The Big Data Behind Driverless Cars

From handheld voice assistants to intuitive hiring algorithms, the power of automation and artificial intelligence is revolutionizing the way we live and work on a daily basis.

With so much ongoing investment into new technologies, it’s no surprise that the industry is growing exponentially year after year. According to Forbes, the AI and RPA market is set to hit a a total value of £1.2 billion by the end of 2019, and that figure is expected to almost double by 2021.

There are so many potential applications and usages for AI in development, although it’s hard to look past driverless cars as the modern pinnacle for automated technology. We’ve all heard about the prototypes manufactured by the likes of Google, Waymo and Tesla, yet how much do we really understand about how these autonomous vehicles operate, the problems they solve or when we can expect to see them on the streets? Let’s take a closer look.

Fuel for innovation

Big data has been a well-worn buzz phrase for a number of years now, though there’s no doubt that data analytics is the fuel behind the development of self-driving technology. Autonomous vehicles draw on a combination of historical data and real-time analysis in order to navigate any given situation on the road. Many normal cars are equipped with sensors to collect key information regarding driver behavior, fuel efficiency, collision detection and engine performance – all of which feeds into the automated processes of a self-driving car.

As you can imagine, the model required to manage such large volumes of data is fairly complex. Specialist platforms are normally required to collate and deploy key information when needed, whilst also integrating and embedding data with third-party applications. The data analytics platform offered by Sisense is a prime example, using memory-optimized technology to ensure that any database can scale seamlessly, as data sources and users increase.

These platforms are essential to the success of driverless cars, allowing the vehicles to connect and sync with a large network that is constantly relaying old and new data about the local environment, potential hazards and route analysis. Safety is a primary concern, of course, so the more data available to draw on, the better.

What can we expect by the end of 2019?

It’s important to remember that self-driving technology is still in its relative infancy, though we’re certainly seeing more and more investment in research and development from a variety of multinational companies. You’ve probably heard about the models introduced by Tesla and Google in recent years, though it’s actually Alphabet’s driverless car company Waymo that seems to be making the most immediate progress this year.

Towards the end of 2018, the firm gained regulatory approval to operate a handful of driverless vehicles in Arizona, US, providing that there is a human supervisor on board. At the moment, Waymo are focusing their attention around Phoenix, using the city as a testing ground for their ‘early rider’ programme. In fact, the firm has just announced that 10 additional Waymo vehicles will be available for users of the popular ride-hailing app Lyft over the next few months.

Elsewhere, Tesla founder Elon Musk has also pledged to push forward the development of cars without supervisors - known as ‘robotaxis’. In a recent statement, Musk declared that his company should have a million completely driverless cars on the road next year. It’s an ambitious target, although the rate of progression we’re seeing in self-driving technology means that it remains a distinct possibility.

There’s still a way to go, though the potential to eliminate human error, optimize fuel efficiency and revolutionize the concept of road travel is there for all to see. Let’s see where the next 12 months take us.

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