Abstract:In the context of increased maintenance operations and generational renewal work, a nuclear owner and operator, like Electricité de France (EDF), is invested in the scaling-up of tools and methods of "as-built virtual reality" for whole buildings and large audiences. In this paper, we first present the state of the art of scanning tools and methods used to represent a very complex architecture. Then, we propose a methodology and assess it in a large experiment carried out on the most complex building of a 1300-megawatt power plant, an 11-floor reactor building. We also present several developments that made possible the acquisition, processing and georeferencing of multiple data sources (1000+ 3D laser scans and RGB panoramic, total-station surveying, 2D floor plans and the 3D reconstruction of CAD as-built models). In addition, we introduce new concepts for user interaction with complex architecture, elaborated during the development of an application that allows a painless exploration of the whole dataset by professionals, unfamiliar with such data types. Finally, we discuss the main feedback items from this large experiment, the remaining issues for the generalization of such large-scale surveys and the future technical and scientific challenges in the field of industrial "virtual reality".

Objective: The study addressed the role of familiarization on a driving simulator with a forward collision warning (FCW) and investigated its impact on driver behavior. Background: Drivers need a good understanding of how an FCW system functions to trust it and use it properly. Theoretical and empirical data suggest that exploring the capacities and limitations of the FCW during the learning period improves operating knowledge and leads to increased driver trust in the system and better driver-system interactions. The authors tested this hypothesis by comparing groups of drivers differing in FCW familiarity. Method: During the familiarization phase, familiarized drivers were trained on the simulator using the FCW, unfamiliarized drivers simply read an FCW manual, and control drivers had no contact with the FCW. During the test, drivers drove the simulator and had to interact with traffic; both familiarized and unfamiliarized drivers used the FCW, whereas controls did not. Results: Simulator familiarization improved driver understanding of FCW operation. Driver-system interactions were more effective: Familiarized drivers had no collisions, longer time headways, and better reactions in most situations. Familiarization increased trust in the FCW but did not raise system acceptance. Conclusion: Familiarization on the simulator had a positive effect on driver-system interactions and on trust in the system. The limitations of the familiarization method are discussed in relation to the driving simulator methodology. Application: Practicing on a driving simulator with driving-assistance systems could facilitate their use during real driving.

Working in complex industrial facilities requires spatial navigation skills that people build up with time and field experience. Training sessions consisting in guided tours help discover places but they are insufficient to become intimately familiar with their layout. They imply passive learning postures, are time-limited and can be experienced only once because of organization constraints and potential interferences with ongoing activities in the buildings. To overcome these limitations and improve the acquisition of navigation skills, we developed Indy, a virtual reality system consisting in a collaborative game of treasure hunting. It has several key advantages: it focuses learners' attention on navigation tasks, implies their active engagement and provides them with feedbacks on their achievements. Virtual reality makes it possible to multiply the number and duration of situations that learners can experience to better consolidate their skills. This paper discusses the main design principles and a typical usage scenario of Indy.

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