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.