Efficient MPC for Emergency Evasive Maneuvers, Part II: Comparative Assessment for Hybrid Control

TL;DR

This paper develops hybrid formulations of nonlinear MPC to enable real-time emergency evasive maneuvers in automated driving, balancing computational efficiency and robustness under friction uncertainties.

Contribution

It introduces hybrid approximations of the MPC problem and evaluates their efficiency and robustness, advancing real-time control for safety-critical autonomous driving scenarios.

Findings

Hybrid MPC formulations improve solution time during emergency maneuvers.

The hybrid controllers maintain robustness under varying friction uncertainties.

Simulation results demonstrate feasibility for high-level automation in safety-critical situations.

Abstract

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.