participated in naturalistic drive training data collection. This paper focuses on analysis of novice driver training signals from an audio processing perspective.
2015 IEEE Intelligent Vehicles Symposium (IV) June 28 - July 1, 2015. COEX, Seoul, Korea
In-vehicle Speech Recognition and Tutorial Keywords Spotting for Yang Zheng, Xian Shi, Amardeep Sathyanarayana, Navid Shokouhi, John H.L. Hansen, Fellow, IEEE licensed drivers. In-vehicle tutorial conversation takes place throughout the drive training process, which includes general guidelines, questions-and-answer, and specific route instructions. From this dialog, it is useful to track novice driver6B 5(63216( :,7+ 5(63(&7 72 7+(,5 787256B ,16758&7,216 including eye movements, control of steering wheel, gas/brake pedal use, instrument panel operation, and so on.
Abstract Novice young drivers are more frequently involved in traffic accidents, and studies have shown that effective supervised driver training is the key in reducing young
Mobile-UTDrive in-vehicle data acquisition platform, two 16-age novice drivers participated in naturalistic drive training data collection. This paper focuses on analysis of novice driver training signals from an audio processing perspective. Specifically, analysis of supervised driver instruction audio and resulting CAN-Bus maneuver operation is performed. Following a procedure which consists of noise suppression, speech recognition and keyword spotting, five tutorial keywords Brake, Gas, Left, Right and Stop are spotted at an overall accuracy rate of 40% versus all spontaneous continuous speech. The time stamps of these keywords are then used as indications of driving maneuvers. As examples of driving performance evaluation, the case of making Left-Turn maneuvers for the two novice drivers are assessed and compared, and the increase of driving skills over experiences are analyzed.
Topics relating to young novice drivers, driver education and training has been widely considered in the literature. According to the review literature [6], a large number of studies focused 21 129,&( '5,9(56B 0(17$/ :25./2$' 25 attitudes in psychological ways. Monash University also '(9(/23('7+(,5?,16,*+7A'5,9(5-training program in Australia 72$''5(66129,&('5,9(56B%(+$9,2r, based on feedback from questionnaires [8]. From a more technology approach, Mourant in 1972 published a study on visual behavior of novice drivers [9], which is followed by Yang, who used their virtual reality driving simulator in 2006 to compare novice and experienced drivers in lane-change scenarios [10].
I. INTRODUCTION Novice young drivers are more frequently involved in traffic accidents than adults [1, 2]. According to analysis of statistics from the ! (3$570(17 2) 5$163257$7,21B6 Fatality Analysis Reporting System (FARS), the fatal crash rate per mile in the United States driven for 16-19 year-olds is nearly 3 times the rate for drivers ages 20 and over [3]. An obvious major reason for this is that young drivers are inexperienced, lacking the necessary driving skills and capabilities. One key effort 72+(/35('8&( 7:2 &+,/'5(1B6 %5$.(-and-gas 0$1(89(5617+,6&$6(7$%/(7B69ertical acceleration is used to getting time alignments with tutorial keywords. The positive value of vertical acceleration means the vehicle is accelerating and its negative means the vehicle is '(&(/(5$7,1* +(78725*,9(67+(,16758&7,212)? A$1' 172
110
115
120
125 130 Time (sec) (b) Left Turn - Boy
135
140
145
150
Yaw rate Pitch rate LTR rate tag Roll
0.5
0
-0.5
195
A. Individual Maneuver Analysis In Figure 8 7+( 63277(' ? A &200$1' 3($.6 $5( plotted together with vehicular dynamic signals > yaw rate and pitch rate. The data of yaw rate and yaw rate come from initial gyroscope sensor of portable device. When a tablet is mounted inside a vehicle, its gyroscope directly reflects vehicle dynamics. It is already proved that yaw rate are closely related with steering wheel angle extracted from CAN-Bus [18]. Alternatively, the acceleration in lateral, longitudinal and vertical dimensions of the device can also represent vehicular kinetics in a similar way. Since the front and back side of device is faced with the driver and windshield, the vertical acceleration of device simulates the acceleration in the direction of vehicle forward motion. Therefore, it is proper to use yaw rate representing steering wheel angle, and use pitch rate or vertical acceleration representing gas/brake pedal pressure. These signals directly 5()/(&7'5,9(56B&21752/2)67((5,1*:+((/$1'*$6%5$.(3('$/ when making a left-turn maneuver.
105
200
205
210
215 220 Time (sec)
225
230
235
240
Figure 9. Example of left-turn maneuver of (a) FND and (b) MND Brake
Brake
Gas
Gas
Figure 10. Example of Brake-and-Gas maneuver of (a) FND and (b) MND
These two examples provide an idea to combine invehicle tutorial conversation with vehicular dynamics 72*(7+(5:+,&+0,*+7%(86()8/7281'(567$1'129,&('5,9(56B behavior and help improve their driving skills. B. Driving Experience Analysis The two novice young drivers were asked to participate in the drive training for five times, one or two weeks each. It is interesting to look at how their skills increase over driving experiences. With the distribution parameters of amount (N), mean (E) and variance (D), Table 4 summarizes the maximum vehicle yaw rate when the driver making a left or right turn (left turn results positive yaw rate while right turn makes negative yaw rate). The absolute value of yaw rates can represent how sharply the driver turning the steering wheel angle.
Counting amount
Gaussian PDF
Left-turn pitch rate (a)
Left-turn pitch rate (b)
Left-turn pitch rate (c)
Figure 11. Histogram of left-turn yaw rates distribution on (a) 6/21/2014, (b) 7/26/2014 and (c) 9/1/2014 TABLE IV. Date
[2] Lucidi, F., Giannini, A. M., Sgalla, R., Mallia, L., Devoto, A. and
MAX YAW RATE OF LEFT & RIGHT TURNS IN DRIVING LOG Left-Turn
Right-Turn
N
N
6/21/2014
69
0.3431
0.1358
68
-0.3376
0.1328
6/29/2014
129
0.3394
0.1552
123
-0.3510
0.1601
7/26/2014
170
0.3092
0.1551
176
-0.3083
0.1463
8/3/2014
173
0.2807
0.1411
170
-0.3322
0.1665
8/30/2014
65
0.2649
0.1255
90
-0.2619
0.1125
9/1/2014
38
0.4256
0.1244
43
-0.4079
0.1185
[3] [4] [5]
2/22.$7'5,9(56B29(5$//3(5)250$1&()25($&+75$,1,1* day, Figure 11 plots the histogram of left-turn yaw rates and their Gaussian distribution fitting. By comparing the yaw rates on the three selected date, it can be inferred that these two novice drivers made turns either sharply or softly on their first driving date 06/21/2014, and tended to use more soft turns on their third driving date 07/26/2014, but then turned in the moderate way on 09/01/2014.
[6]
V. CONCLUSION AND FUTURE WORK
[9]
Given the multiple in-vehicle data collected from portable device with MobileUTDrive app, this paper processes the audio signals when novice drivers practicing their drive training. These tutorial conversation are used for commands keyword spotting, and the time alignments of these keywords are used to indicate driving maneuvers. However, the accuracy of keyword spotting is not high due to the practical characteristics of in-vehicle naturalistic conversation, this needs to be improved. Additionally, more applications of driving performance evaluation could be developed, such as individual maneuver analysis and driving experience analysis.
[7] [8]
[10]
[11]
[12]
This paper opens a door of study on novice driver training situations. The further development of this study will focus on the model building of novice driver behavior. More participants will be asked to participate in. We will compare the difference between novice drivers and experienced drivers, the variance of each specific novice driver will also be analyzed.
[13]
[14]
[15]
ACKNOWLEDGMENT
[16]
The authors would like to thank Prof. John Hansen, and his twins, Heather and Christian, for their participation in the data collection of drive training.
[17] [18]
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(,&+0$11 ?Young novice driver subtypes: Relationship to driving violations, errors and lapsesA Accident Analysis and Prevention, 2010 C McCartt, A.T.; Teoh, E.R.; Fields, M.; Braitman, K.A.; and Hellinga, ?Graduated licensing laws and fatal crashes of teenage drivers: a national studyA Traffic Injury Prevention 11:240-48., L.A. 2010. D R Mayhew, H M Simpson?The safety value of driver education and trainingAInjury Prevention 2002, 8(Suppl II): ii3>ii8. Daniel R. Mayhew, Herbert M. Simpson, Allan F. Williams and Susan A. Ferguson?Effectiveness and Role of Driver Education and Training in a Graduated Licensing SystemAJournal of Public Health Policy, Vol. 19, No. 1, 1998, pp. 51-67. Engström, I., Gregersen, N. P., Hernetkoski, K., Keskinen, E. and Nyberg, A. ?Young novice driver education and training, literature review,A VTI-rapport, Vol. 491A. Linköping: Swedish National Road and Transport Research Institute, 2003. Senserrick T.M. and Swinburne G.C., Evaluation of an Insight drivertraining program for young drivers. Victoria, Australia: Monash University Accident Research Centre, 2001. Ronald R. Mourant and Thomas H. Rockwell, ?Strategies of Visual Search by Novice and Experienced DriversAHuman Factors: The Journal of the Human Factors and Ergonomics Society, 1972, 14(4), pp. 325-335. Guihua Yang, Beverly Jaeger, and Ronald R. Mourant?Driving Performance of Novice and Experienced Drivers in Lane-change &(1$5,26AProceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, 2006. A. Sathyanarayana, O. Sadjadi, J.H.L. Hansen, "Automatic Driving Maneuver Recognition and Analysis using Cost Effective Portable Devices," SAE Inter. Journal on Passenger Cars - Electronic & Electrical Systems, 6(2): 467-477, 2013 A. Sathyanarayana, S.O. Sadjadi, J.H.L. Hans(1?(9(5$*,1* (1625 Information From Portable Devices Towards Automatic Driving $1(89(5 (&2*1,7,21A15th IEEE ITSC, Anchorage, AK, USA, Sep. 2012, pp. 660-665. $7+
