An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

The Role of Human-Automation Consensus in Multiple Unmanned Vehicle Scheduling

Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation’s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.Aurora Flight Sciences Corp.United States. Office of Naval Researc

Opportunities for olfactory interaction in an automotive context

Driving is a highly visual task. Nevertheless, it is a process that involves other senses as well. When we drive, we touch the steering wheel; we listen to what is happening around us, and, even if we are not paying attention to that, we smell what is happening with the car or around it. A scent of gasoline, the burning rubber, the plastic heated up by the sunlight - these are just a few examples. Smell is a very important sense for driving, though it has not been studied much in this context [85], despite being able to provide a much more vivid experience than any other human sense [80]. This thesis aims to fill this gap by investigating opportunities for olfactory interaction in an automotive context. The thesis is mainly focused on designing a scent-delivery device suitable for in-car interaction, on the topic of delivering driving-relevant notifications using scents, and on studying the effects scents have on the driving performance and behaviour, as well as the driver’s mood and well-being. This paper-style PhD thesis consists of two parts. Part II is a collection of seven published papers written in the scope of this thesis, and Part I describes how these papers build a coherent story. Part I starts with an introduction (see Chapter 1) that covers the research questions and contributions of the thesis. It continues with a summary of the background research (see Chapter 2). This overview part then moves on to the description of the approach (see Chapter 3) that covers the process of designing the scent delivery device, the olfactory interaction space, and the studies conducted throughout this PhD. Chapter 4 then summarises the core findings of each study, which are finally discussed in Chapter 5. Part I finishes with a conclusion (see Chapter 6)