采用梯度提升决策树的车辆换道融合决策模型

车辆在执行换道行为时，由于受到较多环境因素影响，难以准确进行换道识别和预测. 为解决这一问题，提出一种基于梯度提升决策树（GBDT）进行特征变换的融合换道决策模型，以仿真驾驶员在高速公路上自由换道时的决策行为. 采用主体车辆与目标车道后车的碰撞时间 t_lag 及车辆周围交通状态变量进行车辆换道行为的建模分析，在NGSIM数据集上对建立的融合换道决策模型进行参数标定和模型测试. 实验结果表明：融合换道决策模型以95.45%的预测准确率超越支持向量机、随机森林和GBDT等单一的换道决策模型，获得了最突出的表现. 变量分析结果表明：新引入的换道决策变量 t_lag 对车辆换道行为具有重要影响. 提出的融合换道决策模型能够进一步减少因换道决策误判而导致的交通事故.

Fusion decision model for vehicle lane change with gradient boosting decision tree

When the vehicle performs lane changing behavior, it is challenging to accurately identify and predict the vehicle’s behavior due to the influence of various environmental factors. In order to solve this problem, a fusion lane changing decision model was proposed; the gradient boosting decision tree (GBDT) was applied for feature transformation. This fusion model was applied to simulate the driver’s decision-making behavior while freely changing lanes on the expressway. The collision time t_lag of the main vehicle and lag vehicle on the target lane and other vehicle traffic state variables were introduced into the model to analyze the lane changing behavior. The parameter calibration and the test of the established fusion lane changing decision model were carried out on the NGSIM (Next Generation Simulation) dataset. The experimental results show that the proposed fusion lane changing decision model surpasses the single lane changing decision model, like support vector machine, random forest and GBDT, with prediction accuracy of 95.45%, giving the most outstanding performance. The variable analysis results show that the newly introduced lane change decision variable t_lag plays a positive role in the vehicle’s lane changing behavior. The proposed fusion lane changing decision model is able to further reduce the traffic accidents caused by misjudgment of lane changing decisions.