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ABSTRACT 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. 

Wide-area soil moisture sensing is a key element for smart irrigation systems. However, existing soil moisture sensing methods usually fail to achieve both satisfactory mobility and high moisture estimation accuracy. In this paper, we present the design and implementation of a novel soil moisture sensing system, named as SoilId, that combines a UAV and a COTS IR-UWB radar for wide-area soil moisture sensing without the need of burying any battery-powered in-ground device. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received radar signals, and automatically detect and discard the data contaminated by the UAV's uncontrollable motion and the multipath interference. Furthermore, we leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. We have extensively evaluated SoilId against a variety of real-world factors, including the UAV's uncontrollable motion, the multipath interference, soil surface coverages, and many others. Specifically, the experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.