The Impact of Physical Motion Cues on Driver Braking Performance: A Clinical Study Using Driving Simulator and Eye Tracker

Abstract

Driving simulators are increasingly being incorporated by driving schools into a training process for a variety of vehicles. The motion platform is a major component integrated into simulators to enhance the sense of presence and fidelity of the driving simulator. However, less effort has been devoted to assessing the motion cues feedback on trainee performance in simulators. To address this gap, we thoroughly study the impact of motion cues on braking at a target point as an elementary behavior that reflects the overall driver’s performance. In this paper, we use an eye-tracking device to evaluate driver behavior in addition to evaluating data from a driving simulator and considering participants’ feedback. Furthermore, we compare the effect of different motion levels (“No motion”, “Mild motion”, and “Full motion”) in two road scenarios: with and without the pre-braking warning signs with the speed feedback given by the speedometer. The results showed that a full level of motion cues had a positive effect on braking smoothness and gaze fixation on the track. In particular, the presence of full motion cues helped the participants to gradually decelerate from 5 to 0 ms−1 in the last 240 m before the stop line in both scenarios, without and with warning signs, compared to the hardest braking from 25 to 0 ms−1 produced under the no motion cues conditions. Moreover, the results showed that a combination of the mild motion conditions and warning signs led to an underestimation of the actual speed and a greater fixation of the gaze on the speedometer. Questionnaire data revealed that 95% of the participants did not suffer from motion sickness symptoms, yet participants’ preferences did not indicate that they were aware of the impact of simulator conditions on their driving behavior.

Fidelity Assessment of Motion Platform Cueing: Comparison of Driving Behavior under Various Motion Levels

“…To avoid the time-invariant ratings of total mismatch yield by traditional assessment methods, the researchers proposed a new evaluation methodology describing physical short-duration cueing errors and consisting of continuous drivers’ rating of motion incongruence during passive driving simulations with consistency, still their method relied entirely on subjective evaluation which is a major limitation of the study. To overcome this weakness, the research conducted previously by El Hamdani et al [ 22 ] applied both objective (i.e., performance in the simulator, eye tracking) and subjective (questionnaire) evaluation methods that showed a positive impact of the feedback given by motion cues on drivers’ braking performance and visual focus on the track. However, the latter paper and the previous surveyed works did not compare the level of the motion platform’s cues with those generated by real cars on the roads.…”

A Systematic Literature Review for Measure, Estimation, and Mitigation of Motion Sickness in Vehicle Environment

“…The selected studies are based on three main theories that explain motion sickness: sensory conflict theory, evolutionary theory, and posture instability theory. Each study had a specific set of participants, and detailed data such as driving experience, MS sensitivity, and group designations were collected (Bae, 2019;Smyth, 2021;El Hamdani, 2022). The severity of MS was measured using real vehicles, simulators, and various data measurement devices and techniques (Henry, 2022;Jones, 2019;Li, 2022;Le, 2020).…”

“…The experiments designed in the reviewed studies featured independent variables, such as participants' gender, tasks, and seating positions, as well as dependent variables, including rating scales or surveys, physiological data, and motion dynamic data. Some studies used single-factor scales like MIsery Scale (MISC) (El Hamdani et al, 2022), while others utilized multi-factor scales, such as the Motion Sickness Assessment Questionnaire (MSAQ), and the Fast Motion Sickness (FMS) Scale (de Winkel, 2021; Chang, 2021; Salter, 2019).…”