Autonomous Vehicle Path Tracking Using Event-Triggered MPC With Switching Model: Methodology and Real-World Validation

Model predictive control (MPC) is advantageous for autonomous vehicle path tracking but suffers from high computational complexity for real-time implementation. Event-triggered MPC aims to reduce this burden by optimizing the control inputs only when needed instead of every time step. Existing works in literature have been focused on algorithmic development and simulation validation for very specific scenarios. Therefore, event-triggered MPC in real-world full-size vehicle has not been thoroughly investigated. This work develops event-triggered MPC with switching model for autonomous vehicle lateral motion control, and implements it on a production vehicle for real-world validation. Experiments are conducted under both closed road and open road environments, with both low speed and high speed maneuvers, as well as stop-and-go scenarios. The efficacy of the proposed event-triggered MPC, in terms of computational load saving without sacrificing control performance, is clearly demonstrated. It is also demonstrated that event-triggered MPC can sometimes improve the control performance, even with less number of optimizations, thus contradicting to existing conclusions drawn from simulation.