Business profile Strategies for progressive autonomy
TRL’s Mike Grimsey, Head of Rail and Paul Copping, Corporate Development Director, look at a future rail network that takes full account of autonomy
“Robots acting independently of human control – which can learn, adapt and take decisions – will revolutionise our economy and society over the next 20 years” Rt Hon David Willetts MP, in his paper Eight Great Technologies (Policy Exchange, January 2013)
A
s we look to the future we must wholly embrace the importance and potential of our transport systems from a technological
perspective, in order that we may understand and provide for ever-increasing expectations and demand for travel. In harnessing what seems to be the
unlimited potential of technology to transform how we design, build and operate our transport systems, we have never been better equipped to create ‘the railway of the future’ to solve the many significant practical challenges while meeting our economic, societal and environmental needs. These are exciting times, particularly
when considering the emerging and increasingly market-ready possibilities created through the deployment of autonomous systems.
Vision 2034 In looking ahead twenty years, most visions of land transport have autonomous vehicles being deployed on UK road and rail networks, probably well before 2034. By this time we can expect HS2 to be at least partially in service and many other supporting projects to have been completed, including extensive electrification of both road and rail infrastructure. In this future scenario, the interaction
between road and rail may well change and the passenger experience will become more similar, enabling a mesh network of trains and autonomous road vehicles to provide a coherent, personalised service that reduces congestion in the major network hubs. Personal vehicle ownership will no longer be the norm. Car clubs, shared vehicles and on-demand services are likely to be a significant element of the road fleet – and there will be many urban mobility schemes such as the LUTZ autonomous personal pods being developed for the Redway network of central Milton Keynes.
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A lot of this innovation may still be
in the R&D pipeline, but even in 2014, autonomy is already a well-worn term. It means ‘self-managing’ and comes from the Greek auto – nomos meaning ‘having its own laws’. In the context of the railway, it can refer to many applications including intelligent driver assistance, driverless vehicles, automated traffic management, automated condition monitoring, remote condition monitoring and automated train protection.
Situational awareness These varied applications have some common attributes – they draw on a lot of data; they analyse, make decisions and trigger actions. Sometimes autonomous systems mimic real time human decisions, such as applying a brake, changing a signal or authorising a departure. Increasingly, they will also undertake tasks which are beyond human capability in manual systems, such as running at full speed with reduced headway; managing the track/wheel interface in real time; or adjusting timetables and capacity dynamically to optimise interconnections with other transport modes. What they have in common is that they provide networked situational awareness for all the elements in the system, to enable analysis and decisions. Given the speed of
movement, this network has to operate in real time.
This is part of the future world
anticipated in the current FuTRO challenges to develop a Universal Data model and a Rail Supermap to complement the Rail Infrastructure Network Model and support the real-time and predictive services planned for the second half of CP5 spanning 2014 to 2018. How can we best prepare for this future
which, although it seems distant, will soon be upon us in terms of development life- cycles? Are there any steps we should be taking now to achieve progressive change?
On the loop or in the loop? Let’s start at the sharp end - the driverless train. Until now, aerospace has led the way for such technologies, with widespread adoption of autopilot systems, automated air traffic control and in flight engine management. The three dimensional challenge of sustained flight required avionics system designers to make early decisions to compensate for the processing limitations of the pilot. Biometric studies of aircraft pilots indicate that their most stressful activity is being on the loop in an automated landing, watching to see if all the systems are working. By comparison, manual in the loop landings are less demanding on the pilot.
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