26M-09「非侵襲型センサーによる運転者の注意散漫行動検出を目的とした認知的負荷の影響の分析」
研究代表者氏名 岩手県立大学 教授 Basabi Chakraborty
研究参加者氏名 千葉工業大学 准教授 真部 雄介
岩手県立大学 学生 中野 光太朗
岩手県立大学 学生 吉田 将
＜要旨＞
車を安全に運転する上で、意識を運転に集中させていることは重要であり、運転者が注意散漫状態になる原因としては運転者へ認知的負荷がある。
本研究では、運転者のいくつかのパターンの運転者の注意散漫状態を、運転者の行動から自動的に検出することを目的とする。そのために、実験として複数の状況での運転データ(操舵角やアクセルストローク、車のスピード)を取得し、データマイニングアルゴリズムを適用して解析を行った。
結果として認知負荷の種類によって運転行動が変化することを統計的に示すことができた。 そして、個人ごとの運転行動の変化がそれぞれ別のパラメータに反映されているということを示した。
キ ー ワ ー ド :driving awareness, driving behavior, driving distraction, cognitive distraction,
cognitive task, time series data analysis
occurs when the driver's mind is busy with something not
１ 研究の概要
directly related with driving known as mind-off-road.
Lack of attention or alertness while driving a
Visual distraction can be automatically detected byvehicle is considered to be one of the major reasons of
tracking the driver's eye movement. A general algorithm
road accidents[1][2]. Recent advances of sensor
that considers driver's glance behavior across atechnology helps researchers to model driving behavior
relatively short period, could detect visualfrom various sensor data attached to the car and thedistraction consistently across drivers. Some research
driver [3]. Automatic detection of distracted driving
works in this direction are presented in [8][9]. However,from driving behavior and issuance of alert can help
detecting cognitive distraction is much more complex as
driver to adhere to safe driving. There are various
the signs of cognitive distraction are usually notcauses of distraction ranging from driver's fatigue
straight forward and can vary across drivers. Moreovercausing drowsiness, sudden health related problems to
the driving behavior does not have a simple linear
multitasking with the use of other in-vehicle systems.
relationship with cognitive distraction. Some studies
From various studies, it is known that driving behavior
on cognitive distraction can be found in [10][11][12].
is affected by driver's physical condition as well as
In this work, we restrict our study to the area ofcognitive multitasking [4][5][6]. Researches are going
cognitive distraction. The main objective of this study
on studying for effective detection of driver's
is to investigate the possibilities of effective
distraction from the analysis of driving behavior [7],detection of distracted driving from the deviation of
yet to come up with a successful commercial application.
the driving behavior of the driver, driving with varying
The effect of distraction and cause of distraction
cognitive load. The simulation experiments are done inleading to unsafe driving vary substantially from drivera driving simulator in different scenarios and multiple
to driver depending on driving experience, individual
drivers are asked to drive 1) with attention without any
confidence level, age, mental state etc. Thus asecondary task 2) with various secondary tasks. The
personalized modeling of driving behavior and impact of
sensors' time series data from driving simulator aredistraction on the model are needed to be studied forcollected and analyzed. Statistical tests are done todeveloping on-board safety system.
check whether there is any significant difference
Among varieties of distractions, two major types are
between the driving behavior with and without secondary
visual distraction and cognitive distraction. Visual
cognitive tasks and what feature or which set of sensor
distraction happens when the driver looks away from thedata indicates the most difference during driving withroad described as eye-off-road, cognitive distraction2attention and driving with distraction.
２ 研究の内容
In this study, we have used driving simulator D3Sim.
The driving behavior is assessed from the simulator
output which contains time series data (steering angle,steering torque, accelerator stroke, brake stroke, carspeed, car angle, engine speed etc.). We have usedvarious scenarios for driving and collected simulator
output. The experimental study in detail is as follows:
1) 4 subjects have been used for this study. All ofthem are students in the age group 20-22 yrs.
2) For each subject, driving data for three situations
have been collected: a) normal driving with attentionb) driving while continuing conversation with copassenger c) driving while doing mental arithmetic at
the elementary school level, such as simple addition,subtraction and multiplication.
3) For each situation, different driving scenarios areused for example, simple route, route having curves andsharp bending and routes with multiple diversions.
4) All subjects are initially allowed to practice fora while in different routes.
5) Each subject is then asked to drive following a carspeeding 60km per hour with a more or less constantseparation in the designated routes (from simple tocomplex) consecutively and repeat driving for 5 times.
6) The driving duration in each case was 3 min.
7) The time series output data from the driving
simulator for steering wheel angle, steering torque,accelerator torque, brake stroke, car speed and engine
speed have been recorded.

Fig2.Data for driving with conversation

Fig3.Data for driving with mental arithmetic
TABLEⅠ
Recognized class

TRUE class

NormalWith
cognitive
load

Normal70.20%

29.80%

With
cognitive
load

31.30%

68.70%TABLEⅡ

Fig1.Data for normal driving

3

1

Feature Number Feature Name

2

Steering Angle SA

3

Steering Torque ST

4
5

Accelerator Stroke AS
Brake Stroke BS

6Car Speed CS

7Engine Speed ES

8

Change in Steering Angle D1SA

9

Change in Steering Torque D1ST

1011

Change in Accelerator Stroke D1AS
Change in Brake Stroke D1BS

12

Change in Car Speed D1CS

13

Change in Engine Speed D1ES

14

Change of Change in Steering Angle D2SA

15

Change of Change in Steering Torque D2ST

1617

Change of Change in Accelerator Stroke D2AS
Change of Change in Brake Stroke D2BS

18

Change of Change in Car Speed D2CS

19

Change of Change in Engine Speed D2ES

the data of driving.
Using the best features from the statistical analysis,
SVM is used to classify two classes of driving. TableI represents the results for the best values obtained.
We have tried nearest neighbor classifier (1NN) also but
we could achieve the average accuracy of classificationas 69%.

３ これまで得られた研究の成果
In this study we selected 150 driving samples for eachperson, normal driving 60 samples, driving withconversation 45 samples and driving with mentalarithmetic 45 samples. For each sample, 6 time series(steering wheel angle SA, steering torque ST,
accelerator torque AT, brake stroke BS, car speed CS andengine speed ES) for 3 minutes are obtained. The data
is first preprocessed by using moving average filter andthen normalized. The original 6 dimensional time series
is extended to 18 dimensions to include first derivative
and the second derivative for finding out the best
feature subset for individual driver for detection ofdistraction.
Figure 1, Figure 2 and Figure 3 represent the time series
data from driving simulator for different time series
for normal driving and driving with cognitive tasks. The
horizontal axis represents time in secs. It can be foundfrom visual inspection of the data that steering angle
and steering torque show difference in case of drivingwith or without cognitive load. Moreover it is found thatthe difference is larger for driving with conversation
than driving with simple mental arithmetic.
For initial classification of the time series data inthree classes (normal and two types of cognitive loads),
the features used from each time series data are maximum
value Mk, variance 2 k and average value k as in thefollowing :
||
==

1

=|
1

|

−|

A. Analysis for Best Feature Subset
In this analysis we used the extended feature set andfeature selection algorithms are used to find out thebest feature subset for identifying three classes ofdriving. Table II represents the different dimensionsof the time series data collected from driving simulator
which are considered to be the features of the driving
behavior characteristics. One objective of this study
is to find the most important feature subset forindividual driver responsible for efficient automatic
detection of distraction.
For feature subset selection, Sequential Forward
Selection (SFS) and Sequential Backward Selection (SBS)
algorithms are used with a wrapper I NN (NearestNeighbor) classifier with Dynamic Time Warping (DTW) asthe distance measure. Table III represents the highest
average classification accuracy of three classes
driving (normal, with talking and with mentalarithmetic) for individual subject with the best feature
subset selected by SFS algorithm. Table IV represents
the classification accuracy with the best feature subset
selected by SBS algorithm. The feature number is
described in Table II. It seems that the best feature
subset came out to be different for different suboptimalfeature selection algorithm. Table V represents theresults of another feature selection algorithm CWC
developed in [20]. The best feature subset in this casecame out to be poor than the other algorithms according
to average classification accuracy. Using the best
feature subset for individual driver, the averageclassification accuracy came out to be 77%.

|TABLE III
CLASSIFICATION ACCURACY WITH FEATURE SUBSET SELECTION
BY SFS

where yk(t); k(= 1; 2;; 6) is the time series datafor kth series, k representing each of the 6 time series
data collected from driving simulator. Nk, is the numberof time intervals from beginning to end of the driving.
Now for every feature and for every series, statisticalsignificance is tested for confirming significant
difference between normal and distracted driving. 1NNclassifier and SVM with RBF kernel is used to classify

4

User

Selected Feature Subset

Classification
accuracy

1(3, 6, 9, 8)

0.90%

2

(11,5,1,17,4)

0.80%

3

(11,4,7,5)

0.64%

4(9,1)

0.67%

Statistical Analysis, 2009.
[3] N. Li, J.J. Jain and C. Busso, “ Modeling of Driver
Behavior in Real World Scenarios using Multiple
Noninvasive Sensors”, IEEE Trans. Multimedia, Vol. 15,Issue 5, pp. 1213–1225, 2013.
[4] D. D. Salvucci, “Modeling Driver Distraction fromCognitive Tasks”,Proc. 24th Annual Conference of theCognitive Science Society, pp. 792–797, 2002.
[5] L. Jin et al., “Driver Cognitive Distraction
Detection Using Driving Performance Measures”,
Discrete Dynamics in Nature and Society, Vol.2012,Article ID 432634, 2012.
[6] H. B. Kang, “Various Approaches for Driver andDriving Behavior Monitoring: A Review”, Proc. IEEE
International Conference on Computer Vision Workshops
ICCVW, 2013 pp. 616–623, 2013.
[7] Y. Dong, Z. Hu, K. Uchimura and N. Murayama, “DriverInattention Monitoring System for Intelligent Vehicles:
A Review”, Proc. of IEEE Trans. on Intelligent
Transportation Systems, Vol. 12, No. 2, pp.596–614, June
2011.
[8] Y. Liang, J. D. Lee and L. Yekhshatyan, “’How
dangerous is looking away from the road? Algorithms
predict crash risk from glance patterns in naturalisticdriving”, Human Factors, Vol. 54, No. 6, pp.
1104-1116,2012.
[9] A. Ragab, C. Craye, M. S. Kamel and F. Karray, “ A
Visual based Driver Distraction Recognition andDetection Using Random Forest”, Image Analysis andRecognition, Vol 8814, Lecture Notes in Computer
Science,pp. 256–265, 2014.
[10] Y. Liang and J. D. Lee, “A hybrid Bayesian Network
approach to detect driver cognitive distraction”,Transportation Research Part C, Vol. 38, pp.146-155,
2014.
[11] T. Ershal, H. Fuller et.al, “Model based analysis
and classification of driver distraction under
secondary task”, IEEE Trans. on Intelligent Transport
Systems, Vol.11, No.3, pp.692-701, 2010.
[12] M. Kutila, et al, “Driver distraction detection
with a camera vision system”, Proc. of IEEE
International Conference on Image Processing ICIP 2007,
Vol.6, pp. 201-204, 2007.

TABLE IVCLASSIFICATION ACCURACY WITH FEATURE SUBSET SELECTION
BY SBSClassification
User Selected Feature Subset
accuracy1(8,13,14,15,17,18)

0.93%

2(5,17)0.64%

3(4,13,14,18)

0.64%

4(3,5,13,15)

0.71%

TABLE V
CLASSIFICATION ACCURACY WITH FEATURE SUBSET SELECTIONBY CWCClassification
User Selected Feature Subset
accuracy
1(7, 9 ,1 ,4, 6)

0.73%

2
3

(6, 5, 11)(2, 5, 4, 1)

0.64%
0.71%

4(3, 11)

0.57%

４ 今後の具体的な展開
For future study, we need to integrate other factorsinfluencing cognitive distraction and also use someother sensors to detect cognitive distraction for more
concrete results and increased classification accuracy
for normal and distracted driving. Also it is found thatdriving experience has an effect on change of driving
behavior with cognitive load. Thus it is worth to study
cognitive distraction for modeling personal driving
behavior.
５ 論文・学会発表等の実績
Basabi Chakraborty, Kotaro Nakano, “AutomaticDetection of Driver’s Awareness with Cognitive Task
from Driving Behavior”2016 IEEE INTERNATIONALCONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS OCTBER 9-12
で発表予定
６ 受賞・特許なし
７ 参考文献[1]S. G. Klauer, et al.,“The impact of driver
inattention on near-crash/crash risk: An analysis using
the 100-car naturalistic driving study data”,
Tech.Rep., 2006.
[2] D. Ascone, T. Lindsay and C. Varghese, “Traffic
Safety factor: An Examination of Driver Distraction as
Recorded in NHTSA Databases”,National Highway Traffic
Safety Administration’s National Center forThis article is taken from the research paper mentionedin section 55