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Prediction of Occupant Posture in Pre-Crash Using Reinforcement Learning
Published October 12, 2011 by Society of Automotive Engineers of Japan in Japan
Muscular control algorithm using reinforcement learning, which is supposed to be a mathematical model of learning process in the basal ganglia associated with human postural control was developed to predict occupant postures in pre-crash which could affect occupant injury outcomes in a crash. The algorithm was applied to a musculo-skeletal finite element (FE) model of human whole body. Simulations using the FE model under a condition with a brake deceleration predicted occupant postures in pre-crash.