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Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (6): 1171-1181    DOI: 10.3785/j.issn.1008-973X.2019.06.017
Computer and Aut omation Technology     
Fusion decision model for vehicle lane change with gradient boosting decision tree
Bing XU(),Xiao LIU,Zi-yang WANG,Fei-hu LIU,Jun LIANG*()
College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
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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 tlag 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 tlag 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.

Key wordsgradient boosting decision tree (GBDT)      free lane changing behavior      NGSIM dataset      lane changing decision model      collision time     
Received: 27 December 2018      Published: 22 May 2019
CLC:  TP 181  
Corresponding Authors: Jun LIANG     E-mail:;
Cite this article:

Bing XU,Xiao LIU,Zi-yang WANG,Fei-hu LIU,Jun LIANG. Fusion decision model for vehicle lane change with gradient boosting decision tree. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1171-1181.

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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 tlag 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 tlag 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.

关键词: 梯度提升决策树(GBDT),  自由换道行为,  NGSIM数据集,  换道决策模型,  碰撞时间 
Fig.1 Structure diagram of fusion decision model based on gradient boosting decision tree (GBDT)
Fig.2 Study area of NGSIM dataset
Fig.3 Data filtering results of No.260 vehicle
Fig.4 Schematic diagram fir free lane changing behavior of main vehicle
特征编号 符号 特征含义
1 ${v_{\rm{s}}}$ 主体车辆速度
2 ${a_{\rm{s}}}$ 主体车辆加速度
3 ${d_{{\rm{lead}}}}$ 目标车道前车与主体车辆的距离
4 $\Delta {v_{{\rm{lead}}}}$ 目标车道前车与主体车辆速度差
5 ${d_{{\rm{lag}}}}$ 目标车道后车与主体车辆的距离
6 $\Delta {v_{{\rm{lag}}}}$ 目标车道后车与主体车辆速度差
7 ${d_{{\rm{lc}}}}$ 当前车道前车与主体车辆的距离
8 $\Delta {v_{{\rm{lc}}}}$ 当前车道前车与主体车辆速度差
9 ${d_{{\rm{fc}}}}$ 当前车道后车与主体车辆的距离
10 $\Delta {v_{{\rm{fc}}}}$ 当前车道后车与主体车辆速度差
11 $\Delta {a_{{\rm{lead}}}}$ 目标车道前车与主体车辆加速度差
12 $\Delta {a_{{\rm{lag}}}}$ 目标车道后车与主体车辆加速度差
13 $t_{\rm lc}$ 主体车辆与当前车道前车碰撞时间
14 ${t_{{\rm{lead}}}}$ 主体车辆与目标车道前车碰撞时间
15 ${t_{{\rm{lag}}}}$ 主体车辆与目标车道后车碰撞时间
16 $\Delta {a_{{\rm{lc}}}}$ 当前车道前车与主体车辆加速度差
17 $\Delta {a_{{\rm{fc}}}}$ 当前车道后车与主体车辆加速度差
Tab.1 Characteristic variable of free lane changing decision behavior
Fig.5 Importance of characteristic variables under different evaluation indicators
换道决策模型 参数寻优范围 参数说明
SVM “C”:(0.001,60) C:误差惩罚项参数
“gamma”:(0.000 1,2) Gamma:高斯核函数的参数
RF “n_estimators”:(100,1 000) n_estimators:树的数量
“max_depth”:(3,10) max_depth:树的最大深度
“max_features”:(0.1,0.999) max_features:寻找最佳分裂点时属性数量占比
“min_samples_split”:(2,10) min_samples_split:分裂节点所需最少样本数
GBDT “learning_rate”:(0.001,0.2) learning_rate:算法学习率
“n_estimators”:(100,2 000) n_estimators:树的数量
“subsample”:(0.1,1) subsample:训练样本下采样比例
“max_depth”:(5,10) max_depth:树的最大深度
“min_samples_leaf”:(1,12) min_samples_leaf:分裂节点所需最少样本数
Tab.2 Parameter optimization range of different lane changing decision models
Fig.6 Parameter optimization process of different lane changing decision models
决策模型 ${R_{{\rm{acc}}}}$ ${R_{{\rm{TP}}}}$ ${R_{{\rm{TN}}}}$
SVM 81.82 72.79 85.30
RF 92.99 93.20 92.91
GBDT 93.56 91.84 94.23
融合决策模型 95.45 95.24 95.54
Tab.3 Performance of different decision models on test sets
特征变量组 ${R_{{\rm{acc}}}}$ ${R_{{\rm{TP}}}}$ ${R_{{\rm{TN}}}}$
[1,3,4,5,6,11,12] 85.42 89.12 83.99
[1,3,4,5,6,11,12,14] 88.64 89.12 88.45
[1,2,···,17] 95.45 95.24 95.54
Tab.4 Comparison experiment of different characteristic variables
Fig.7 Feature selection process of fusion decision model based on GBDT
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