Event-Triggered Model Predictive Control for Autonomous Vehicle Path Tracking: Validation Using CARLA Simulator
Zhaodong Zhou, Christopher Rother, Jun Chen
Abstract
Model predictive control (MPC) has been widely researched for automotive control. However, the real-world application of MPC for autonomous vehicles (AV) is still limited due to the high computational requirement of solving the real-time optimization problem. To address this issue, this letter presents an event-triggered MPC framework and illustrates its application in AV path tracking control using the CARLA simulation environment. Unlike traditional time-triggered MPC, event-triggered MPC solves the optimization problem only when an event is triggered. Otherwise, the previously optimized control sequence is used to determine control action. Therefore, when following the same path, event-triggered MPC solves fewer optimization problems than time-triggered MPC, thereby reducing the computation requirements. To demonstrate the effectiveness of the proposed approach, both time-triggered MPC and event-triggered MPC are simulated and compared using CARLA. Results validate that the proposed event-triggered MPC method reduces significant computation with acceptable performance degradation.