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enabled by the fourth industrial revolution is the internet of things (IoT) – sometimes called the
“internet of all things”. Some of the key areas of innovations in Industry 4.0 are Implantable
Technologies, Our Digital Presence, Wearable Internet, Ubiquitous Computing, A Supercomputer in
Your Pocket, Storage for All, The Internet of and for Things, The Connected Home, Smart Cities, Big
Data for Decisions, Driverless Cars, Artificial Intelligence and Decision-Making, Robotics. An IoT
gateway device bridges the communication gap between IoT devices, sensors, equipment, systems
and the cloud. By systematically connecting the field and the cloud, IoT gateway devices offer local
processing and storage solutions, as well as the ability to autonomously control field devices based on
data input by sensors. An Edge Gateway sits at the intersection of edge systems, between the external
internet and the local intranet that is being used by the other devices in your ecosystem. Thus it is the
key access point for network connectivity, both inside and outside your device ecosystem. Sensors and
numerous other means of connecting things in the physical world to virtual networks are proliferating
at an astounding pace. Smaller, cheaper and smarter sensors are being installed in homes, clothes and
accessories, cities, transport and energy networks, as well as manufacturing processes. Today, there
are billions of devices around the world such as smart phones, tablets and computers that are
connected to the internet. Their numbers are expected to increase dramatically over the next few
years, with estimates ranging from several billions to more than a trillion. In the process, it will have
transformative impact across all industries, from manufacturing to infrastructure to healthcare. Let us
now see briefly the Evolution of Self Driving Cars employing the concepts of Embedded Systems,
Artificial Intelligence.

S Self driving carself driving cars
Self driving carsSelf driving cars
In the early morning you start from home towards the workplace. You take your briefcase, leave your
house, and step into your car. You press a button and tell: “Okay, take me to the office”. Your car
tracks a route, taking into account which roads will have the least traffic. In the back seat, you have
time to prepare your documents, drink your morning coffee or browse as the car drives itself - safely at
the exact speed limit - to your office. For decades, this was pure science fiction, but self-driving cars
are beginning to enter the sphere of science fact. With Google, Tesla, and others actively working on
their development, we could start seeing them in our driveways by 2020.

T The beginning of the inventionhe beginning of the invention
The beginning of the inventionThe beginning of the invention
In 1925, the inventor Francis Houdina demonstrates a radio-controlled car, which he drives through
the streets of Manhattan without anyone at the steering wheel. According to the New York Times, the
radio-controlled vehicle can start its engine, shift gears, and sound its horn. In 1969, John McCarthy,
one of the founding fathers of artificial intelligence came closest to describing the modern
autonomous vehicle in an essay titled “Computer-controlled cars.” He referred to an “automatic
chauffeur,” capable of navigating a public road via a “television camera input that uses the same visual
input available to the human driver.” He wrote that users should be able to enter a destination using a
keyboard, which would prompt the car to immediately drive them there. Additional commands
allowed the users to change destination, stop at a restaurant, slow down when needed or speed up in
the case of an emergency. No such vehicle is built, but McCarthy’s essay lays out the mission for other
researchers to investigate further.

Neural Networkeural Network-- based autonomous drivingbased autonomous driving
Neural NetworkNeural Network-- based autonomous drivingbased autonomous driving
N
To build an autonomous driving system that was more adaptable across a variety of conditions, neural
network was used. Dean A. Pomerleau describes a learning system, called ALVINN (Autonomous Land
Vehicle In a Neural Network). It is an artificial neural network designed to control the Navlab,
Carnegie mellon’s autonomous driving test vehicle. In 1995, Pomerleau and fellow researcher Todd
Jochem took their Navlab, the self-driving car system on the road.

The grand challenge he grand challenge
T
The grand challenge The grand challenge
In 2002, DARPA announced its Grand Challenge, to researchers to build an autonomous vehicle able
to navigate 142 miles through the Mojave Desert. When the challenge started in 2004, none of the 15
competitors were able to complete the course. The “winning” entry made it less than eight miles in
several hours, before catching fire. It was a set back to the goal of building real self-driving cars.
Parking gets smarter. While autonomous vehicles still seemed quiet away, in the early 21st century,
self-parking systems began to emerge — demonstrating that sensors and autonomous road

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