In advanced driving maneuvers, such as a slalom maneuver, it is assumed that drivers use all the available cues to optimize their driving performance. For example, in curve driving, drivers use lateral acceleration to adjust car velocity. The same result can be found in driving simulation. However, for comparable curves, drivers drove faster in fixed-base simulators than when actually driving a car. This difference in driving behavior decreases with the use of inertial motion feedback in simulators. The literature suggests that the beneficial effect of inertial cues in driving behavior increases with the difficulty of the maneuver. Therefore, for an extreme maneuver such as a fast slalom, a change in driving behavior is expected when a fixed-base condition is compared to a condition with inertial motion. It is hypothesized that driving behavior in a simulator changes when motion cues are present in extreme maneuvers. To test the hypothesis, a comparison between No-Motion and Motion car driving simulation was done, by measuring driving behavior in a fast slalom. A within-subjects design was used, with 20 subjects driving the fast slalom in both conditions. The average speed during the Motion condition was significantly lower than the average speed during the No-Motion condition. The same was found for the peak lateral acceleration generated by the car model. A power spectral density analysis performed on the steering wheel angle signal showed different control input behavior between the two experimental conditions. In addition, the results from a paired comparison showed that subjects preferred driving with motion feedback. From the lower driving speed and different control input on the steering wheel, we concluded that motion feedback led to a significant change in driving behavior.

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.