Analysing Ultra-Wide Band Positioning for Geofencing in a Safety Assurance Context

There is a desire to move towards more flexible and automated factories. To enable this, we need to assure the safety of these dynamic factories. This safety assurance must be achieved in a manner that does not unnecessarily constrain the systems and thus negate the benefits of flexibility and automation. We previously developed a modular safety assurance approach, using safety contracts, as a way to achieve this. In this case study we show how this approach can be applied to Autonomous Guided Vehicles (AGV) operating as part of a dynamic factory and why it is necessary. We empirically evaluate commercial, indoor fog/edge localisation technology to provide geofencing for hazardous areas in a laboratory. The experiments determine how factors such as AGV speeds, tag transmission timings, control software and AGV capabilities affect the ability of the AGV to stop outside the hazardous areas. We describe how this approach could be used to create a safety case for the AGV operation.

Time to Die: Death Prediction in Dota 2 using Deep Learning

Esports have become major international sports with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unknown territory. Micro-predictions are however of perennial interest across esports commentators and audience, because they provide the ability to observe events that might otherwise be missed: esports games are highly complex with fast-moving action where the balance of a game can change in the span of seconds, and where events can happen in multiple areas of the playing field at the same time. Such events can happen rapidly, and it is easy for commentators and viewers alike to miss an event and only observe the following impact of events. In Dota 2, a player hero being killed by the opposing team is a key event of interest to commentators and audience. We present a deep learning network with shared weights which provides accurate death predictions within a five-second window. The network is trained on a vast selection of Dota 2 gameplay features and professional/semi-professional level match dataset. Even though death events are rare within a game (1\% of the data), the model achieves 0.377 precision with 0.725 recall on test data when prompted to predict which of any of the 10 players of either team will die within 5 seconds. An example of the system applied to a Dota 2 match is presented. This model enables real-time micro-predictions of kills in Dota 2, one of the most played esports titles in the world, giving commentators and viewers time to move their attention to these key events.

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations.