Cooperative dynamic vehicle control allocation using time-variant differential games

At higher automation levels, drivers do not need to permanently monitor the surrounding traffic environment and are allowed to focus on other tasks, while an advanced driver assistance system controls the vehicle. However, there is evidence that human drivers are prone to driving mistakes when they have to take over control from the ADAS due to their lack of situation awareness. Smoothly shifting control authority during the takeover period by a shared control approach promises to remedy this issue. We present a framework which models the human-machine interaction and makes it possible to adapt the shift of control to the needs of the human driver. Driver and ADAS are modeled as optimal controllers interfering with the same system (differential game). The handover/takeover-interaction is realized by time-variant objective functions. In consideration of the course of the takeover and the actions of the other partner this framework enables to obtain the allocation of the control effort by calculating the Nash equilibrium between driver and ADAS through solving a set of coupled Riccati differential equations. The functionality of this framework and the effects of three different takeover concepts (direct, continuous and stepwise shift) are shown via simulation of steering interaction during a lane change scenario.