This paper first analyzes the influencing factors of free lane change of autonomous driving vehicle

and uses the traditional mathematical model to establish a vehicle lane change rule model based on the benefits

safety and necessity of lane change. Second

in view of the different factors considered in lane changing decision-making under different driving conditions

this paper proposes to extract decision variables from three aspects: physics-based features

interaction-aware features and road-structure-based features

and designs a feature extraction algorithm to make the factors considered in lane changing model decision-making more comprehensive. Then

for the multi-parameter and non-linearity problems existing in the decision-making process of autonomous lane change

a support vector machine (SVM) decision-making model based on Bayesian optimization algorithm (BOA) is proposed. Finally

the proposed model is verified on the NGSIM data set. The comparison test shows that the established BOA Gaussian-SVM model has a high comprehensive prediction performance

and the recognition rate of channel change behavior can reach 92.97%

which is better than other models and much higher than rule-based model. At the same time

simulation experiments are carried out on Airsim platform

and the results prove the effectiveness of BOA Gaussian-SVM decision model.