Producing sensations of motion in driving simulators often requires using cumbersome and expensive motion platforms. In this article we present a novel and alternative approach for producing self-motion sensations in driving simulations by relying on haptic-feedback. The method consists in applying a force-feedback proportional to the acceleration of the virtual vehicle directly to the hands of the driver, by means of a haptic device attached to the manipulated controller (or a steering wheel). We designed a proof-of-concept based on a standard gamepad physically attached at the extremity of a standard 3DOF haptic display. Haptic effects were designed to match notably the acceleration/braking (longitudinal forces) and left/right turns (lateral forces) of the virtual vehicle. A preliminary study conducted with 23 participants, engaged in gamepad-based active VR navigations in a straight line, showed that haptic motion effects globally improved the involvement and realism of motion sensation for participants with prior experience with haptic devices. Taken together, our results suggest that our approach could be further tested and used in driving simulators in entertainment and/or professional contexts.

This work focuses on enabling 3D interaction assistance by adding adaptivity depending on the tasks, objectives, and the general interaction context. We model the context using Conceptual Graphs (CG) based on an ontology. Including CG in our scene manager (Virtools) allows us to add semantic information and to describe the available tools. We handle rules leading to adaptation with a logic programming layer (Prolog+CG) included in the Amine platform. This project is a step towards Intelligent Virtual Environments, which proposes a hybrid solution by adding a separate semantic reasoning to classic environments. The first case study automatically manages few modalities depending on the distance to objects, user movement, available tools and modality risks.

We propose a semantic reasoning engine for context-awareness in classic VR environments. It is currently used to automatically detect user's interests and manage visual enhancements depending on the user's movement.