  Optimization-based approaches such as Model Predictive Control (MPC) are
promising approaches in proactive control for safety-critical applications with
changing environments such as automated driving systems. However, the
computational complexity of the MPC optimization problem coupled with the need
for real-time control in hazardous scenarios is the main bottleneck in
realization of automation levels four and five for driving systems. In this
paper, we construct hybrid formulations of the nonlinear MPC problem for
tracking control during emergency evasive maneuvers and assess their
computational efficiency in terms of accuracy and solution time. To hybridize
the MPC problem, we combine three hybrid approximations of the prediction model
and four approximations of the nonlinear stability and tire saturation
constraints and simulate the closed-loop behavior of the resulting controllers
during five emergency maneuvers for different prediction horizons. Further, we
compare the robustness of the controllers in the presence of friction
uncertainty as well to assess the accuracy-time trade-off in cases where the
friction of the road is either unknown or has an offset error with respect to
the prediction model. This robustness is studied for different levels of
friction uncertainty, as well as investigated with respect to the proximity to
the vehicle handling limits. We show that the hybridization of the MPC problem
is an efficient approach for real-time implementation of MPC during emergency
evasive maneuvers, paving the way for implementation of high levels of
automation.

Systems and Control;Systems and Control