Fundamentals of Robotic Mechanical Systems
Fundamentals of Robotic Mechanical Systems
Open Scene Graph A: Introduction, B: Examples and Applications
VR '04 Proceedings of the IEEE Virtual Reality 2004
Discovering Statistics Using SPSS
Discovering Statistics Using SPSS
Driver behavior comparison between static and dynamic simulation for advanced driving maneuvers
Presence: Teleoperators and Virtual Environments
CyberWalk: Enabling unconstrained omnidirectional walking through virtual environments
ACM Transactions on Applied Perception (TAP)
| Hi-index | 0.00 |
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.