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.
Following a previous study in the Simona simulator on perception of coherent visual and inertial cues in a flight simulator, an experiment is performed in the Desdemona simulator to investigate the influence of the frequency of visual and inertial stimuli on the limits of the perceived coherence zone. The coherence zone is defined as the range of inertial motion amplitudes that, though not being a physical match to the visual cues, are still perceived by subjects as coherent. The main hypothesis tested is that the semi-circular canals dynamics influence the internal comparison between the visually and inertially perceived self-velocity. Furthermore, the results between the Simona and Desdemona studies are compared. In general, the effect of amplitude and frequency on the measured coherence zones follow the same trends as in the previous study: the coherence zone width increases with increasing visual cue amplitude and the point of mean coherence decreases with respect to the oneto-one line for the higher amplitudes. The results for the low frequency and low amplitude stimulus might be affected by the inertial sensory threshold, making it difficult to draw definite conclusions about the posed hypothesis. The results between the two simulator studies are different in terms of absolute values, but the trends are the same.
In the design of a high fidelity simulator environment, knowledge about motion perception thresholds is essential. Thresholds are generally measured in a passive experimental setup, in which subjects do not actively influence their motion profile. In this paper, a method for analytical identification of motion perception thresholds in active control tasks is proposed. The effect of vestibular motion on thresholds was analyzed and a comparison to conventional passive threshold measurements was made. The threshold identification method was based on a multi-channel pilot model extended with a nonlinear absolute threshold element. Maximum likelihood parameter estimation, combining a Genetic Algorithm and an unconstrained Gauss-Newton algorithm optimization, was applied. A theoretical study indicated that there is an upper limit to the vestibular motion amplitude to allow accurate threshold identification. Two experiments were performed in the SIMONA Research Simulator to test the application of the method. A passive experiment to measure the sensory pitch threshold and an active control task to identify the active pitch threshold. In the active experiment, the vestibular motion amplitude was varied and two types of control tasks were used. For the disturbance-rejection task, the pitch threshold was only identifiable for high motion amplitude levels. The target-tracking task allowed identification of the threshold for medium and high amplitude levels. Neither of the tasks allowed threshold identification with low levels of motion amplitude. The pitch thresholds obtained from the active and passive experiment were in the same order of magnitude
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