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This study addresses a critical aspect of automated driving: enhancing safety during highway emergency scenarios. This is achieved by formulating the problem within the framework of Stochastic Model Predictive Control (SMPC). In SMPC, safety constraints are designed such that a small chance of constraint violation is accepted, contrary to conventional Model Predictive Control (MPC), where hard constraints are imposed on the optimization problem that may never be violated. In the state-of-the-art, the exploration of SMPC for motion planning is minimal and focuses mostly on optimistic motion planning, depending on conservative backup solutions in emergencies. This research contributes to the state-of-the-art by designing a collision-avoiding, risk-averse, emergency motion planner by formulating the problem as SMPC, acknowledging the stochastic nature of real-world driving environments. By formulating the emergency planning problem specifically as SMPC, including risk minimization and providing a reference lane where the collision risk is minimal, the motion planner successfully navigates the automated vehicle through complex, dynamic emergency scenarios involving both static and dynamic obstacles without collision. In addition, the Interactive Multiple Model Algorithm (IMM) is used to estimate the maneuver intention of other vehicles (OVs). These maneuver estimates are used to formulate the probabilistic constraints in SMPC. Furthermore, two novel backup controllers are designed to compute a fast, collision-avoiding trajectory when SMPC is infeasible. From the results, the backup controller based on MPC, designed such that it is always feasible and provides support in finding a feasible SMPC solution in the next iteration, showed the best performance. The success of the proposed emergency motion planner is presented through simulation in 6 complex scenarios, where the planner safely navigates the automated vehicle through the emergencies without co, Mechanical Engineering | Systems and Control