In multi-agent applications, normative systems are usually used to regulate the behavior of the agents. They provide an efficient means to ensure limited deviations from an expected ideal behavior. Many works have been done in this classical research direction, less frequent are the works on norms in simulation. In this paper we focus on simulations of spatially situated agents, typically movin...

Creating active, student-centered learning situations in postsecondary education is an ongoing challenge for engineering educators. Contemporary students familiar with visually engaging and fast-paced games can find traditional classroom methods of lecture and guided laboratory experiments limiting. This paper presents a methodology that incorporates driving simulation, motion simulation, and e...

Desdemona is a moving-base research-simulator that was designed with a special focus on spatial disorientation demonstrations, flight simulation, and driving simulation. The cabin is equipped with a car mock-up that contains force-feedback on pedals and steering wheel, and out-the-window visuals. The cabin is mounted in a fully gimbaled system that, as a whole, can move vertically and horizonta...

Linguistic data summarization targets the description of patterns emerging in data by means of linguistic expressions. Just as human beings do, computers can use natural language to represent and fuse heterogeneous data in a multi criteria decision making environment. Linguistic data description is particularly well suited for applications in which there is a necessity of understanding data at ...

Driving simulators have to be student-oriented. We created the Virtual Driving Instructor (VDI), an intelligent tutoring multiagent system, which provides studentadaptivity. The VDI enhances the interaction between the driving simulator and the student. It uses regressive instruction and feedback, which are adapted to the student, presentation context and task, to help the student improving his...

Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through training in single-agent environments, but they have difficulty in generalizing to multi-agent driving scenarios. We argue these difficulties arise ...

PURPOSE To examine the relationship between Motor Vehicle Collisions (MVCs) in drivers with glaucoma and standard automated perimetry (SAP), Useful Field of View (UFOV), and driving simulator assessment of divided attention. METHODS A cross-sectional study of 153 drivers from the Diagnostic Innovations in Glaucoma Study. All subjects had SAP and divided attention was assessed using UFOV and d...

The virtual driving simulator environment consists of static universe, dynamic objects and interior of driver’s vehicle. The static universe can be building, trees, road and others. The dynamics objects can include any moving objects in virtual scene like cars, people, and crowd. With more complex virtual scene will contain many thousands of polygons which need more graphic processing power and...

While driving simulators are a valuable tool for assessing multiple dimensions of driving performance under relatively safe conditions, researchers and practitioners must be prepared for participants that suffer from simulator sickness. This paper describes multiple theories of motion sickness and presents a method for assessing and reacting to simulator sickness symptoms. Results showed that t...