In this paper, a Gaussian Mixture Model clustering-based method is proposed to learn the optimal threshold of the knock intensity metric which minimizes the probability of the judgment error of the knock event. Firstly, statistical analysis of knock intensity on an experimental database measured from a gasoline engine test bench is conducted and the results show that Gaussian Mixture Model (GMM) has better fitting performance than the single Gaussian model. Then, the learning problem which consists of two sub-problems is formulated based on the assumption that the probability distribution model of knock intensity is a two-component GMM. The first sub-problem is a clustering problem in which parameters of the two-component GMM are optimized to maximize the likelihood of obtaining the measurements. The second sub-problem is also an optimization problem in which the probability of the judgment error is formulated as a function of the threshold. The solution to the problem is the required threshold decision. The convergence analysis of the proposed method is implemented. Furthermore, the proposed method is also validated in the experimental database. The proposed method is expected to be applied in the real-world application to save time, labor effort on the knock threshold calibration.

This article presents an on-board map learning–based spark advance control framework for combustion engines. The proposed control framework addresses the knock probabilistic constrained thermal efficiency optimization problem with three layers. First, in the upper layer, maps of knock event distribution and thermal efficiency are learned with manifold pressure and combustion phase as inputs. Second, the middle layer generates the knock probability constrained optimal combustion phase reference that is subsequently tracked by a hypothesis test-based feedback controller. Third, the lower layer employs a partial likelihood-based knock controller that retards the spark advance in case of the frequent knock events. The key contributions of this work are the three-layer control framework and the knock event distribution map learning in the upper layer. The knock event is supposed to obey binomial distribution, and the distribution is modeled by beta distribution and learned in the perspective of Bayesian learning. Moreover, the normalization algorithm is proposed for online feedfoward map update. The proposed map learning–based spark advance control framework is experimentally validated in a test bench equipped with a spark-ignition engine.

Autonomous vehicles (AVs) bring advantages such as comfort, safety, and eco-friendliness compared to conventional ones. The focus on comfort has become increasingly important as it plays a key role in the widespread acceptance of AVs. Passenger demand for alleviating motion sickness (MS) during travel has also driven extensive research in this area. This manuscript conducts a comprehensive and comparative review of published articles to provide up-to-date research outcomes on MS mitigation. It also examines the optimization methods employed over the past two decades to enhance motion comfort in AVs. The methods for detecting and resolving MS are elaborated upon, and the research gap and challenges are addressed. Furthermore, the manuscript presents the current efficacy of the research and proposes an anti-motion sickness control framework that utilizes a cloud control platform, which might offer potential future research directions in the field of autonomous driving. This study serves as a valuable reference for future efforts and opportunities to improve the motion comfort of AVs.