While traditional real-time vehicle simulation is becoming a commodity in the automotive industry, the increasing usage of driving simulators with hybrid (i.e. including SIL, MIL, HIL) embedded technology is making the engineering teams focus more and more on other critical aspects of the process, such as whole subsystem compatibility, integrity, interoperability rather than only on single subcomponent features. Among the important subsystems, the human driver deserves a special level of attention. Advanced Driver Assistance Systems (ADAS) are seen as an essential competitive technical and commercial advantage for automotive companies. However, those same systems are becoming increasingly more powerful and more invasive. On the one side, the driver would become less and less involved in direct vehicle control, but on the other side is asked to naturally accept vehicle “robotization”. That “robotization” involves a meaningful number of realistic test cases for assessing the ADAS algorithms would require such a long time to make it incompatible with any meaningful time-to-market or return-on-investment of any new vehicle development plans. Full system simulation including Driver-in-the-Loop as early as possible is basically mandatory as a result. VI-grade engineers have been pioneering the development of advanced off-line driver models for automatic vehicle simulation since the beginning of the current century. A decade later, VI-grade’s innovative spirit was confirmed by the launch of off-the-shelf driving simulator turnkey solutions. And most recently, VI-grade introduced techniques for real-time monitoring of the psycho-physiological condition of the driver in a driving simulator under various level of cognitive loads.

Scientifically proven techniques for objectively measuring human acceptance of the virtual reality in a vehicle application will be presented. In support of this study, Driver-in-Motion (DiM) simulator motion architecture is ideal on which to implement this study. Further, MultiSensorial Cueing Algorithms found on the DiM simulator are designed to minimize the difference between the perception of real vehicle dynamics versus the virtual equivalent. So, when the driver is driving the same car in the same scenario in a high quality real-time vehicle model on the simulator, actively or semi-actively driven, development engineers will have the tools to enable to evaluate driver acceptance with more than just subjective feedback, but also objective psycho-physiological data to provide even greater confidence.