Abstract-This paper presents the integration design and implementation of a longitudinal automation system with the interaction of human-in-the-loop (HITL). The proposed system has a hierarchical structure composed and consists of an adaptive sensory processor, a supervisory control and a regulation control. The adaptive sensory processor routes the information from on-board sensors to avoid missing detection of the vehicle ahead. Based on the recognized measurement from the adaptive sensory processor, the supervisory control determines the desired velocity for the vehicle so as to maintain safety and smooth operation in different modes. The regulation control utilizes soft-computing technique and drives the throttle action to execute the desired velocity commanded from the supervisory control. The feasible sensory distance is within 40 m, and the according driving velocity can achieve 100 km/h upward. The challenge in low velocity operation can also be handled by the regulation control against gear changes and torque converter. Among experimental tests under various kinds of traffic flows, the system validness is exhibited and also the preferable comfort is achieved through the examination of international standard ISO 2631.
S-system modeling from time series datasets can provide us with an interactive network. However, system identification is difficult since an S-system is described as highly nonlinear differential equations. Much research adopts various evolution computation technologies to identify system parameters, and some further achieve skeletal-network structure identification. However, the truncated redundant kinetic orders are not small enough as compared with the preserved terms. In this paper, we integrate quantitative genetics, bacterium movement, and fuzzy set theory into evolution computation to develop a new genetic algorithm to achieve convergence enhancement and diversity preservation. The proposed exploration and exploitation genetic algorithm (EEGA) can improve the best-so-far individual and ensure global optimal search at the same time. The EEGA enhances evolution convergence by golden section seed selection, normal-distribution reproduction, mixed inbreeding and backcrossing, competition elitism, and acceleration operations. Search-then-conquer evolution direction operations, eugenics-based screen-sifting mutation, eugenic self-mutation, and fuzzy-based tumble migration preserve population diversity to avoid premature convergence. Furthermore, to ensure that a reasonable gene regulation network is inferred, fuzzy composition is introduced to derive a reconstruction index. This performance index let EEGA possess self-interactive multiobjective learning. The proposed fuzzy-reconstruction-based multiobjective genetic algorithm is examined by three dry-lab biological systems. Simulation results show that a safety pruning action is guaranteed (the truncation threshold is set to be 10 −15 ), and only one-or two-step pruning action is taken.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.