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

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. © 2020, Editorial Board of Journal of Harbin Institute of Technology. All right reserved.