Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

In 2005, the Vehicle and Operator Services Agency (VOSA) introduced a computerised system for reporting MOT (roadworthiness) test results. Since that time, the results of approximately 35,000,000 MOT tests annually have been collected and stored in a Department for Transport (DfT) database. The DfT business plan , published 8 November 2010, promised to make available the "detailed VOSA MOT data" - and on 24 November, comprehensive data was released - consisting of the results of 150,000,000 MOT tests from 2005 to the spring of 2010. Some fields, such as vehicle registration plates and unique VTS (vehicle test station) identities have been withheld from the published data in order to preserve anonymity. However, what remains still contains a wealth of information that is not available in any other data set. In addition to the results of the MOT test itself (including detailed reasons for failure), the data include: - the vehicle odometer (mileage) reading - the vehicle manufacturer, type and engine capacity - the vehicle's year of first use - the top-level postal area (letters only from the postcode) of the VTS Our initial objective is to use the vehicle odometer readings - which are not available in any other (large scale) data set - combined with the data about vehicle type, to analyse how patterns of vehicle usage (and associated carbon footprint) have changed with time, disaggregated over different regions of the country. The project will therefore aim: - to develop software tools for the analysis of the MOT data; - to work with the DfT and VOSA on maximizing the use that can be made of the MOT data set whilst respecting issues such as data protection; - to scope the application of MOT odometer readings and the possibilities for triangulating with other data sets (such as vehicle emissions, new vehicle registrations and Census data); - to develop one (or two) small-scale demonstrations illustrating potential applications of our approach. The ultimate aim, going beyond the scoping study, is to create a publicly available tool that all those undertaking travel behavior change initiatives could use to assess the impacts of their work on car ownership, use and related carbon emissions, thereby dramatically reducing the need for every individual project to commission surveys or other forms of travel behavior measurement. Further research could also include specific analyses of: changes in car ownership and use that have occurred in the Sustainable Travel and Cycling Demonstration Towns; the nature of the distribution and diffusion of electric, hybrid and other alternative-technology vehicles; the location and concentration of 'dirty' vehicle use with implications for the targeting of climate change and air quality initiatives; and the relationship between car use and physical activity.

How does a racer drive around a track? Approaching a bend in the road, a driver needs to monitor the road, steer around curves, manage speed and plan a trajectory avoiding collisions with other cars - and all of this, fast and accurately. For robots this remains a challenge: despite progress in computer vision over the last decades, artificial vision systems remain far from human vision in performance, robustness and speed. As a consequence, current prototypes of self-driving cars rely on a wide variety of sensors to palliate the limitations of their visual perception. One crucial aspect that distinguishes human from artificial vision is our capacity to focus and shift our attention. This project will propose a new model of visual attention for a robot driver, and investigate how attention focusing can be learnt automatically by trying to improve the robot's driving. How and where we focus our attention when solving a task such as driving is studied by psychologists, and the numerous models of attention can be sorted in two categories: first, top-down models capture how world knowledge and expectations guide our attention when performing a specific task; second, bottom-up models characterise how properties of the visual signal make specific regions capture our attention, a property often referred to as saliency. Yet, from a robotics perspective, there remains a lack of a unified framework describing the interplay of bottom-up and top-down attention, especially for a dynamic, time-critical task such as driving. In the racing scenario described above, the driver must take quick and decisive action to steer around bends and avoid obstacles - efficient use of attention is therefore critical. This project will investigate the hypothesis that our attention mechanisms are learnt on a task specific basis, in a such a way as to provide our visual system optimal information for performing the task. We will investigate how state-of-the-art computer vision and machine learning approaches can be used to learn attention, perception and action jointly to allow a robot driver to compete with humans on a racing simulator, using visual perception only. A generic learning framework for task-specific attention will be developed that is applicable across a broad range of visual tasks, and bears the potential for reducing the gap with human performance by a critical reduction in current processing times.

Traffic jams are an annoying feature of everyday life. They also hamper our economy: the CBI has estimated that delays due to road traffic congestion cost UK businesses up to 20 billion annually. UK road traffic is forecast to grow by 30% in the period 2000-2015, so it seems that the congestion problem can only get worse. There is consequently an intense international effort in using Information and Communication Technologies to manage traffic in order to alleviate congestion --- this broad area is known as Intelligent Transport Systems (ITS). Regular motorway drivers will already be familiar with ITS. Examples include 1. the Controlled Motorways project on the M25 London Orbital (which sets temporary reduced speed limits when the traffic gets heavy); 2. Active Traffic Management on Birmingham's M42 (where the hard-shoulder becomes an ordinary running lane in busy periods); and 3. The `Queue Ahead'warning signs which are now almost ubiquitous on the English motorway network. The investment in this telematics infrastructure has been very significant --- about 100 million pounds for Active Traffic Management alone.Each of the ITS applications described above has at its heart detailed mathematical and computer models that forecast how traffic flows and how queues build up and dissipate. However, these models are far from perfect, and the purpose of this research is to improve the models by working on the fundamental science that underpins them. This a so-called multiscale challenge, since there is a whole hierarchy of models of different levels of detail, ranging from simulation models that model the behaviour of individual drivers, up to macroscopic models that draw an analogy between the flow of traffic and compressible gas. This research will establish methods for finding out which models are good and which ones are bad. Moreover, it will use modern `machine learning' techniques to combine good models so that computer-based traffic forecasting has human-like artificial intelligence.

Traffic jams are an annoying feature of everyday life. They also hamper our economy: the CBI has estimated that delays due to road traffic congestion cost UK businesses up to 20 billion annually. UK road traffic is forecast to grow by 30% in the period 2000-2015, so it seems that the congestion problem can only get worse. There is consequently an intense international effort in using Information and Communication Technologies to manage traffic in order to alleviate congestion --- this broad area is known as Intelligent Transport Systems (ITS). Regular motorway drivers will already be familiar with ITS. Examples include 1. the Controlled Motorways project on the M25 London Orbital (which sets temporary reduced speed limits when the traffic gets heavy); 2. Active Traffic Management on Birmingham's M42 (where the hard-shoulder becomes an ordinary running lane in busy periods); and 3. The `Queue Ahead'warning signs which are now almost ubiquitous on the English motorway network. The investment in this telematics infrastructure has been very significant --- about 100 million pounds for Active Traffic Management alone.Each of the ITS applications described above has at its heart detailed mathematical and computer models that forecast how traffic flows and how queues build up and dissipate. However, these models are far from perfect, and the purpose of this research is to improve the models by working on the fundamental science that underpins them. This a so-called multiscale challenge, since there is a whole hierarchy of models of different levels of detail, ranging from simulation models that model the behaviour of individual drivers, up to macroscopic models that draw an analogy between the flow of traffic and compressible gas. This research will establish methods for finding out which models are good and which ones are bad. Moreover, it will use modern `machine learning' techniques to combine good models so that computer-based traffic forecasting has human-like artificial intelligence.