Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 758-771.doi: 10.13229/j.cnki.jdxbgxb20210919

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Drivers' takeover behavior and intention recognition based on factor and long short⁃term memory

Rong-han YAO1(),Wen-tao XU1,Wei-wei GUO2   

  1. 1.School of Transportation and Logistics，Dalian University of Technology，Dalian 116024，China
    2.Beijing Key Laboratory of Urban Intelligent Traffic Control Technology，North China University of Technology，Beijing 100144，China
  • Received:2021-09-14 Online:2023-03-01 Published:2023-03-29

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Abstract:

To identify drivers’ takeover behavior and intention in an autonomous driving environment， with the help of a driving simulator and an eye-tracking device， the simulation tests were conducted to let drivers takeover an autonomous vehicle ten times in five emergency situations on a two-way six-lane freeway of 18.95 km. Using the vehicle operation and visual attention data， the three common factors were extracted by the factor analysis， and the K-means clustering analysis was used to qualitatively identify the drivers' takeover behavior and intention. The factor analysis was respectively combined with the support vector machine and the long short-term memory neural network， then the two models were obtained to quantitatively identify drivers' takeover behavior and intention. Research results show that： drivers' takeover behavior is influenced by their longitudinal response， lateral response and visual attention； the clustering analysis can qualitatively describe the takeover behavior and intention of different types of drivers and reveal the potential driving safety risks； compared with the support vector machine， the long short-term memory neural network and the factor and support vector machine model， the factor and long short-term memory model is more effective in identifying drivers' takeover intention， with the best four performance indices of accuracy rate， recall rate， F1-score and precision rate； and the common factors which are obtained using the factor analysis for data downscaling and effective information enrichment are helpful to improve the classification performance of the driving takeover intention recognition model. This study is helpful to identify drivers who are at a higher risk of takeover and to design some targeted driving assistance strategies.

Key words: engineering of communications and transportation system, drivers' takeover behavior and intention, factor analysis, K-means clustering analysis, long short-term memory neural network

CLC Number: 

  • U491.2

Fig.1

Equipment used for takeover tests"

Fig.2

Emergent locations and corresponding emergency situations for takeover incidents"

Table 1

Experimental results of participants taking over an autonomous vehicle"

参与者模拟试验正式试验
1接管质量好出现事件6和10时，自动驾驶车辆与前方车辆碰撞
2没及时接管出现事件6和10时，自动驾驶车辆转角过大
3接管质量好接管质量好
4接管质量好出现事件3和6时，自动驾驶车辆分别与故障车辆和前方车辆发生碰撞
6接管质量好出现事件3时，自动驾驶车辆转角过大
7接管质量好接管质量好
8车辆转角过小接管质量好
9接管质量好出现事件1和3时，自动驾驶车辆转角过大
10接管质量好出现事件3时，自动驾驶车辆转角过大；出现事件8时，自动驾驶车辆遇限速标志而没减速
11接管质量差出现事件2时，自动驾驶车辆转角过大；出现事件3时，自动驾驶车辆与故障车辆发生碰撞；出现事件4、5和6时，自动驾驶车辆刹停；出现事件8时，自动驾驶车辆遇限速标志而没减速

Fig.3

Variations of takeover behavior parameters with driving distance when participant 7 dealing with first takeover event"

Table 2

Dependent variables selected fortakeover behavior analysis"

因变量定义
视觉注意力数据左眼瞳孔直径标准差/mm左眼瞳孔直径的离散程度
右眼瞳孔直径标准差/mm右眼瞳孔直径的离散程度
凝视行为/%视线落在道路上的时间百分比
车辆运行数据速度标准差/（km?h-1）车辆速度的离散程度
最大速度差/（km?h-1）车辆速度的变化幅度
平均速度/（km?h-1）车辆速度的平均快慢程度
最大纵向加速度/（m?s-2）接管过程中车辆最大制动加速度
最大横摆角/rad车辆横摆角的变化幅度
横摆角标准差/rad车辆横摆角的离散程度

Table 3

Bartlett's spherical test and KMO test"

检验指标结果
巴特利特球形检验近似卡方875.790
自由度36
显著性0.000
KMO取样适切性量数0.672

Table 4

Total variance explained by factor analysis"

成分特征值方差百分比/%累积方差百分比/%
13.69841.08641.086
22.39526.60867.694
31.30614.51282.206
40.6577.29689.502
50.4424.91394.415
60.3043.37797.792
70.1021.13398.925
80.0870.96299.887
90.0100.113100.000

Table 5

Component matrix obtained by factor analysis"

原始指标因子载荷值
公因子1公因子2公因子3
速度标准差/（km?h-1）0.9670.0430.155
最大速度差/（km?h-1）0.9550.0970.200
平均速度/（km?h-1）-0.8900.048-0.082
最大纵向加速度/（m?s-2）0.7920.0080.277
左眼瞳孔直径标准差/mm0.2250.793-0.288
最大横摆角/rad-0.3210.7470.520
右眼瞳孔直径标准差/mm0.2870.719-0.363
横摆角标准差/rad-0.4360.6470.576
凝视行为/%-0.077-0.5080.584

Table 6

Rotated component matrix"

原始指标因子载荷值
公因子1公因子2公因子3
最大速度差/（km?h-1）0.9710.122-0.059
速度标准差/（km?h-1）0.9650.112-0.128
平均速度/（km?h-1）-0.864-0.0760.218
最大纵向加速度/（m?s-2）0.838-0.023-0.018
右眼瞳孔直径标准差/mm0.1840.8230.138
左眼瞳孔直径标准差/mm0.1540.8210.257
凝视行为/%0.096-0.7660.098
横摆角标准差/rad-0.1940.0430.950
最大横摆角/rad-0.1000.1750.944

Table 7

Factor score coefficient matrix"

原始指标因子得分系数
公因子1公因子2公因子3
左眼瞳孔直径标准差/mm0.0000.4000.046
右眼瞳孔直径标准差/mm-0.0040.416-0.019
凝视行为/%0.117-0.4460.181
平均速度/（km?h-1）-0.2470.0100.035
速度标准差/（km?h-1）0.286-0.0130.024
最大速度差/（km?h-1）0.296-0.0190.063
最大纵向加速度/（m?s-2）0.272-0.0920.092
横摆角标准差/rad0.045-0.0970.520
最大横摆角/rad0.062-0.0330.509

Fig.4

K-means clustering results"

Table 8

Clustering results and profile values when using drivers' takeover longitudinal response as a clustering indicator"

类别样本量试验编号接管强度轮廓值
平均值最小值最大值
1217，8，16，46，51，53，56，57，58，59，60，75，81，85，86，88，93，94，95，96，98强0.5800.1230.723
2371，4，9，10，17，20，26，27，28，29，31，34，38，45，47，49，50，52，54，55，61，64，67，69，71，72，73，74，76，77，78，79，83，84，89，97，99一般0.5070.0650.735
3412，3，5，6，11，12，13，14，15，18，19，21，22，23，24，25，30，32，33，35，36，37，39，40，41，42，43，44，48，62，63，65，66，68，70，80，82，87，90，91，92弱0.7110.0620.829

Table 9

Clustering results and profile values when using drivers' takeover lateral response as a clustering indicator"

类别样本量试验编号接管强度轮廓值
平均值最小值最大值
1202，3，5，6，16，17，20，33，42，43，47，52，53，55，72，75，81，82，88，99强0.4820.0820.620
2681，4，7，8，9，10，11，12，13，14，15，19，21，22，23，24，25，26，27，29，30，31，32，34，35，36，37，40，41，44，45，46，49，51，54，56，57，59，60，61，62，63，64，65，66，68，69，70，71，73，74，76，78，79，80，83，84，85，86，89，90，91，92，93，94，95，96，98一般0.5960.0250.773
31118，28，38，39，48，50，58，67，77，87，97弱0.6210.0450.789

Table 10

Clustering results and profile values when using drivers' takeover visual attention as a clustering indicator"

类别样本量试验编号接管强度轮廓值
平均值最小值最大值
1333，5，8，9，13，14，15，19，25，29，30，31，34，42，48，60，61，62，63，64，65，66，67，68，69，72，73，74，75，76，81，93，99强0.5880.2070.705
2421，11，12，16，17，20，21，22，23，24，26，27，28，32，35，39，40，41，45，46，47，49，51，53，56，57，58，59，70，71，77，78，79，83，84，85，86，88，89，91，97，98一般0.4990.0260.687
3242，4，6，7，10，18，33，36，37，38，43，44，50，52，54，55，80，82，87，90，92，94，95，96弱0.6310.2650.768

Fig.5

Variations of turn-light status with driving distance when participants dealing with first takeover event"

Fig.6

Structure of FA-LST model for recognizing driving intention"

Table 11

Confusion matrices of driving intention recognition obtained by LSTM and FA-LST models"

真实意图制动左转左转直行制动右转制动右转
FA-LSTLSTMFA-LSTLSTMFA-LSTLSTMFA-LSTLSTMFA-LSTLSTMFA-LSTLSTM
预测意图制动左转8534227101010000
左转113723151067000000
直行000212331226121500
制动00001225133133594600
右转0000320045845900
制动右转000000009900

Table 12

Confusion matrices of driving intention recognition obtained by SVM and FA-SVM models"

真实意图制动左转左转直行制动右转制动右转
FA-SVMSVMFA-SVMSVMFA-SVMSVMFA-SVMSVMFA-SVMSVMFA-SVMSVM
预测意图制动左转706911954480011
左转993112964254244500
直行305161124812411310374000
制动864818181081060011
右转0071040562139637811
制动右转000000000033

Table 13

Performance indices of driving intention recognition"

性能指标模型驾驶意图
制动左转左转直行制动右转制动右转
精确率FA-LST0.9880.9440.9800.9920.8690.000
LSTM0.9710.8120.9280.9780.8840.000
FA-SVM0.7780.8100.9220.8370.9060.500
SVM0.8210.7710.9040.8220.8940.500
召回率FA-LST0.7940.9710.9980.6520.9930.000
LSTM0.3180.8220.9930.6520.9960.000
FA-SVM0.7690.8450.9230.7770.8881.000
SVM0.7580.8040.9180.7630.8481.000
F1-分数FA-LST0.8800.9570.9890.7870.9270.000
LSTM0.4790.8170.9590.7820.9370.000
FA-SVM0.7730.8270.9220.8060.8970.667
SVM0.7880.7870.9110.7910.8700.667
准确率FA-LST0.951
LSTM0.903
FA-SVM0.890
SVM0.872
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