Effect of Simulator Motion Cuing on Steering Control Performance

Abstract

The goal of the present study was to explore how simulator motion cuing affects the driver's control performance of a car. Steering behavior was used as a measure of control performance. The experimental task was a slalom maneuver in which the velocity of the car was limited to 70 km/h. Subjective and objective variables were measured. The paper describes the objective steering behavior. The slalom task was driven under four conditions in which the lateral motion scale factors were 1 (one-to-one lateral motion), 0.7, 0.4, and 0 (no-motion), respectively. In total, 16 participants completed the experiment. The study showed that the motion condition affects the steering wheel behavior. The general tendency is that less steering correction took place when the magnitude of the motion cues was increased, which was quantified by two performance indicators. First, the number of steering wheel reversals was reduced when the motion cue magnitude was increased. Second, the amount of relatively high-frequency correction was reduced with increasing motion cue magnitude. It is concluded that motion feedback can improve the driver's control performance in an extreme scenario such as a slalom maneuver. Therefore, the effect of motion on control performance must be considered when a driving simulator study addressing control performance is designed.

Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

“…Taken together, these findings suggest that in normal driving, drivers integrate visual and vestibular cues, and when vestibular cues are removed, this makes task performance more challenging. However, after repeated exposure to the slalom task, control efforts decrease (Feenstra et al, 2010) and task performance improves (Correia Grácio et al, 2011), suggesting some form of behavioural adaptation over time to the lack of vestibular cues. For motion scaling between zero and one, there are reports from several studies of a local optimum, in the 0.4-0.8 range, where task performance and subjective preferences peak (Berthoz et al, 2013;Savona et al, 2014), and in one case this local optimum was also observed for steering effort (Savona et al, 2014).…”

“…The model adopts the commonly used concept of a desired path (Plochl and Edelmann, 2007) to define the sinusoidal slalom task. The exact setup of this task was here based on (Feenstra et al, 2010): 62.5 m spacing between cones, 3 m lateral amplitude, and the task was carried out at a constant longitudinal speed of 70 km/h.…”

An objective assessment of the utility of a driving simulator for low mu testing

“…This type of interpretation, which suggests that drivers might seek to avoid large accelerations, is in line with findings of drivers adapting to speeds and driving trajectories that limit the experienced acceleration, when motion cues are added or increased (Correia Grácio et al, 2009;Siegler et al, 2001). For objective task performance, just as with subjective realism, it generally improves when motion cues are first added and this performance increase is typically associated with decreases in measures of control effort, such as steering wheel reversal rate or high-frequency steering power (Damveld et al, 2012;Feenstra et al, 2010;Repa et al, 1982). One point to note regarding these findings on objective performance, is that the performance has often been defined in terms of deviation from a researcher-defined reference path, for which there may be no strong motivation.…”

Driving Simulator Research on Safe Highway Design and Operation

“…The TNO driving simulators cover a wide range of configurations including an elementary PC-based simulator for simple comprehension studies (13, 14) (Figure 2a); a fixed-base driving simulator originally developed for driver training purposes (15), but now also used for providing a dynamic task environment in the design and development phase of innovative systems (16-18) (Figure 2b); the primary high-fidelity moving-base TNO driving simulator that has been evolving for several decades for passenger cars (Figure 2c) or trucks (Figure 2d); and the advanced motion platform DESDEMONA for realistic driving in more extreme safety-critical scenarios (19,20) (Figure 2e).…”