The rise of driverless cars underpinned by data
- 07 December, 2015 15:32
The driverless Volvo XC90
After working late to hand off your projects ahead of a long-awaited family holiday, you summon a car with your smartphone app, which normally arrives in two minutes. Since you’ve asked for long-range comfort, it will be at your gate in seven.
As the family piles in, you look forward to getting some sleep as the driverless car pilots itself to your holiday house on the coast.
Sound implausible? Think again. Driverless cars are poised to be unleashed onto our roads and change the traditional car ownership model forever. They will be safely piloted, and at lower cost than driving yourself. Pricing will be competitive and optimised around fleet usage.
Huge volumes of data collected on trips taken, passenger preferences, traffic volumes, and vehicle performance and efficiency will be captured and analysed to optimise utilisation and improve the quality of your experience.
So when can we expect to see driverless cars dominating the roads? Trends such as machine learning, big data and analytics are powering visionary development programs at traditional and non-traditional players in the car industry.
To cite just three examples, Google-developed cars are already navigating familiar roads autonomously, while Toyota’s stepping stone, ‘Highway Teammate’ technology - that enables a car to brake, accelerate, merge and overtake without driver intervention - will be on the road by 2020.
Then there’s Tesla’s auto pilot feature that uses data from a combination of cameras, radar, ultrasonic sensors to automatically steer, change lane and adjust speed in traffic.
So how is this data phenomenon making more cost effective cars available and driving these cars for us going to impact the industry? Analysts predict the impact of driverless cars and the decline of traditional car ownership will be rapid and far-reaching. PwC estimates the number of cars on the road in the United States will fall from 245 million to just 2.4 million.
Infoready’s analysis of fleet kilometres – using data from the ABS and vehicle intensity using taxis as a proxy for driverless cars – found that the availability of a driverless option will move the number of passenger cars from 175 per 100 households to around 60 per 100.
While there may initially be some barriers to driverless cars - such as concerns over the security of any networked systems and the fine-tuning of legal and regulatory frameworks -take up will reach a tipping point beyond which it quickly becomes the new normal.
Data and analytics are becoming increasingly intrinsic to all aspects of our daily lives and the driverless car is the latest example of a rapidly rising tide of disruptions underpinned by data.
Just as banks are evolving from financial institutions to software companies with a banking licence, and the hotel industry is being redefined by the likes of AirBnB.
The car industry of the not too distant future will look very different to what it does today. Traditional car manufacturers like Mercedes, Ford and Toyota are being challenged, and their greatest competitors could become Google, Microsoft and Facebook.
This tide of disruption not only affects individual industries, but related businesses as well. The driverless car is not only poised to revolutionise the automotive industry - it will profoundly change downstream industries such as car finance and insurance, parking, repairs, maintenance and fuelling.
The ripple effect will spread also to road infrastructure and design. How many buildings will need multi-level car parks if driverless cars are continuously on the road? The secondary effects and multiplied economic consequences of a change such as the emergence of driverless cars are profound.
So what lessons should technology decision-makers outside the car industry learn from these developments? Data will drive disruption in your industry.
Understand what the equivalent of driverless cars are in your industry. Identify the opportunities that data disruption can deliver - in your industries and downstream - and develop plans to take them when they arise. Capturing data and using analytics to gain insights is integral to this process.
Alister Lee is GM, analytics at InfoReady