A Method for Identifying Drivers' Risk Perception Based on LightGBM
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摘要: 碰撞风险与风险感知能力有关, 为准确评估驾驶人风险感知能力, 设计考虑危险源个数与类型的驾驶模拟试验, 采集危险场景下的驾驶人驾驶行为与眼动特征等数据。利用Mantel-Haenszel检验分析危险源因素、驾驶人个人特性在不同风险感知水平人群下的差异性, 借助Spearman相关性分析探索驾驶行为、眼动特征与风险感知能力之间的关系。结果表明: 危险源个数、类型与风险感知能力负相关。驾龄、车速、纵向加速度、刹车深度、制动反应时间及位置等与风险感知能力显著相关。风险感知能力迟钝的驾驶人车速偏高且加速度更大, 刹车深度更深, 从发现危险事件到采取行动需要更多的反应时间。构建综合风险感知能力评价指标集, 借助Random Forest算法对特征进行重要性排序, 在此基础上利用LightGBM算法建立驾驶人风险感知能力判别模型, 分析不同特征个数输入对模型性能的影响。结果表明: 与SVM和AdaBoost等算法相比, 基于LightGBM算法的模型F1值达到86.07%, 精度为86.14%, 可以有效地对不同风险感知等级的驾驶人进行分类。
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关键词:
- 交通安全 /
- 风险感知 /
- 危险源 /
- LightGBM算法
Abstract: Accident risk is related to risk perception, and a driving simulation is designed to evaluate the ability of drivers' risk perception considering hazard target' type(explicit/implicit)and amount(single/double). Data of driving behaviors and eye movement characteristics is collected under different risk scenarios. Mantel-Haenszel test is used to analyze differences of hazard target' factors and drivers' characteristics under different risk perception levels. The Spearman correlation test is used to study relationships among driving behaviors, eye movement characteristics, and risk perception ability. The results show that hazard target' factors are negatively correlated with risk perception. Driving age, vehicle speed, longitudinal acceleration, braking depth, braking response time, and response position are significantly related to risk perception. Drivers with poor risk perception would drive at higher speeds with faster acceleration and deeper brake. They need more time to react to emergencies. The evaluation set of risk perception ability is constructed, and the importance of features is ranked using the Random Forest algorithm. The model of risk perception is built based on LightGBM with the effects of different features analyzed. The results show that the identification effect of the model is the best based on LightGBM compared with SVM and AdaBoost. The F1 value reaches 86.07%with an accuracy of 86.14%, which can classify drivers with different risk perception levels.-
Key words:
- traffic safety /
- risk perception /
- hazard target /
- LightGBM algorithm
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图 3 危险源个数、危险源类型、性别与风险感知能力散点图
Figure 3. Relationship between risk perception, target number, target type, and gender
图 4 显著因子在风险感知能力下的分布图
Figure 4. Distribution of significance factors under risk perception
表 1 危险场景描述
Table 1. Description of risk scenarios
序号 场景描述 冲突类型 图示 1 主车行驶于中间车道，其右前方行驶1辆小汽车 干扰 2 主车正穿过交叉口，其右侧人行横道有行人同向行走 干扰 3 主车正行驶于中间车道，其右前方有1名骑行者正前进 干扰 4 主车行驶于中间车道，其右前方有1辆小汽车打左转向灯3 s后向左换道至主车所在车道 机动车 图 2(a) 5 直行方向为绿灯，主车欲穿行交叉口，前方有1名行人突然冲入人行横道意欲穿行 行人 图 2(b) 6 直行方向为绿灯，主车行驶于最右车道，欲穿行交叉口，其右侧有1名骑行者欲换道，右侧交叉口处有1处施工围挡区，施工区前50 m处有前方施工标志，此时1辆小汽车欲从隔离围挡后面驶出 机动车、非机动车 图 2(c) 7 主车行驶于中间车道，行人被中央分隔带遮挡，人行横道前50 m处设有注意行人标志。当主车接近人行横道时，1名行人突然从中央分隔带处闯入人行横道并跑到道路右侧 行人 图 2(d) 8 主车行驶于中间车道，其右前方有1名骑行者，其所在车道被施工区阻碍，骑行者左换道至主车所在车道 非机动车 图 2(e) 9 主车行驶于最右车道，欲直行穿行交叉口，此时直行方向刚由红灯变为绿灯。其右前方交叉口处有1辆公交车停在交叉口旁等待绿灯通行，此时1名骑行者从公交车后冲出，并进入交叉口内 非机动车 图 2(f) 10 主车行驶于中间车道，其右前方有1辆小汽车亦正向前行驶，前方有人行横道，人行横道前50 m处有注意行人标志。右前方的小汽车打左转向灯3 s后于人行横道处向左换道至主车所在车道，而后，1名被中央分隔带植物所遮挡的行人突然穿过人行横道至道路右侧 机动车、非机动车 图 2(g) 11 主车正准备接近交叉口，其右前方有1名骑行者。交叉口处有施工围挡区，施工区前50 m处有前方施工标志，该施工区阻碍了骑行者的行进路线，因此，骑手向左换道至主车所在车道并加速驶离，而后，从隔离围挡区后驶出1辆小汽车 机动车、非机动车 图 2(h) 12 主车正以一定速度欲直行通过前方交叉口，此时直行方向刚由红灯变为绿灯，其右前方有行人准备通过马路，右前方交叉口处停有1辆公交车，此时行人突然快速通过人行横道，而后，公交车后有1辆自行车冲出，并进入交叉口内 行人、非机动车 图 2(i) 
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表 2 风险感知能力分类标准
Table 2. Standard for classifying risk perception
TTC < 3 s 3~5.5 s > 5.5 s 风险感知等级 高(危险) 中(一般) 低(安全)
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表 3 Search算法的模型参数优化
Table 3. Optimization of model parameters based on the Grid Search algorithm
参数 范围 优化结果 max-depth (1, 8, 1) 5 num-leaves (3, 31, 1) 10 min-data-in-leaf (1, 102, 10) 11 学习控制参数 feature-fraction [0.6, 0.7, 0.8, 0.9, 1.0] 0.6 bagging-fraction [0.6, 0.7, 0.8, 0.9, 1.0] 0.6 lambda-11 [1e-5，1e-3，1e-1，0.0, 0.1，0.3, 0.5, 0.7, 0.9，1.0] 0.1 lambda-12 [1e-5，1e-3，1e-1，0.0, 0.1，0.3, 0.5, 0.7, 0.9，1.0] 1e-05 min-split-gain [0.0, 0.1，0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9，1.0] 0.0 IO参数 max-bin (5, 256) 251
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表 4 Mantel-Haenszel检验结果
Table 4. Results of Mantel-Haenszel test
变量 线性间的联合检验 卡方 自由度 相关系数Cor 显著性(双侧)p 危险源个数 8.40 1 -0.159 0.004 危险源类型 25.96 1 -0.279 < 0.001 驾龄 8.98 1 0.164 0.003 性别 0.95 1 -0.053 0.329
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表 5 危险源个数、危险源类型、驾龄与风险感知能力交叉表
Table 5. Crosstabulation among risk perception, target number, target type, and driving years
风险感知能力 总计 安全 一般 危险 危险源个数 单目标 计数 37 29 76 142 期望计数 40.4 43.8 57.8 142 占危险源个数百分比/% 26.1 20.4 53.5 100.0 占风险感知能力百分比/% 38.9 28.2 55.9 42.5 双目标 计数 58 74 60 192 期望计数 54.6 59.2 78.2 192 占危险源个数百分比/% 30.2 38.5 31.3 100.0 占风险感知能力百分比/% 61.1 71.8 44.1 57.5 危险源类型 显性 计数 24 48 81 153 期望计数 43.5 47.2 62.3 153 占危险源类型百分比/% 15.7 31.4 52.9 100.0 占风险感知能力百分比/% 25.3 46.6 59.6 45.8 隐性 计数 71 55 55 181 期望计数 51.5 55.8 73.7 占危险源类型百分比/% 39.2 30.4 30.4 100.0 占风险感知能力百分比/% 74.7 53.4 40.4 54.2 驾龄 新手 计数 47 32 40 119 期望计数 33.8 36.7 48.5 119 占驾龄百分比/% 39.5 26.9 33.6 100.0 占风险感知能力百分比/% 49.5 31.1 29.4 35.6 熟练 计数 48 71 96 215 期望计数 61.2 66.3 87.5 215 占驾龄百分比/% 22.3 33.0 44.7 100.0 占风险感知能力百分比/% 50.5 68.9 70.6 64.4
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表 6 描述性分析
Table 6. Descriptive analysis
指标 风险感知能力 高(危险) 中(一般) 低(安全) 车速/(km/h) 均值 48.56 41.70 35.40 标准差 11.15 10.10 7.56 纵向加速度/(m/s3) 均值 -0.59 -0.48 -0.41 标准差 0.51 0.37 0.26 方向盘旋转率/s-1 均值 -0.002 5 -0.000 8 -0.001 4 标准差 0.738 3 0.0197 0.019 2 刹车深度/% 均值 48 37 31 标准差 27 23 18 制动反应时间/s 均值 0.65 0.35 0.17 标准差 0.22 0.19 0.22 制动反应位置/m 均值 114.96 74.13 34.36 标准差 24.64 26.58 35.93 注视次数/次 均值 28.54 30.52 26.89 标准差 11.09 15.20 13.41 首次注视时间/s 均值 0.58 0.56 0.64 标准差 0.18 0.22 0.19 VSS/rad 均值 0.045 0.044 0.049 标准差 0.24 0.02 0.03 HSS/rad 均值 0.068 0.078 0.087 标准差 0.35 0.04 0.03
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表 7 Spearman相关结果
Table 7. Results of Spearman correlation
特征 相关系数 显著性(双尾) 特征 相关系数 显著性(双尾) 车速 0.508 < 0.001 制动反应位置 0.742 < 0.001 纵向加速度 -0.162 0.003 VSS -0.013 0.813 方向盘旋转率 0.052 0.341 HSS -0.202 < 0.001 刹车深度 0.279 < 0.001 注视次数 0.028 0.608 制动反应时间 0.734 < 0.001 首次注视时间 -0.114 0.037
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表 8 不同特征数下的模型性能
Table 8. Performance evaluation indicators of different features
% 特征个数 评价指标 F1值 precision recall accuracy 2 73.33 73.60 73.27 73.27 4 85.00 85.67 85.15 85.15 6 86.07 86.41 86.14 86.14 8 83.04 84.53 83.17 83.17 10 78.44 80.93 78.22 78.22 12 80.44 82.72 80.20 80.20 14 80.31 83.43 80.20 80.20
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表 9 3种模型的性能比较
Table 9. Performance of three models
% 算法 评价指标 F1值 precision recall accuracy LightGBM 86.07 86.41 86.14 86.14 SVM 77.37 78.79 77.23 77.23 AdaBoost 78.51 78.58 78.22 78.22
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