  With increased developments and interest in cooperative driving and higher
levels of automation (SAE level 3+), the need for safety systems that are
capable to monitor system health and maintain safe operations in faulty
scenarios is increasing. A variety of faults or failures could occur, and there
exists a high variety of ways to respond to such events. Once a fault or
failure is detected, there is a need to classify its severity and decide on
appropriate and safe mitigating actions. To provide a solution to this
mitigation challenge, in this paper a functional-safety architecture is
proposed and an optimization-based mitigation algorithm is introduced. This
algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle,
suffering from a severe fault, such as a power steering failure, to a
safe-state. The internal model of the NMPC uses the information from the fault
detection, isolation and identification to optimize the tracking performance of
the controller, showcasing the need of the proposed architecture. Given a
string of ACC vehicles, our results demonstrate a variety of tactical
decision-making approaches that a fault-affected vehicle could employ to manage
any faults. Furthermore, we show the potential for improving the safety of the
affected vehicle as well as the effect of these approaches on the duration of
the manoeuvre.
