  Uncertainty in the behavior of other traffic participants is a crucial factor
in collision avoidance for automated driving; here, stochastic metrics should
often be considered to avoid overly conservative decisions. This paper
introduces a Stochastic Model Predictive Control (SMPC) planner for emergency
collision avoidance in highway scenarios to proactively minimize collision risk
while ensuring safety through chance constraints. To address the challenge of
guaranteeing the feasibility for the emergency trajectory, we incorporate
nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we
exploit Max-Min-Plus-Scaling (MMPS) approximations of the nonlinearities to
avoid conservatism, enforce proactive collision avoidance, and improve
computational efficiency in terms of performance and speed. Consequently, our
contributions include integrating a dynamic ego vehicle model into the SMPC
planner, introducing the MMPS approximation for real-time implementation in
emergency scenarios, and integrating SMPC with hybridized chance constraints
and risk minimization. We evaluate our SMPC formulation in terms of proactivity
and efficiency in various hazardous scenarios. Moreover, we demonstrate the
effectiveness of our proposed approach by comparing it with a state-of-the-art
SMPC planner and validate the feasibility of generated trajectories using a
high-fidelity vehicle model in IPG CarMaker.
