Driving load grading evaluation method based on hidden Markov model

• Main Authors: ZHANG JIE, SI YIHAO, SHI JIAN, TAN HAIQIU, ZHANG HAODONG, SON DONG-SEON, XIE LIJUN, WANG WUHONG, JIANG XIAOBEI
• Format: Patent
• Language: Chinese; English
• Published: 27.10.2023
• Subjects: CALCULATING; COMPUTING; CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; COUNTING; DIAGNOSIS; ELECTRIC DIGITAL DATA PROCESSING; HUMAN NECESSITIES; HYGIENE; IDENTIFICATION; MEDICAL OR VETERINARY SCIENCE; PERFORMING OPERATIONS; PHYSICS; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT; SURGERY; TRANSPORTING; VEHICLES IN GENERAL

The invention discloses a driving load grading evaluation method based on a hidden Markov model, and belongs to the field of man-machine collaborative driving. The implementation method comprises the steps that a portable physiological recorder is adopted, electrocardiosignals of a driver in a preset driving load grade scene are collected, and sample data sets under all driving load grades are obtained by segmenting an electrocardiosignal sequence sample data set; the method comprises the following steps: acquiring electrocardiosignals, performing feature extraction and standardization processing on the electrocardiosignals from the angles of time domain, frequency domain and non-linear domain, and performing parameter optimization and training on a model based on a hidden Markov model (HMM) and a grid optimization method to realize graded evaluation and monitoring on the driving load of a driver so as to remind the driver to keep a proper workload level. The method can be applied to the field of man-machine