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Illusory self-motion (vection) can be generated by visual stimulation. The purpose of the present study was to compare behavioral vection measures including intensity ratings, duration, and onset time across different visual display types. Participants were exposed to a pattern of alternating black-and-white horizontal or vertical bars that moved either in vertical or horizontal direction, respectively. Stimuli were presented on four types of displays in randomized order: (a) large field of view dome projection, (b) combination of three computer screens, (c) single computer screen, (d) large field of view flat projection screen. A Computer Rod and Frame Test was used to measure field dependence, a cognitive style indicating the person’s tendency to rely on external cues (i.e., field dependent) or internal cues (i.e., field independent) with respect to the perception of one’s body position in space. Results revealed that all four displays successfully generated at least moderately strong vection. However, shortest vection onset, longest vection duration, and strongest vection intensity showed for the dome projection and the combination of three screens. This effect was further pronounced in field independent participants, indicating that field dependence can alter vection.

In a flight simulator, the calculated aircraft motions are scaled down and filtered to fit within the envelope of the simulator motion system. A number of recent flight and ground simulation studies have reported that the simulator motion was too strong, when in fact, the motion was scaled down and filtered. This paper puts forth the hypothesis that this could be due in part to the motion drive algorithm and vehicle model exaggerating the jerk. To test the plausibility of this hypothesis a paired-comparison experiment was run to determine if the subjective impression of motion strength is a function of both the acceleration and jerk of the motion. The experiment found that the level of jerk and acceleration contributed to the perceived strength of motion, with larger jerks and accelerations leading to increased motion strength. In addition, the duration of the acceleration had a significant effect on the perceived motion strength, with longer durations leading to increased motion strength. Although the relationship between jerk and motion strength suggests that exaggerated jerk in the simulator could lead to the preference for scale factors less than one, the strength of the relationship strongly suggests that it does not entirely account for the preference. Nomenclature A i = nominal acceleration of motion condition i, m=s 2 A nom = acceleration used to normalize the linear model, m=s 2 a i = total score of motion condition i (number of times selected) D i = length of constant-acceleration phase of motion, s D n = score variable for model testing J i = nominal jerk level of motion condition i, m=s 3 J nom = jerk level used to normalize the linear model, m=s 3 l = actuator length command, m n = total number of comparisons between two conditions p = probability of null hypothesis S x = simulator fore-aft displacement command, m s = Laplace variable T J = duration of the constant jerk section of the motion condition, s T s = duration of half of the motion condition, s t = number of motion conditions V x = motion-based fore-aft velocity, m=s x c = commanded X acceleration x i = intermediate X ij = total number of times that condition i was preferred over condition j A = perceived motion strength change due to a change in acceleration A = normalized perceived motion strength change due to a change in acceleration D = perceived motion strength change due to a change in the duration of constant acceleration i = perceived motion strength of motion condition i, no order effect i = perceived motion strength of motion condition i, order effect J = perceived motion strength change due to a change in jerk J= normalized perceived motion strength change due to a change in jerk = model order effect = simulator pitch command, rad i = preference probability for condition i ij = probability that condition i is selected over condition j, no order effect ij = probability that condition i is selected over condition j, when i is the first condition = standard deviation 2 = 2 distribution ! = angular jerk, time derivative of angular ac...

Recent evidence suggests that visual-auditory cue integration may change as a function of age such that integration is heightened among older adults. Our goal was to determine whether these changes in multisensory integration are also observed in the context of self-motion perception under realistic task constraints. Thus, we developed a simulated driving paradigm in which we provided older and younger adults with visual motion cues (i.e., optic flow) and systematically manipulated the presence or absence of congruent auditory cues to self-motion (i.e., engine, tire, and wind sounds). Results demonstrated that the presence or absence of congruent auditory input had different effects on older and younger adults. Both age groups demonstrated a reduction in speed variability when auditory cues were present compared to when they were absent, but older adults demonstrated a proportionally greater reduction in speed variability under combined sensory conditions. These results are consistent with evidence indicating that multisensory integration is heightened in older adults. Importantly, this study is the first to provide evidence to suggest that age differences in multisensory integration may generalize from simple stimulus detection tasks to the integration of the more complex and dynamic visual and auditory cues that are experienced during self-motion.

Previous psychophysical research has examined how younger adults and non-human primates integrate visual and vestibular cues to perceive self-motion. However, there is much to be learned about how multisensory self-motion perception changes with age, and how these changes affect performance on everyday tasks involving self-motion. Evidence suggests that older adults display heightened multisensory integration compared with younger adults; however, few previous studies have examined this for visual-vestibular integration. To explore age differences in the way that visual and vestibular cues contribute to self-motion perception, we had younger and older participants complete a basic driving task containing visual and vestibular cues. We compared their performance against a previously established control group that experienced visual cues alone. Performance measures included speed, speed variability, and lateral position. Vestibular inputs resulted in more precise speed control among older adults, but not younger adults, when traversing curves. Older adults demonstrated more variability in lateral position when vestibular inputs were available versus when they were absent. These observations align with previous evidence of age-related differences in multisensory integration and demonstrate that they may extend to visual-vestibular integration. These findings may have implications for vehicle and simulator design when considering older users.

Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

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