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How do self-driving cars work? The science behind it

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Technological advancements have been vital to the progress and well being of humanity. Development in the automotive industry in terms of safety have been paramount to the increasing reliability of vehicles, and the next step forward as we all know are autonomous vehicles.

This article will inform you about all the basic knowledge in terms of self-driving cars and the science behind it.

A brief history

The Self-driving car is a project that has been in the works for an extended period now. It all started with the United States trying to develop crewless trucks and stopping when it came to having these vehicles deal with on-road traffic at a decent speed.

The above predicament led to the organisation of a challenge series(in 2004,2005 and 2007) by DARPA which brought in tons of students and visionaries with their unique ideas which mix and match all the available technology at that point of time.

The 2004 edition was a massive failure but the competition in 2005 produced five track completions, and in 2007 the vehicles were able to perform road actions and follow safety rules.

The competition was followed by some companies jumping onto the development bandwagon.

Google started its program in 2009 and did so by hiring Urban Challenge veterans. They were able to make enough progress which allowed them to navigate down California’s most intense streets.

Elon Musk’s Tesla soon followed suit with the introduction of ‘Auto-pilot’ in all of their vehicles. At this point all major automotive companies like Mercedes, Nissan and Ford jumped onto the autonomous train along with ride-hailing services like Waymo and Uber.

Also read: Should you trust facial recognition technology? The ups and downs

Current scenario

Auto-pilot by Tesla is a technology that one hears about everywhere, and it is by far the most popular term that can be related to autonomous driving.

How do self-driving cars work? The science behind it
Tesla Model S

At this point in time, Tesla is able to make their vehicles park themselves, come over to their driver via the Summon function, maintain position on roads by accelerating, steering and braking, and perform lane changes and exits on roads without any interaction from the driver — though they must still pay attention to the things happening on the road.

The above example is pretty close to the definition of self-driving, but there are also vehicles by Waymo which are offering shuttle rides in select cities to select passengers with a driver in the seat to oversee vehicle behaviour.

Following the accident in Tempe, Arizona, Uber suspended all testing facilities in Canada, Pennsylvania, California, and Arizona but planned to resume services 18 months after an announcement was made in early 2018.

As far as the current scenario is concerned, the push for further research and development is underway. Semi-autonomous capabilities provided by Cadillac, Nissan and Tesla, are progressing steadily and Waymo with its shuttle service ‘Waymo One’ is taking big leaps.

Smaller companies and start-ups have also started contributing in their way by manufacturing the required sensors or by providing data (for example – mapping data).

Technology used in self-driving cars


Radar stands for Radio Detection and Ranging and finds its use in self-driving vehicles in the form of a sensor that helps in measurement of the distance of the object on the road, its velocity, and certain angular aspects of it.


Light Detection and Ranging is what Lidar stands for, and this technology uses lasers in a pulsing manner to perform its function. It is used to measure and form accurate representations of areas and objects around it to generate the map data for a self-driving vehicle. This helps a vehicle understand what is around it and allows it to be coded in a manner to avoid collisions.

Machine Learning

Road conditions, weather and traffic levels are instances which vary on a day to day basis. Performing routine updations to code and mapping data is not feasible because of which self-driving cars also have machine learning capabilities as this helps them understand changing trends and understand what’s around them.


Odometry is a process whose function depends upon motion sensors. Odometry finds its use in autonomous cars by using the motion sensors present to track the change in position in relation to when the motion started.


SLAM stands for simultaneous localisation and mapping; this is used in conjunction with all the other technological terms mentioned to understand the orientation and precise location of the object in question. Its accuracy is down to the centimetre unlike the GPS used widely across the world.

Inertial Measurement Unit (IMU)

An electronic device, the Inertial Measurement Unit works using a set up consisting of accelerometers and gyroscopes and in some cases magnetometers. It measures the force per unit mass of a body and other quantities like angular rate and the magnetic field surrounding the body.

The future

Self-driving cars as we know them are more or less already here. What the future holds for them are stability, reliability and feasibility.

The hardware that is required by these cars has almost reached a point where this technology can be deemed affordable. The radars and the computer technology needed is available widely, and the equipment used for lidar is slowly becoming less expensive due to multiple start-ups coming up.

The reduction in costs and further growth of this industry may once again revolutionise the world in the same manner as automobiles did and this something that may be a step in the right direction for the greater well being of all citizens.

Also read: What is Fast Charging? 5 common fast charging techs explained

Aryan Surendranath

Has an interest in technology and video games. When not doing anything related to either of these you will find him with his books, educational or not.

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