How can cars become fully independent of human direction? What is the best technology to incorporate into new vehicles and infrastructure? How will humans and vehicles interact with each other and their environment?
These are just a few of the questions facing academics and industrialists who will be working on a new £11 million research programme to develop fully autonomous cars, jointly funded by the Engineering and Physical Sciences Research Council (EPSRC) and Jaguar Land Rover.
The research, which will take place at ten UK universities and the Transport Research Laboratory, was announced today by Secretary of State for Business, Sajid Javid during a visit to Jaguar Land Rover's facility at Gaydon in Warwickshire.
As part of its strategic partnership with Jaguar Land Rover, EPSRC issued a joint call for research proposals that focussed on developing fully autonomous cars Towards Autonomy - Smart and Connected Control. Five projects were selected and Jaguar Land Rover will be leading the collaboration with these successful research groups.
The projects will look into the use of radar and video sensing to interpret the external environment, road conditions and other road users; how drivers will react to new autonomous systems; how systems can be designed to adapt to the personal characteristics of users; investigate how the transition between human control and automated systems can be designed to best effect; how distributed control systems and cloud computing can be integrated with vehicles; and how data from intelligent infrastructure, drivers and automated vehicles can be used to aid interaction.
Business Secretary, Sajid Javid said: "The UK Government has no intention of being a passenger in innovation so is pioneering autonomous car technology in partnership with industry. This £11 million research and development programme and the winning projects are a perfect example of this and will help to keep us at the forefront of the robotics revolution."
Dr Wolfgang Epple, Director of Research and Technology, Jaguar Land Rover, said: "To realise the future potential for fully autonomous vehicles, we need to give drivers, pedestrians and other road users the confidence that a car driving around with little or no human input is a safe, viable and rewarding experience. These collaborative projects will bring some of the UK's leading academics together with our autonomous driving team to address the fundamental real-world challenges that are part of our journey towards autonomous driving."
Professor Philip Nelson, EPSRC's Chief Executive, said; "Science and engineering research is vital to technological innovation and to keeping UK businesses at the forefront of global markets. This joint investment shows how strategic partnerships between the research councils, universities and business can identify industry's challenges and build the academic expertise needed to meet them. The universities and partners in these projects will take novel approaches to safely change the way we travel in the future."
EPSRC is working in partnership to deliver the UK's national strategy in Robotics and Autonomous Systems through such inter-connected investments.
For further information please contact the EPSRC Press Office on 01793 444 404 or email firstname.lastname@example.org
Notes for Editors:
This competition is part of an ambitious, wider programme of activity on connected and autonomous vehicles which includes the Chancellor's Spring Budget 2015 announcement of £100 million collaborative R&D funding (the first £20 million competition for which closed on 30 September); the publication of the Code of Practice, a world-leading innovation for testing these technologies on UK roads; and the creation of a joint policy unit, the Centre for Connected and Autonomous Vehicles, to coordinate and enhance government activity.
The projects are:
TASCC: Pervasive low-TeraHz and Video Sensing for Car Autonomy and Driver Assistance (PATH CAD)
Led by Dr Marina Gashinova
University of Birmingham, Heriot-Watt University, University of Edinburgh
Summary: This project combines novel low-THz (LTHz) sensor development with advanced video analysis, fusion and cross learning. Using the two streams integrated within the sensing, information and control systems of a modern automobile, it aims to map terrain and identify hazards such as potholes and surface texture changes in all weathers, and to detect and classify other road users (pedestrians, car, cyclists etc.).
The project is a collaboration between three academic institutions - the University of Birmingham with its long standing excellence in automotive radar research and radar technologies, the University of Edinburgh with world class expertise in signal processing and radar imaging and Heriot-Watt University with equivalent skill in video analytics, LiDAR and accelerated algorithms.
TASCC: Human Interaction: Designing Autonomy in Vehicles (HI:DAV)
Led by Professor Neville Stanton
University of Southampton, University of Cambridge
Summary: Highly automated vehicles are likely to be on public roads within the next ten years. The largest gap in our understanding of vehicle automation is how drivers will react to this new technology and how best to design the driver-automation interaction.
This project will answer these questions by studying a wide range of drivers with different driving experience in simulators, or test-tracks and in road going vehicles.
The studies will progress from the simulator to the test-track, as interaction and interface designs evolve with testing. On the test-track, driver behaviour physiological and psychological states will be recorded to see what further changes are needed and whether the automation can be even more highly tailored. As the research progresses revised designs will be taken into road going vehicles for the final set of tests.
The team will start by modelling driver behaviour in laboratories to help design inclusive, user-centered, interfaces with vehicle automation. Then they will test the designs out in a driving simulator (which comprises a Jaguar XJ connected to computers with large projectors and screens).
They will test drivers of different ages, gender, experience and capabilities, in a range of scenarios (eg, different road types and environmental conditions) with different automation systems (eg, autonomous driving, auto 'valet' parking, adaptive vehicle personalisation, off-road assistance) and different interfaces.
The design approach will aim to personalise the driver interfaces to the widest range of drivers possible so that the system adapts to the driver, rather than the driver having to adapt to the system.
TASCC: Driver-Cognition-Oriented Optimal Control Authority Shifting for Adaptive Automated Driving (CogShift)
Led by Dr Dongpu Cao
Cranfield University, UCL
Summary: The emerging development of automated driving demands a mutual understanding and smooth coordination between human driver and vehicle controller, so as to avoid conflict and mismatch in demands, and achieve desirable driving performance, smooth and swift transitions which enhance driving safety during complex operating scenarios.
However, such driver-vehicle collaboration during automated driving will impact on the driver's attention and cognition and it is important to consider these effects in order to prevent any negative impact on driving.
This project aims to achieve a safe engagement and smooth and swift control-authority shift between the driver and the vehicle controller during adaptive automated driving. The team will first conduct a comprehensive study of driver attention and cognitive control characteristics when interacting with the vehicle controller. An optimal control authority shifting system which considers driver cognition will then be systematically developed and validated.
This cross-disciplinary research challenge will be addressed using a unique combination of researchers from engineering, cognitive neuroscience and human factors. The research will not only contribute to the cutting-edge technology innovations in automated driving, but will also result in a major advance in the science of human attention and cognitive control when interacting with automation.
TASCC: Secure Cloud-based Distributed Control (SCDC) Systems for Connected Autonomous Cars
Led by Dr Mehrdad Dianati
University of Surrey, Imperial College London, University of Warwick, Transport Research Laboratory
Summary: Automotive industry and the consumers are eager for smart features on new cars and more efficient vehicles. Modern cars are not considered as mere means for travelling from point A to B anymore, but rather smart systems that offer personalised services and have the capability to adapt to the user's preferences and needs. They are expected to become intelligent agents that learn from their environments and exploit various sources of information to become increasingly autonomous systems that relieve the driver from tedious tasks, such as parking, and improve safety, efficiency, and desirability of the future cars.
This ambitious research is defined by a number of world-class academic institutions and leading industrial partners to work with Jaguar Land Rover to design and validate a framework that combines the power of connected vehicles concept with the notion of autonomous systems and build a novel platform for cost-effective deployment of autonomous features and ultimately realisation of connected and fully autonomous cars.
This can be made possible thanks to modern wireless technologies and the power of cloud computing that allows sharing expensive computing resources (hence, reducing costs per vehicle) and provides access to information that are only available on the cloud.
To realise the ambition of the project, a number of key challenges in the areas of ultra-low-latency wireless technologies, cloud computing, distributed control systems, and human interaction issues will be addressed. In addition, potential security threats will be identified and analysed to assess the possible risks for the public and reputational damage for car manufacturers should such technologies be commercialised.
The long term objective is to ultimately enable affordable driver-less cars, in the short term, the project aims to enable a number of demonstrable autonomous features in a test environment.
TASCC: The Cooperative Car
Led by Dr Nathan Griffiths
University of Warwick
Summary: The most significant transition in motoring for a century is beginning as the complex tasks involved in driving become increasingly performed by machine. Individual drivers and their cars will form part of wider and smarter urban transport infrastructure, and the cars of the future will be intelligent and cooperative.
The opportunities to deliver better safety, traffic efficiency, and more productive and pleasant journeys are enormous, but a revolution on this scale faces great challenges for science and society.
Almost imperceptibly to the driver, modern vehicles are equipped with hundreds of micro-computers and sensors, including cameras, radar, GPS, and telemetry measuring everything from speed, braking, and steering to environmental conditions.
Many vehicles have wireless communications (from 2018, new EU cars will have data communication for automated emergency calls) enabling data to be uploaded in real-time to the cloud to be later analysed and used. Current vehicle features operate relatively independently, however such data gives the potential for a vehicle to learn about its driver and environment, and paves the way for integrated intelligent features and eventually for autonomous cars.
Despite significant progress, there are many unsolved challenges, not least related to how such cars will be accepted by the public. So far, autonomous vehicles have been confined to small geographic areas, for example Google's Self-Driving Car relies on detailed data prepared beforehand by human and computer analysis, and is unable to fully cope with adverse weather, road works and other real-world aspects of driving.
There has been little research on: how autonomous vehicles will fit in with today's manually driven cars; how drivers and occupants will interact with them; and how they will run safely in our towns, with pedestrians and cyclists.
Accelerating the transition to autonomous vehicles, this project will tackle scientific challenges whose solutions will deliver some of the convenience, safety and efficiency benefits of future autonomous cars in mainstream vehicles, and will lay the foundation for fully autonomous vehicles.
Jaguar Land Rover has a vision of a self-learning car (SLC) that will minimise driver distractions, enhance safety, and deliver a personalised driving experience. This project will apply advanced research techniques in machine learning and the processing and mining of large data streams to make the SLC a reality.
For example, it will use telemetry and information about the occupants, such as their cognitive load, to personalise the driving experience, predict the destination, adaptively configure safety systems, advise on congestion avoidance and parking opportunities.
In the near future vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) communication will be a reality: cars will know about other cars on the road and be able to exchange information with them.
Cars will become cooperative: with each other and with urban environments. When combined with existing sensors, vehicles will be able to share information on road, traffic and parking conditions.
This project will develop software algorithms, applying experimental methods from behavioural sciences and processing information from connected cars to understand driver habits, and develop strategies to encourage behaviour modifications: for example to design adaptive pricing to reduce parking and congestion. It will also investigate how best human drivers and autonomous cars can interact, for example when taking or handing-over control, or when interacting and negotiating with other road users.
In order to deliver safe and efficient autonomous and semi-autonomous cars of the future, the project will develop intelligent driver systems, and cooperation and behaviour modelling techniques that learn about drivers, enabling vehicles to cooperate with each other and with urban transport infrastructures.
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