基于规则与机器学习融合的换道决策建模方法研究

The autonomous lane-changing system is one of the important research directions of vehicle intelligence at present and lane-changing decision plays a decisive role for the success of lane-changing manoeuvre. An enabling lane-changing decision model is proposed by combing rule abstraction and machine learning model for lane-changing on structured roads. Considering the multi-parameter and nonlinear properties of the lane-changing decision-making process, a lane-changing decision model is first developed based on the support vector machine, and the Bayesian optimization algorithm is introduced to determine its optimal parameters. Then the major influencing factors of the lane-changing decision-making are analysed including the necessity, safety and benefit. These factors are added as the new features into the training datasets so that each original training sample is augmented to improve the training effect. Finally, the prediction accuracy of the developed model is examined based on the NGSIM data. The results show that the prediction accuracies of the trained lane-changing decision models based on the augmented and non-augmented training datasets are 84.22% and 77.19%, respectively. This verifies the effectiveness of the proposed lane-changing decision model and its augmented training method.