Connection of nonlinear model predictive controllers for smooth task switching in autonomous driving

• Published in: Asian journal of control 2023-05, Vol.25 (3), p.1805-1822
• Main Authors: Honda, Kohei, Okuda, Hiroyuki, Suzuki, Tatsuya, Ito, Akira
• Format: Article
• Language: English
• Subjects: autonomous driving; Cost function; Decision making; hybrid system; Hybrid systems; model predictive control; Motion planning; multiple driving tasks; Nonlinear control; Optimization; Passenger comfort; Predictive control; switched model predictive control; Switching; Traffic control; Traffic models
• ISSN: 1561-8625; 1934-6093
• DOI: 10.1002/asjc.2892

Motion planning, decision making, and control are vital functions in autonomous driving for accomplishing the desired driving task while considering passenger comfort, road infrastructure, and surrounding traffic participants. Model predictive control (MPC) is a promising method for simultaneously realizing these functions. However, formulating a single MPC that can run through all driving scenarios is difficult, and previous research has often been conducted to design an MPC for a specific driving task. To extend the availability of MPC for all driving tasks, smooth switching between different MPCs designed for each driving task must be addressed. One of the difficulties in switching between MPCs is guiding the state to a feasible set of optimization problems after switching. In this paper, we present a new framework to realize the smooth connection of MPCs, that is, to reduce the optimization infeasibility at the time of MPC switching. In our proposed method, two general nonlinear MPCs with different state spaces, cost functions, constraints, and formulations can be systematically switched via automatically generated intermediate‐MPCs without requiring any particular alterations. This can help reduce the system complexity of the hybrid MPC system.