State Estimation and Sensorimotor Noise in a Driver Steering Model with a Gaussian Process Internal Model
摘要
Refinements to a mathematical model of human drivers’ steering control incorporating driver learning are reported. State estimation and realistic sensorimotor noise sources are introduced to the driver model to better represent neural processes. It is found that the driver model exhibits the expected learning behaviour in terms of estimation and control performance. Further work is planned to validate the model experimentally.