Model Predictive Control (MPC) is an effective reference tracking strategy for automated vehicle control, particularly useful during emergency evasive maneuvers such as double lane changes. This control method often requires a high-fidelity vehicle model to accurately capture nonlinearities and uncertainties, significantly increasing computational demand. Hybrid systems modeling frameworks have been developed to approximate these nonlinearities, thereby reducing computational complexities while maintaining satisfactory tracking performance. However, existing benchmarks that evaluate the impact of these hybrid approximations on tracking performance and computational demands are lacking. Establishing such comparative benchmarks is crucial for understanding how different levels of model complexity affect the overall efficiency and effectiveness of model predictive approaches in automated driving scenarios.

This research addresses this gap by presenting a comparative analysis of various levels of hybrid model complexity. It assesses their tracking performance and computational demand using both MPC formulation and Model Predictive Contouring Control (MPCC) formalism in different emergency maneuver scenarios.
Four hybrid approximations of the nonlinear single-track vehicle model with varying complexity levels are considered and employed as prediction models in both MPC and MPCC optimization problems. The closed-loop behavior of these control frameworks is simulated in double-lane change maneuver scenarios, evaluating tracking performance scenarios with varying levels of curvature. Additionally, variations in friction and velocity are evaluated in different scenarios to assess controller robustness to model uncertainty.

Results indicate that reducing the complexity of hybrid approximations can decrease computational demand, albeit at the expense of tracking performance. Moreover, MPC formalism offers a more robust approach to tracking performance and provides a feasible solution in a broader range of scenarios than the MPCC framework. By shedding light on the impact of different complexity levels for the hybrid approximation of the nonlinear model and the control optimization problem formulation, this work offers comprehensive guidelines for hybrid MPC applications for automated driving in emergency scenarios.

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 collision. The results are compared to related state-of-the-art, showing its effectiveness.