Optimization on Unified Theory of Acceptance and Use of Technology for Driverless Car Test Behavior

To demonstrate that the integrated technology acceptance model based on the keras multilayer perceptrons (MLP) has good optimization effects, and predicts the ability of the public to test the behavior of unmanned vehicles more accurately than traditional statistical methods, The training and testing of MLP and SPSS linear regression were compared under 96 valid questionnaires. The results show that when MLP modeling is completed, its MSE=1.191 is significantly better than SPSS MSE=1.316, which shows that the model has good prediction effect after optimization training through deep learning.