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.

Road traffic crashes pose a major public health challenge. During 2013 crashes claimed 1713 UK lives and seriously injured 21657. This is an unacceptable human tragedy and also cost the economy £14.7 billion. Novice drivers have higher crash risk than any other group of drivers. Crash rates are highest immediately after the licence is obtained and decreases quickly over the first few months of driving. When people hear these statistics they usually attribute this effect to "experience". This is correct; we can be confident that this safety improvement does result from driving experience. However, we do not know how driving behaviour changes as result of experience; we do not know what it is that more experienced drivers do differently from very new drivers. If we can work out what experienced drivers are doing differently then we can support new drivers to adopt the same behaviours from the start of their driving careers. This support might take the form of training, testing, in-vehicle technology and legislation. In order to identify how behaviour changes over the early stages of driving, we will repeatedly interview a sample of new drivers over the first 3 months of their driving careers. We will focus on four situations in which new drivers are particularly vulnerable to crashes: (1) turning right across a traffic flow, (2) loss-of control on curves, (3) situations risking rear-end shunts and (4) driving at night. We will use these results to design a new questionnaire to measure the behaviours that change over the first few months of driving. We will then conduct a large scale study of new drivers to refine our questionnaire and test whether it measures the driving behaviours that underlie the road safety improvements during the early months of driving. Evidence that safer scores on our measure are more common in experienced drivers and predict lower crash involvement would be supportive of its effectiveness. The results of this study can inform policy and practice aimed at reducing risky behaviour associated with elevated crash risk in novice drivers. The behaviours identified as underlying the safety improvement in the first few months of driving can be targeted in driver training to try to improve novice drivers' road safety from day one. Our measure may be used in evaluations of whether training packages are effective in this regard. Our work could also inform revisions to the driving test to facilitate only giving licencing drivers who have reached a certain level in the behaviours identified. More broadly, the project will contribute to understanding the behaviours that put novice drivers at increased crash risk. Our results will inform policy decisions regarding legislation to protect young drivers from crash. For example there is mounting pressure to introduce a Graduated Licensing Scheme in the UK to prohibit novice drivers from the riskiest driving behaviours during their first year of driving. Our work can inform this debate by indicating which behaviours are most important to prohibit. Our results can also inform the design of in-car driver support technologies to aid novice drivers in the most safety-relevant aspects of driving. In these ways we believe our project can support more effective public health interventions to reduce crash involvement among novice drivers in the future.

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.