Efficient MPC for Emergency Evasive Maneuvers, Part I: Hybridization of the Nonlinear Problem

TL;DR

This paper presents a hybridization framework for nonlinear MPC in emergency vehicle maneuvers, enabling faster, sub-optimal solutions by approximating dynamics and constraints with hybrid models, suitable for real-time applications.

Contribution

It introduces a hybrid modeling approach using MMPS to approximate nonlinear vehicle dynamics and constraints, improving computational efficiency during emergency maneuvers.

Findings

Hybrid models outperform traditional methods in computational speed.

Two novel grid types improve constraint approximation accuracy.

Guidelines provided for implementing hybridization in other control applications.

Abstract

Despite the extensive application of nonlinear Model Predictive Control (MPC) in automated driving, balancing its computational efficiency with respect to the control performance and constraint satisfaction remains a challenge in emergency scenarios: in such situations, sub-optimal but computationally fast responses are more valuable than optimal responses obtained after long computations. In this paper, we introduce a hybridization approach for efficient approximation of nonlinear vehicle dynamics and non-convex constraints using a hybrid systems modeling framework. Hybridization allows to reformulate the nonlinear MPC problem during emergency evasive maneuvers as a hybrid MPC problem. In this regard, Max-Min-Plus-Scaling (MMPS) hybrid modeling is used to approximate the nonlinear vehicle dynamics. Meanwhile, different formulations for constraint approximation are presented, and various grid-generation methods are compared to solve these approximation problems. Among these, two novel grid types are introduced to structurally include the influence of the system dynamics on the grid point distributions in the state domain. Overall, the work presents and compares three hybrid models and four hybrid constraints for efficient MPC synthesis and offers guidelines for implementation of the presented hybridization framework in other applications.