Autonomous vehicles (AVs) must be controlled by software, and such software thus has responsibility for safe vehicle behaviour. It is therefore essential that we rigorously test such software. This is difficult to do for AVs, as they have to respond appropriately to a great diversity of external situations as they go about their missions. It is possible to find faults in an AV software specification by testing its behaviour in a variety of external situations, either in reality or in computer simulation. Such testing may reveal that the specification ignores certain situations (e.g. negotiating a motorway contraflow lane) or defines behaviour that is unsafe in a subset of situations (e.g. its policy for adapting to icy surfaces leads to unsafe speed control in crowded urban environments). This project will test the hypothesis that testing based on coverage of possible external situations ("situation coverage") is an effective means of finding AV specification faults. We will test the hypothesis by creating a tool that generates situations for simulated AVs, both randomly and using heuristic search, and assessing whether higher situation coverage correlates with greater success at revealing seeded specification faults. (For the search, the fitness function will be based on the situation coverage achieved) The project will draw on previous work on test coverage measures, on search-based testing, and on automated scenario generation in training simulations. To assess the effectiveness of the approach, we will use a small but practically-motivated case study of an autonomous ground vehicle, informed by the advice of an advisory panel set up for this project.

The Innovative Manufacturing and Construction Research Centre (IMCRC) will undertake a wide variety of work in the Manufacturing, Construction and product design areas. The work will be contained within 5 programmes:1. Transforming Organisations / Providing individuals, organisations, sectors and regions with the dynamic and innovative capability to thrive in a complex and uncertain future2. High Value Assets / Delivering tools, techniques and designs to maximise the through-life value of high capital cost, long life physical assets3. Healthy & Secure Future / Meeting the growing need for products & environments that promote health, safety and security4. Next Generation Technologies / The future materials, processes, production and information systems to deliver products to the customer5. Customised Products / The design and optimisation techniques to deliver customer specific products.Academics within the Loughborough IMCRC have an internationally leading track record in these areas and a history of strong collaborations to gear IMCRC capabilities with the complementary strengths of external groups.Innovative activities are increasingly distributed across the value chain. The impressive scope of the IMCRC helps us mirror this industrial reality, and enhances knowledge transfer. This advantage of the size and diversity of activities within the IMCRC compared with other smaller UK centres gives the Loughborough IMCRC a leading role in this technology and value chain integration area. Loughborough IMCRC as by far the biggest IMRC (in terms of number of academics, researchers and in funding) can take a more holistic approach and has the skills to generate, identify and integrate expertise from elsewhere as required. Therefore, a large proportion of the Centre funding (approximately 50%) will be allocated to Integration projects or Grand Challenges that cover a spectrum of expertise.The Centre covers a wide range of activities from Concept to Creation.The activities of the Centre will take place in collaboration with the world's best researchers in the UK and abroad. The academics within the Centre will be organised into 3 Research Units so that they can be co-ordinated effectively and can cooperate on Programmes.

Autonomy is surely a core theme of technology in the 21st century. Within 20 years, we expect to see fully autonomous vehicles, aircraft, robots, devices, swarms, and software, all of which will (and must) be able to make their own decisions without direct human intervention. The economic implications are enormous: for example, the global civil unmanned air- vehicle (UAV) market has been estimated to be £6B over the next 10 years, while the world-wide market for robotic systems is expected to exceed $50B by 2025. This potential is both exciting and frightening. Exciting, in that this technology can allow us to develop systems and tackle tasks well beyond current possibilities. Frightening in that the control of these systems is now taken away from us. How do we know that they will work? How do we know that they are safe? And how can we trust them? All of these are impossible questions for current technology. We cannot say that such systems are safe, will not deliberately try to injure humans, and will always try their best to keep humans safe. Without such guarantees, these new technologies will neither be allowed by regulators nor accepted by the public. Imagine that we have a generic architecture for autonomous systems such that the choices the system makes can be guaranteed? And these guarantees are backed by strong mathematical proof? If we have such an architecture, upon which our autonomous systems (be they robots, vehicles, or software) can be based, then we can indeed guarantee that our systems never intentionally act dangerously, will endeavour to be safe, and will - as far as possible - act in an ethical and trustworthy way. It is important to note that this is separate from the problem of how accurately the system understands its environment. Due to inaccuracy in modelling the real world, we cannot say that a system will be absolutely safe or will definitely achieve something; instead we can say that it tries to be safe and decides to carry out a task to its best ability. This distinction is crucial: we can only prove that the system never decides to do the wrong thing, we cannot guarantee that accidents will never happen. Consequently, we also need to make an autonomous system judge the quality of its understanding and require it to act taking this into account. We should also verify, by our methods, that the system's choices do not exacerbate any potential safety problems. Our hypothesis is that by identifying and separating out the high-level decision-making component within autonomous systems, and providing comprehensive formal verification techniques for this, we can indeed directly tackle questions of safety, ethics, legality and reliability. In this project, we build on internationally leading work on agent verification (Fisher), control and learning (Veres), safety and ethics (Winfield), and practical autonomous systems (Veres, Winfield) to advance the underlying verification techniques and so develop a framework allowing us to tackle questions such as the above. In developing autonomous systems for complex and unknown environments, being able to answer such questions is crucial.

Road traffic injuries have become a leading cause of death globally accounting for 1.2 million deaths annually, and will rise in worldwide rank to sixth place as a major cause of death (including decease), by 2020. It is encouraging that, despite the constant increase of the number of vehicles in Europe during the last decade, the number of fatalities demonstrates a slow decay. This can be partly attributed to the enormous improvements in vehicle safety, through the introduction of both passive and active safety systems. By no means, however, have the current state-of-the-art of vehicle safety systems proven adequate to radically reverse the sober traffic accident statistics. Current active safety systems, such as the Electronic Stability Control (ESC), aim at restricting the operation of the vehicle within a region characterised by an on-demand linear increase of tyre forces, away from the tyre's maximum force capacity, allowing the average driver to maintain control of the vehicle. With this project we wish to explore the benefits of using the whole of the available performance of the vehicle, rather than restricting its response, in accident avoidance situations. We propose the development of novel control algorithms, which will use the control authority introduced by current active safety systems and modern power/drive-train configurations, and employ expert driving skills to actively assist the driver exploit the limits of handling of the vehicle during emergency manoeuvring. MIRA, one of the world's leading independent providers of vehicle product engineering, testing, certification and research, has expressed their great interest in exploring the limits of the handling capacity of vehicles with modern power/drive-train configurations and the potential benefits in active safety. The company has agreed to offer their support to this project by means of technical consultation and active participation in the management and execution of the proposed research tasks. Current drive-by-wire (DBW) actuators have allowed for a considerably enhanced control authority over the vehicle, as compared to traditional steering, brake and power/drive-train systems. The human operator provides commands through the conventional controls, that is, the steering wheel, and the throttle and brake pedals, whereas, for instance, the ESC allows for individual wheel braking, and electric motors in hybrid vehicles allow for individual wheel torque control. Race drivers have developed expert techniques to exploit most of the available force capacity of the tyres using the traditional controls. The enhanced control authority provided by modern vehicle controls potentially allows for even more efficient use of the available tyre performance. In this work we wish to explore the performance limits of modern vehicles equipped with DBW actuators, and identify optimum operating conditions related to accident avoidance. The first research task of the proposed work is to obtain steady-state cornering conditions at the limit of handling of the vehicle, that is, in a region of vehicle operation where the tyres produce forces close to their maximum capacity. As a case study we will consider the power/drive-train configuration of MIRA's prototype hybrid vehicle (H4V) which uses two high-torque independently controlled electric motors to drive the rear wheels. Consequently, we will design controllers using linear and nonlinear control design tools, which will stabilise the vehicle in potentially unstable driving conditions, instead of restricting the vehicle in a stable operating region away from its performance limits, using DBW control inputs. The control design will be implemented in a high fidelity simulation environment using experimentally validated vehicle models provided by MIRA. The implementation strategy entails the detection of emergency situations and accounts for the driver's intention.