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How Data Science Is Driving Driver-Less Cars?

This entry is part 12 of 20 in the series Data Science

In the past 5 years, driver-less cars have taken shape from being just an idea to an inevitable innovation of the data-driven age. Cars that drive themselves can be seen prowling the streets of California, Michigan, Paris, London, Singapore, and Beijing. Autonomous driving is estimated to add $7 trillion to the global economy!

However, in this blog, we will find out how an application of emerging technologies like data science, artificial intelligence and the internet of things made driver-less cars a successful reality

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How Data Science Is Driving Driver-Less Cars?

When it comes to the perfect blend of artificial intelligence and data scientists working in-sync, there’s no better example than autonomous vehicles. Wonder how?

Let’s go through the simple structure of a driver-less car. Sensors, processors, and actuators, these are the three main hardware in the driver-less car model. Data collected in the form of real-time images and videos of traffic and road conditions are collected by sensors.

This information is sent to processors that instructs the car what to do during navigation through actuators. Actuators are nothing but the tools that supervise the car in controlling its physical components like brakes, tires, or steering wheels so that the drive is a safe one.

Data scientists help develop the algorithms that allow autonomous vehicles in recognizing things like traffic lights, stop signs, etc. A driver-less car needs to know what information it needs to process for a safe drive. Data scientists aid these machines to convert real-life driving experiences into programmable information.

Although still a subject of debate, scientists claim that an autonomous vehicle could generate upto1GB of data/sec. Moreover, when on the road, no driver-less car is driving in isolation. Data from other self-driving cars is exchanged to create a better driving experience. This is largely an application of cloud computing and Internet of Things (IoT).

If you are keen on knowing more about IoT, you should definitely check out this blog.

The Speed Bumps Ahead For Driver-Less Cars

According to the WHO, road traffic injuries are the leading cause of death among people aged between 15 and 29 years worldwide. A majority of these accidents happen due to error on the driver’s part. Self-driving cars are expected to reduce this incidence rate.

However, great expectations are coexistent with challenges too. Below are the common speed bumps ahead for driver-less cars:

  1. Winning the trust of the consumer: Being driven by a machine and placing one’s trust in a car is not something an average consumer can digest. The fact that autonomous cars had met with accidents in the past has already unnerved many. Better machines that are capable of making faster and fool-proof decisions on the road can help win consumer trust.
  2. Improvised government regulations: Autonomous cars would need a whole new set of regulations, unlike conventional cars. Governments across the world should frame more inclusive policies that incorporate these new-age cars.
  3. A dearth of quality talent in developing this technology: Despite a huge boom for data scientists across the world, the number of skilled professionals in AI, machine learning and deep learning is rising only gradually. Driver-less cars need well-trained data scientists who can take autonomous driving places. If this has captured your attention, you should check out Acadgild’s data science courses that will train you to become a data scientist in a short span of time.

Ultimately, the possibilities with autonomous vehicles are endless. An intricate complexity of both artificial and human intelligence lies at the center of this technology.

The success rate of this innovation relies upon the right data, right processing systems and more importantly the right team of data scientists who will propel these driver-less cars into the most efficient means of travel for mankind at large!

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