基于XGBoost算法的危险场景驾驶行为模式分析及安全评估

魏田正; 魏雯; 李海梅; 刘浩学; 朱彤; 刘斐

doi:10.3963/j.jssn.1674-4861.2022.05.006

基于XGBoost算法的危险场景驾驶行为模式分析及安全评估

doi: 10.3963/j.jssn.1674-4861.2022.05.006

魏田正^1,,
魏雯²,
李海梅¹,
刘浩学^{1, 3},
朱彤^2, ,,
刘斐¹

1.
长安大学汽车学院西安 710064
2.
长安大学运输工程学院西安 710064
3.
长安大学汽车运输安全保障技术交通行业重点实验室西安 710064

基金项目:

国家重点研发计划项目 2019YFE0108000

长安大学研究生科研创新实践项目 300103722024

详细信息

作者简介:
魏田正（1996—），博士研究生. 研究方向：交通安全与驾驶员行为.E-mail：2019022010@chd.edu.cn

通讯作者:
朱彤（1977—），博士，副教授. 研究方向：交通安全与驾驶员行为. E-mail：zhutong@chd.edu.cn

中图分类号: X951
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- 文章访问数: 1230
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- 被引次数: 0
出版历程
- 收稿日期: 2022-07-25
- 网络出版日期: 2022-12-05

An Analysis of Driving Behavior Model and Safety Assessment Under Risky Scenarios Based on an XGBoost Algorithm

WEI Tianzheng^1
,,
WEI Wen²,
LI Haimei¹,
LIU Haoxue^{1, 3},
ZHU Tong^{2
, ,},
LIU Fei¹

1.
School of Automobile, Chang'an University, Xi'an 710064, China
2.
College of Transportation Engineering, Chang'an University, Xi'an 710064, China
3.
Key Laboratory of Automotive Transportation Safety Enhancement Technology of Ministry of Transport, Chang'an University, Xi'an 710064, China

摘要

摘要: 危险感知能力对驾驶人的驾驶行为模式具有重要影响。为准确评估驾驶人的危险感知能力、提升危险感知水平判别的准确度，提出了基于模拟驾驶技术的危险感知能力影响分析方法和基于极端梯度提升树（XGBoost）算法的危险感知水平判别模型。通过设计3种常见交通冲突场景，采集模拟驾驶中驾驶人的多维度驾驶行为特征数据，并分析危险感知能力与驾驶行为的相关关系。通过模拟实验发现：驾驶人对行人的危险感知能力较弱，易发生碰撞事故；驾驶人在危险场景中的车速（p＝0.01）、制动反应位置（p < 0.01）以及反应时间（p < 0.01）与危险感知水平之间存在显著负相关关系。在相关性分析的基础上，利用XGBoost算法识别能反映驾驶人危险感知能力的重要特征变量，并构建以制动反应位置、反应时间、车速、刹车深度，以及加速度为指标的驾驶人危险感知水平判别模型；通过与LightGBM、支持向量机（SVM），以及逻辑回归（LR）等算法分类预测性能的对比分析，评价危险感知模型的判别精度，结果表明：基于XGBoost算法的危险感知水平判别模型的判别准确率为84.8%、F1值为83.4%、AUC值为0.959，优于LightGBM（准确率为78.8%、F1值为76.7%、AUC值为0.924）、SVM（准确率为57.6%、F1值为42.2%、AUC值为0.859），以及LR算法（准确率为69.7%、F1值为65.5%、AUC值为0.836）。所提方法可为判别驾驶人危险感知能力及其对驾驶行为模式的影响提供可靠手段。
- 交通安全 /
- 驾驶行为 /
- 危险感知 /
- 机器学习 /
- XGBoost
Abstract: Hazard perception is a critical factor of a driving behavior model. A simulator-based method and an extreme gradient boosting tree(XGBoost)algorithm are proposed, in order to study the impacts of hazard perception on driving behaviors and improve the accuracy of hazard perception. Three typical scenarios of traffic conflicts are simulated, and a large amount of driving behavior data are collected. The correlation between hazard perception and driving behavior models is discussed under the three scenarios. The correlation analysis reveals that when hazard perception(e.g., dangerous behaviors of pedestrians)is weak, and the vehicle speed(p=0.01), braking reaction position(p < 0.01), and reaction time(p < 0.01)are significantly negatively correlated with the drivers'hazard perception. Based on the correlation analysis, the XGBoost algorithm is used to identify important features determining the capability of hazard perception of drivers. Then, a discriminant model of hazard perception is proposed with following the indicators, such as braking reaction position, reaction time, vehicle speed, braking depth, and acceleration. Compared the proposed method with Light Gradient Boosting Machine(LightGBM), Support Vector Machine(SVM), and Logistic Regression(LR)algorithms, it is found that the accuracy of the XGBoost-based method is 84.8%, its F1-score is 83.4%, and the area under the receiver operating characteristic Curve(AUC)is 0.959, which is better than the LightGBM(accuracy is 78.8%, F1-score is 76.7%, and AUC is 0.924), SVM(accuracy is 57.6%, F1-score is 42.2%, and AUC is 0.859)and LR algorithm(accuracy is 69.7%, F1-score is 65.5%, and AUC is 0.836). In conclusion, the proposed method can provide a more reliable way for understanding the capability of hazard perception of drivers and its impacts on driving behavior models.
- traffic safety /
- driving behavior /
- hazard perception /
- machine learning /
- XGBoost

HTML全文

图 1 驾驶模拟器

Figure 1. Driving simulator

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图 2 危险场景示意图

Figure 2. Schematic diagram of risk scenarios

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图 3 3种类型交通冲突场景的TTC值箱图

Figure 3. TTC box plot of three types of traffic conflict scenarios

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图 4 制动反应位置与车速交互作用分析

Figure 4. Analysis of interaction between braking reaction position and vehicle speed

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图 5 刹车深度与反应时间交互作用分析

Figure 5. Analysis of interaction between brake depth and reaction time

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图 6 特征重要性

Figure 6. Importance of features

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图 7 特征变量个数对模型性能的影响分析

Figure 7. Analysis of the influence of the number of characteristic variables on model performance

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图 8 危险感知水平预测混淆矩阵图

Figure 8. Confusion matrix plot of hazard perception prediction level

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图 9 ROC曲线图

Figure 9. ROC curve

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表 1 风险场景描述

Table 1. Risk scenario description

场景	风险类型	交通冲突类型	示意图	场景描述
1	显性危险	轿车与轿车	图 2（a）	被试车辆右侧有1辆同向行驶的轿车在打转向灯3 s后换道至中间车道
2	隐性危险	轿车与轿车	图 2（b）	被试车辆直行方向为绿灯，在其右前方有1辆轿车正准备从施工区隔离围挡后驶出
3	显性危险	轿车与行人	图 2（c）	被试车辆直行方向为绿灯，在右侧交叉口处人行横道上有1名行人正准备冲入人行横道
4	隐性危险	轿车与行人	图 2（d）	被试车辆驶近前方人行横道时，有1名被树木遮挡的行人突然冲入人行横道
5	显性危险	轿车与非机动车	图 2（e）	被试车辆右前方有1名骑行者的行驶方向被施工区阻碍，骑行者换道至中间车道
6	隐性危险	轿车与非机动车	图 2（f）	被试车辆直行方向为绿灯，在其右前方公交车后有1名骑行者突然从公交车后冲出

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表 2 危险场景TTC值的总体情况

Table 2. Overall situation of TTC values in risky scenarios

危险场景类型	均值	标准差	中位数	百分位数/%
危险场景类型	均值	标准差	中位数	25	75
显性危险	4.14	1.80	3.52	2.60	5.68
隐性危险	3.60	2.47	2.90	1.64	5.47

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表 3 Spearman相关性分析结果

Table 3. Spearman correlation analysis results

特征	相关系数	显著性（双尾）p
车速/（km/h）	-0.204	0.010
加速度/（m/s²）	-0.055	0.491
制动反应位置/m	-0.743	0.000
刹车深度	-0.107	0.178
转向盘旋转率/（1/s）	0.015	0.847
制动车速/（km/h）	-0.078	0.328
反应时间/s	-0.606	< 0.001

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表 4 描述性统计分析

Table 4. Descriptive statistical analysis

指标	危险感知能力
指标	低	标准差	中	标准差	高	标准差
车速/（km/h）	47.68	12.10	44.66	8.09	41.80	8.30
纵向加速度/（m/s²）	-0.41	0.44	-0.48	0.45	-0.41	0.30
制动反应位置/m	107.52	36.86	80.71	19.98	36.42	20.54
刹车深度	0.11	0.08	0.11	0.05	0.08	0.05
转向盘旋转率/（1/s）	0.02	0.02	0.02	0.02	0.02	0.02
制动车速/（km/h）	59.88	11.60	59.01	9.75	57.46	8.94
反应时间/s	0.77	0.38	0.48	0.56	0.29	0.42

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表 5 最优模型参数

Table 5. Optimal model parameters

参数	范围	参数最优值
learning_rate	[0，1，0.01]	0.1
max_depth	[1，15，1]	5
n_estimators	[0，1 000，100]	200
subsample	[0，1，0.1]	0.7
min_child_weight	[1，10，1]	3
reg_alpha	[0，0.05，0.001]	0.001
gamma	[0，1，0.1]	0.28
colsample_bytree	[0，1，0.1]	0.8

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表 6 模型分类性能

Table 6. Performance of model classification

模型	accuracy	precision	recall	F1值	AUC
XGBoost	0.848	0.839	0.850	0.834	0.959
LightGBM	0.788	0.775	0.777	0.767	0.924
LR	0.697	0.660	0.656	0.655	0.836
SVM	0.576	0.452	0.487	0.422	0.859

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