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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from  Applied Deep Learning and Computer Vision for Self-Driving Cars

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Challenges in current deployments 

Companies have started public testing with autonomous taxi services in the US, which are often driven at low speeds and nearly always with a security driver. 

A few of these autonomous taxi services are listed in the following table:

Voyage In the villages of Florida
Drive.ai Arlington, Texas
Waymo One Phoenix, Arizona
Uber Pittsburgh, PA
Aurora San Francisco and Pittsburgh
Optimus Ride Union Point, MA
May Mobility Detroit, Michigan
Nuro Scottsdale, Arizona
Aptiv Las Vegas, Boston, Pittsburgh, and Singapore
Cruise San Francisco, Arizona, and Michigan

 

The fully autonomous vehicle announcements (including testing and beyond) are listed in the following table:

Tesla Expected in 2019/2020
Honda Expected in 2020
Renault-Nissan Expected in 2020 (for urban areas)
Volvo Expected in 2021 (for highways)
Ford Expected in 2021
Nissan Expected in 2020
Daimler Expected between 2021 and 2025
Hyundai Expected in 2021 (for highways)
Toyota Expected in 2020 (for highways)
BMW Expected in 2021 
Fiat-Chrysler Expected in 2021
Note: Due to the COVID-19 pandemic, global lockdown timelines might be impacted.

However, despite these advances, there is one question we must ask: SDC development has existed for decades, but why is it taking so long to become a reality? The reason is that there are lots of components to SDCs, and the dream can only become a reality with the proper integration of these components. So, what we have today is multiple prototypes of SDCs from multiple companies to showcase their promising technologies.

The key ingredients or differentiators of SDCs are the sensors, hardware, software, and algorithms that are used. Lots of system and software engineering is required to bring all these four differentiators together. Even the choice of these differentiators plays an important role in SDC development.

In this section, we will cover existing deployments and their associated challenges in SDCs. Tesla has recently revealed their advancements and the research they've conducted on SDCs. Currently, most Tesla vehicles are capable of supplementing the driver's abilities. It can take over the tedious task of maintaining lanes on highways; monitoring and matching the speeds of surrounding vehicles; and can even be summoned to you while you are not in the vehicle. These capabilities are impressive and, in some cases, even life-saving, but it is still far from a full SDC. Tesla's current output still requires regular input from the driver to ensure they are paying attention and capable of taking over when needed.

There are four primary challenges that automakers such as Tesla need to overcome in order to succeed in replacing the human driver. We'll go over these now.

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Applied Deep Learning and Computer Vision for Self-Driving Cars
Published in: Aug 2020 Publisher: Packt ISBN-13: 9781838646301
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