Vehicle lane-changing decision model based on decision mechanism and support vector machine

Abstract:

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.