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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

Advancements in SDCs

The idea of SDCs on our streets seemed like a crazy sci-fi fantasy until just a few years ago. However, the rapid progress made in recent years in both AI and autonomous technology proves that the SDC is becoming a reality. But while this technology appears to have emerged virtually overnight, it has been a long and winding path to achieving the self-driving vehicles of today. In reality, it wasn't long after the invention of the motor car when inventors started thinking about autonomous vehicles.

In 1925, former US army electrical engineer and founder of The Houdina Radio Control Co., Francis P Houdina, developed a radio-operated automobile. He equipped a Chandler motor car with a transmitting antenna and operated it from a second car that followed it using a transmitter.

In 1968, John McCarthy, one of the founding fathers of AI, referred to a concept similar to an SDC in an essay entitled Computer-Controlled Cars. He mentioned the idea of an automatic chauffeur that is capable of navigating a public road using a television camera's input (http://www-formal.stanford.edu/jmc/progress/cars/cars.html).

In the early 1990s, Dean Pomerleau, who is a PhD researcher from Carnegie Mellon University, did something interesting in the field of SDCs. Firstly, he described how neural networks could allow an SDC to take images of the road and predict steering control in real time. Then, in 1995, along with his fellow researcher Todd Jochem, he drove an SDC that they created on the road. Although their SDC required driver control of the speed and brakes, it traveled around 2,797 miles. 

You can find out more information about Pomerleau at http://www-formal.stanford.edu/jmc/progress/cars/cars.html

Then came the grand challenge by DARPA in 2002, which we discussed previously. This competition offered a $1 million prize to any researcher who could build a driverless vehicle. It was stipulated that the vehicle should be able to navigate 142 miles through the Mojave Desert. The challenge kicked off in 2004, but none of the competitors were able to complete the course. The winning team traveled for less than 8 miles in a couple of hours.

In the early 2000s, when the autonomous car was still futuristic, self-parking systems began to evolve. Toyota's Japanese Prius hybrid vehicle started offering automatic parking assistance in 2003. This was later followed by BMW and Ford in 2009.

Google secretly started an SDC project in 2009. The project was initially led by Sabastian Thrun, the former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. The project is now called Waymo. In August 2012, Google revealed that their driverless car had driven 300,000 miles without a single accident occurring. 

Since the 1980s, various companies such as General Motors, Ford, Mercedes-Benz, Volvo, Toyota, and BMW have started working on their own autonomous vehicles. As of 2019, 29 US states have passed legislation enabling autonomous vehicles.

In August 2013, Nissan pledged that they will be releasing several driverless cars by the end of 2020. Nissan Leaf has broken the record for the longest SDC journey in the UK. This autonomous model has driven itself 230 miles from Bedfordshire to Sunderland. So far, this is the longest and most complex journey that's been taken on UK roads by any autonomous vehicle.

Nvidia Xavier is an SDC chip that has incorporated AI capabilities. Nvidia also announced a collaboration with Volkswagen to transform this dream into a reality, by developing AI for SDCs.

On this journey to becoming a reality, the driverless car has launched into a frenzied race that includes tech companies and start-ups, as well as traditional automakers.

The autonomous vehicle is expected to drive a market of $7 trillion by 2050, which explains why companies are investing heavily to achieve the first-mover advantage.

The market for self-driving vehicles, including cars and trucks, is categorized into transport and defense, based on their application. Transportation is expected to emerge in the future which is further divided into industrial, commercial, and consumer applications.

The SDC and self-driving truck market size is estimated to grow to 6.7 per thousand vehicular units globally in 2020 and is expected to increase at a compound annual growth rate (CAGR) of 63.1% from 2021 to 2030.

It is expected that the highest adoption of driverless vehicles will be in the US due to the increase in government support for the market gateway of the autonomous vehicle. The US transportation secretary Elaine Chao signaled strong support for SDCs in the CES tech conference, Las Vegas, which was organized by the Consumer Technology Association on January 7th, 2020.

Additionally, it is expected that Europe will also emerge as a potentially lucrative market for technological advancements in self-driving vehicles with increasing consumer preference. 

In the next section, we will learn about the challenges in current deployments of autonomous driving.

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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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