Advanced driving simulators aim at rendering the motion of a vehicle with maximum fidelity, which requires increased mechanical travel, size, and cost of the system. Motion cueing algorithms reduce the motion envelope by taking advantage of limitations in human motion perception, and the most commonly employed method is just to scale down the physical motion. However, little is known on the effects of motion scaling on motion perception and on actual driving performance. This paper presents the results of a European collaborative project, which explored different motion scale factors in a slalom driving task. Three state-of-the-art simulator systems were used, which were capable of generating displacements of several meters. The results of four comparable driving experiments, which were obtained with a total of 65 participants, indicate a preference for motion scale factors below 1, within a wide range of acceptable values (0.4-0.75). Very reduced or absent motion cues significantly degrade driving performance. Applications of this research are discussed for the design of motion systems and cueing algorithms for driving simulation.

This study assesses the issue of voluntary training of a standardized online competition (serious gaming) between surgical residents. Surgical residents were invited to join a competition on a virtual reality (VR) simulator for laparoscopic motor skills. A final score was calculated based on the task performance of three exercises and was presented to all the participants through an online database on the Internet. The resident with the best score would win a lap-top computer. During three months, 31 individuals from seven hospitals participated (22 surgical residents, 3 surgeons and six interns). A total of 777 scores were logged in the database. In order to out-perform others some participants scheduled themselves voluntarily for additional training. More attempts correlated with higher scores. The serious gaming concept may enhance voluntary skills training. Online data capturing could facilitate monitoring of skills progression in surgical trainees and enhance (VR) simulator validation.

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Although the stereo viewing system promises improved depth perception and the TFT and image projection displays are supposed to improve hand-eye coordination, none of these systems provided better task performance than the standard viewing system in this pelvi-trainer experiment.

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Compensating for the kinematic effects introduced by the incision improves hand-eye coordination. The results of this study indicate that the incision provides a point of reference for hand-eye coordination during endoscopic manipulation.

Research on new automotive systems currently relies on car driving simulators, as they are a cheaper, faster, and safer alternative to tests on real tracks. However, there is increasing concern about the motion cues provided in the simulator and their influence on the validity of these studies. Especially for curve driving, providing large sustained acceleration is difficult in the limited motion space of simulators. Recently built simulators, such as Desdemona, offer a large motion space showing great potential as automotive simulators. The goal of this research is: first, to develop a motion drive algorithm for urban curve driving in the Desdemona simulator; and second, to evaluate the solution through a simulator driving experiment. The developed algorithm, the one-to-one yaw algorithm, is compared to a classical washout algorithm (adapted to the Desdemona motion space) and a control condition where only road rumble is provided. Results show that regarding lateral motion, the absence of cues in the rumble condition is preferred over the presence of false cues in the classical algorithm. "No motion" seems to be favored over "bad motion." In terms of longitudinal motion, the one-to-one yaw and the classical algorithm are voted better than the rumble condition, showing that the addition of motion cues is beneficial to the simulation of braking. In a general way, the one-to-one yaw algorithm is classified better than the other two algorithms.