Technology / How data lies at the heart of the driverless car revolution
How data lies at the heart of the driverless car revolution
9 May 2016 |
Business Reporter's Matt Smith talks to the experts about the potential of data-fuelled driverless cars.
On a weekday morning, you step out of your front door in London’s suburbs and climb into a driverless car. Your journey to work in the city is smooth, as the autonomous vehicle follows a route designed to avoid congestion and red lights. Your journey is much quicker than it used to be, thanks to an expertly managed transport system fuelled by big data analytics.
This is the vision of the future touted by those behind the technology, but how close is it to reality, and how effectively are governments and firms using the road data available?
For Dr Steve Melia, senior lecturer in planning and transport at the University of the West of England (UWE) and author of Urban Transport Without The Hot Air, the potential is there, but these organisations must first have access to the data and the means to process it.
“The question is usually about access to it – the practicalities of obtaining it and who has the capacity and the time and the resources to do what with it,” he says. “There are a number of big projects that are looking into those issues.The potential for research – to better understand how vehicles behave in a city, for example – that is something everyone has recognised the potential of. There’s been a lot of talk and there are a small number of projects, but it’s a massive job.”
One obstacle is that data that could be used to bring about more efficient and sustainable transport is held by private companies, which either view it as confidential or simply lack any business incentive to share it. “One of the major issues with all this is intellectual property and who owns the data,” Melia says. “A company such as Uber would collect an awful lot of data, but it’s probably not going to be very motivated to share it with other parties.”
That said, even with the right data in the right hands, there is no guarantee our transport problems would be solved. The potential of self-driving cars, for example, has filled many column inches, but it is difficult to predict exactly how a technology will change the world.
“It’s one thing getting the technology to work, but it’s actually at least as big a challenge to work out how the world would operate if there were lots of these things moving about the roads,” Melia says, before explaining one of the potential unexpected pitfalls.
“One of the negatives from a capacity point of view is that if respecting all the rules was actually built into the vehicles, that would significantly reduce road capacity in many ways, because in reality most of the time most drivers don’t respect those sorts of guidelines.”
As well as large-scale analysis to tackle capacity issues, there is also potential for automated cars to share data with each other – a prospect that Professor Graham Parkhurst, professor of sustainable mobility at UWE, says would open up “all kinds of possibilities”.
“If vehicles were more connected together in terms of their operating systems then you could have platooning, which is running vehicles very close together because their braking systems are interlinked,” he says of data-sharing vehicles travelling on high-speed roads.
While this could increase the capacity of motorways, for example, Parkhurst says that in urban areas where vehicles are closer together anyway the data shared could enable self-driving cars to synchronise their journeys with traffic lights and cut energy waste.
“In principle you could optimise vehicle speeds so they arrive at the traffic lights to find they are ideally on green… so you are not braking and accelerating, which is bad not only for carbon emissions but also for noxious air pollution,” he says.
At a management level, increased data collection could enable those in charge to build more accurate predictions of congestion at a given time. Think of the traffic data in Google Maps, Parkhurst says – but more detailed, and available to inform road management.
“One of the big ideas is that we can use predictive analytics to work out, based on where people travelled before, where the problems and congestion is likely to occur on future similar days,” he explains. “The idea is that transport system managers, if they had these data analytics, could be pre-emptive in their management.”
There is certainly potential for data to change the way we travel, but there are still challenges to be overcome to bring these ideas into the real world. One of these challenges is the volume of data available, which can be too much for firms and governments to process quickly.
“I think one of the challenges is that the sheer speed at which this technology and the availability of data is changing will exceed the capacity of organisations to make full use of it,” Melia says. “So to what extent it is used and in what ways I think will probably depend on more traditional issues of politics and finance.”
And despite the technology available and the potential of big data, Melia points out that it is not an automatic solution to problems surrounding congestion and the environment.
“Technology itself does not solve human problems,” he says. “I tend to feel the technology itself is neutral… Whether technology leads to an improvement or to a worsening of the situation depends on how human beings use it.”