a new approach for human-machine interaction research for closing the out-of-the-loop unfamiliarity
With the introduction of autonomous vehicles, drivers will be able to engage in non-related tasks while being driven. But in critical situations, the car needs the support of the human driver. How do distracted drivers get back into the control-loop quickly when the car requests a take-over? To investigate effective take-over actions, we developed an interactive virtual reality experiment, that uses the premises of the embodied cognition theory. Accordingly, the car should not only provide sensory input but also help enhance the driver’s motor response by interpreting intention and thus helping to accomplish desired actions. This binds humans and machines together in becoming true cooperation partners in joint action. Therefore, we aim for close monitoring of participants combined with sensorimotor feedforward and feedback. The presented prototype also serves as an open-access, cost-efficient toolkit that enables interested researchers to tailor the presented LoopAR tool to their own needs as part of a previously published toolkit called WestDrive. With the presented work, we hope to shift the paradigm of future research from only visual aids to full sensorimotor integration assistance.