In dynamic driving simulators, the experience of operating a vehicle is reproduced by combining visual stimuli generated by graphical rendering with inertial stimuli generated by platform motion. Due to inherent limitations of the platform workspace, inertial stimulation is subject to shortcomings in the form of missing cues, false cues, and/or scaling errors, which negatively affect simulation fidelity. In the present study, we aim at quantifying the relative contribution of an active somatosensory stimulation to the perceived intensity of self-motion, relative to other sensory systems. Participants judged the intensity of longitudinal and lateral driving maneuvers in a dynamic driving simulator in passive driving conditions, with and without additional active somatosensory stimulation, as provided by an Active Seat (AS) and Active Belts (AB) integrated system (ASB). The results show that ASB enhances the perceived intensity of sustained decelerations, and increases the precision of acceleration perception overall. Our findings are consistent with models of perception, and indicate that active somatosensory stimulation can indeed be used to improve simulation fidelity.

The use of dynamic driving simulators is nowadays common practice in the automotive industry. The effectiveness of such devices is strongly related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called Motion Cueing Algorithms (MCAs). Model Predictive Control (MPC) has been successfully applied to MCAs, being well suited to solve constrained optimal control problems. However, the predictive aspect of the algorithm has not been exploited effectively yet, mainly due to the hard real-time requirement when using a significantly long prediction window. In this paper, a real time implementation of the so called Look-Ahead (LA) strategy is presented, that is based on an effective manipulation of the reference along the prediction horizon, and on an on-line switching policy to a Non-Look-Ahead strategy when the expected driver behavior is not reliable. An optimal tuning of the MCA is computed by means of a multi-objective optimization, where both performance improvement due to the prediction exploitation, and robustness to varying driver behavior are taken into account. Finally, a characterization of the driver skill level is proposed and validated in experimental environment.

Driving simulators are nowadays a widely used tool in the automotive industry. In particular, the need for safe and repeatable conditions in automated driving testing is now defining a new challenge: to extend the use of the tool to nonprofessional drivers. Quality of the motion control strategies in generating both realistic and feasible inputs to the driver is therefore, more than ever, a crucial aspect. The motion strategies are implemented in the so-called motion cueing algorithms (MCAs). A recently proposed effective approach to MCA is based on model predictive control (MPC), as it is well suited to solve constrained optimal control problems and to take advantage of models of the human sensing system. However, the predictive aspect of the algorithm has not been exploited yet, due to the hard real-Time requirement when using long prediction windows. In this paper, a real-Time implementation of an MPC-based MCA with predictive feature is presented, endowed with an on-line switching policy to a nonpredictive algorithm when the expected driver behavior is considered unreliable. The motion action based on the actual driver behavior and the expected one are considered in the same procedure, thus fully exploiting the availability of a perceptive model. An optimal tuning procedure is also proposed, based on a multiobjective optimization, where both performance improvement due to the prediction exploitation, and robustness to varying driver behaviour are considered. Finally, a characterization of the driver skill level is proposed and validated in an experimental environment for the specific case of the vertical DOF.