter 3

Computer Vision - YOLO

Chapter 4

Extra : Introduction to Goolgle Colab

Notebook

1. Introduction

Chapter 1 Introduction

1.1 Introduction

Tesla was founded as Tesla Motors, Tesla was incorporated on July 1, 2003, by Martin Eberhard and Marc Tarpenning. The two founders were influenced to start the company after GM recalled all its EV1 electric cars in 2003 and then destroyed them, and seeing the higher efficiency of battery-electric cars as an opportunity to break the usual correlation between high performance and low mileage.


Many believe Tesla is billionaire Elon Musk's brainchild. However, the fact is that the company was founded by Eric Eberhard and Marc Tarpenning in 2003. Tesla‘s innovation and technological advancements have been astonishing and highly impressive to not just the automotive industry, but the world. The following graph shows the quarterly sales of their cars.


Certificate

How Tesla Self Driving Car Works?