Signal Processing - November 2016 - 15

Since 1999, Nagoya University has been collecting on-road
motion, driver behavior, and surrounding environmental facdriving behavior data using instrumented vehicles (Figure 1)
tors during real-world driving. To collect a reasonable amount
[10], [11], which have included video, audio, vehicle sensor,
of realistic driving data for research purposes, many dataand physiological data, from more than 800 drivers using
collection projects have been conducted around the world,
these vehicles.
including naturalistic driving studies (NDSs) and field operaWe have also developed a driving-data acquisition system
tional tests (FOTs) [1]-[9].
using a smartphone and the vehicle's CAN-bus [24]. It transWe have also been conducting on-road driving-data colmits driving data obtained from the CAN-bus via a Bluetooth
lection since 1999 [10]. In addition to our own data-collection
connection to the smartphone mounted
efforts, we have shared international drivOne way to deal with the
on the windshield, and records the driving data in collaboration with researching signals from the CAN-bus along
ers in the United States and Europe [11]
highly variable nature of
with driving data obtained from the
and have applied statistical signal prodriving behavior is to employ
smartphone itself. Although this system
cessing and machine-leaning techniques
a data-centric approach that
cannot record the same range of driving
to the recorded driving data to analyze
models driver behavior using
data with the same quality as the instruand model human driving behavior.
large amounts of driving data
mented vehicles, it allows us to easily
Over the course of our research, we first
collected from numerous drivers collect large amounts of driving data
applied cepstral analysis and Gaussian
mixture models (GMMs) for modeling
in a variety of traffic conditions. from a much wider range of vehicles. So
far, we have recorded driving data from
driver pedal-operation behavior [12].
more than 50 drivers operating different models of vehicles
We extended our GMM-based behavior-modeling framework
using this data acquisition system.
to predict car-following behavior [13]-[15] and to detect risky
driving behavior for a driver self-coaching system [16]. We
then applied HMMs for modeling vehicle trajectories durStatistical analysis and modeling
ing lane-change maneuvers [17], and for integrated modelof driver behavior
ing of driver gaze and vehicle-operation behavior during
Statistical machine-learning techniques have been successlane changes [18]. We also employed a Bayesian network
fully applied to data-driven driver-behavior modeling. For
(BN) to detect driver frustration [19]. Other statistical
example, Sakaguchi et al. modeled driver stopping behavframeworks were also used for detecting potential threats
ior using a BN and successfully applied this model to detect
[20] and driver over-reliance on automated driver-assistance
deviant braking behavior [25]. Oliver et al. presented their
systems [21], [22].
driver-behavior recognition and prediction method based on
dynamic graphical model, HMMs, and their extensions (coupled-HMM) trained using on-road driving data collected from
Collection of on-road driving data
The first step that is necessary to analyze and model driver
behavior is to collect a reasonable amount of realistic driving
data. Table 1 shows examples of several projects around the
Table 1. Examples of on-road driving-data corpora.
world that have collected real-world or naturalistic driving data.
Project
Number of
The 100-car study is a landmark NDS that collected approxi(Period of Data
Drivers or
mately 43,000 hours of driving data from 100 cars and 241 drivCollection)
Area
Type of Vehicles
Vehicles
ers [1]. This data has helped researchers understand the causes
100-Car [1]
United
Passenger cars
241 drivers
of crashes and allowed them to develop effective crash preven(2003-2004)
States
(100 vehicles)
tion measures [2]. The second Strategic Highway Research
United
Passenger cars
3,150 drivers
SHRP2 [3]
Program (SHRP2) also collected a huge amount of driving
(2010-2013)
States
data from more than 3,000 drivers [3]. Satzoda et al. used the
euroFOT [5]
Europe
Passenger cars,
1,100 drivers
SHRP2 data to evaluate a technique for detecting critical events
(2010-2011)
trucks
related to lanes and road boundaries [4]. The European Field
UDRIVE [6]
Europe
Passenger cars,
290 drivers
Operational Test (euroFOT) collected data from more than
(2014-2016)
trucks, powered
1,000 drivers to evaluate ADASs such as adaptive cruise contwo-wheelers
trol and lane departure warning systems [5]. UDRIVE, the first
400-car [7]
Australia
Passenger cars
400 drivers
(2014)
large-scale European NDS project, is also recording naturalistic driving data [6]. In addition, there are other research projects
Japan
Taxis
200 taxis
JSAE/TUAT [8]
(2005-present)
collecting large-scale corpora of real-world or naturalistic driving data [7]-[11]. Some of these data sets are publicly available
HQL [9], [25]
Japan
Instrumented
97 drivers
(2001-2003)
vehicles
for a fee, e.g., [8] and [9]. An EU-research project, FOT-Net
Data [23], assists researchers in sharing data sets by providing
NUDrive
Japan
Instrumented
800 drivers
[10], [11]
vehicles
information of available data sets and organizing meetings on
(1999-2010)
sharing and reuse of FOT/NDS data.
IEEE SIgnal ProcESSIng MagazInE

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November 2016

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Table of Contents for the Digital Edition of Signal Processing - November 2016

Signal Processing - November 2016 - Cover1
Signal Processing - November 2016 - Cover2
Signal Processing - November 2016 - 1
Signal Processing - November 2016 - 2
Signal Processing - November 2016 - 3
Signal Processing - November 2016 - 4
Signal Processing - November 2016 - 5
Signal Processing - November 2016 - 6
Signal Processing - November 2016 - 7
Signal Processing - November 2016 - 8
Signal Processing - November 2016 - 9
Signal Processing - November 2016 - 10
Signal Processing - November 2016 - 11
Signal Processing - November 2016 - 12
Signal Processing - November 2016 - 13
Signal Processing - November 2016 - 14
Signal Processing - November 2016 - 15
Signal Processing - November 2016 - 16
Signal Processing - November 2016 - 17
Signal Processing - November 2016 - 18
Signal Processing - November 2016 - 19
Signal Processing - November 2016 - 20
Signal Processing - November 2016 - 21
Signal Processing - November 2016 - 22
Signal Processing - November 2016 - 23
Signal Processing - November 2016 - 24
Signal Processing - November 2016 - 25
Signal Processing - November 2016 - 26
Signal Processing - November 2016 - 27
Signal Processing - November 2016 - 28
Signal Processing - November 2016 - 29
Signal Processing - November 2016 - 30
Signal Processing - November 2016 - 31
Signal Processing - November 2016 - 32
Signal Processing - November 2016 - 33
Signal Processing - November 2016 - 34
Signal Processing - November 2016 - 35
Signal Processing - November 2016 - 36
Signal Processing - November 2016 - 37
Signal Processing - November 2016 - 38
Signal Processing - November 2016 - 39
Signal Processing - November 2016 - 40
Signal Processing - November 2016 - 41
Signal Processing - November 2016 - 42
Signal Processing - November 2016 - 43
Signal Processing - November 2016 - 44
Signal Processing - November 2016 - 45
Signal Processing - November 2016 - 46
Signal Processing - November 2016 - 47
Signal Processing - November 2016 - 48
Signal Processing - November 2016 - 49
Signal Processing - November 2016 - 50
Signal Processing - November 2016 - 51
Signal Processing - November 2016 - 52
Signal Processing - November 2016 - 53
Signal Processing - November 2016 - 54
Signal Processing - November 2016 - 55
Signal Processing - November 2016 - 56
Signal Processing - November 2016 - 57
Signal Processing - November 2016 - 58
Signal Processing - November 2016 - 59
Signal Processing - November 2016 - 60
Signal Processing - November 2016 - 61
Signal Processing - November 2016 - 62
Signal Processing - November 2016 - 63
Signal Processing - November 2016 - 64
Signal Processing - November 2016 - 65
Signal Processing - November 2016 - 66
Signal Processing - November 2016 - 67
Signal Processing - November 2016 - 68
Signal Processing - November 2016 - 69
Signal Processing - November 2016 - 70
Signal Processing - November 2016 - 71
Signal Processing - November 2016 - 72
Signal Processing - November 2016 - 73
Signal Processing - November 2016 - 74
Signal Processing - November 2016 - 75
Signal Processing - November 2016 - 76
Signal Processing - November 2016 - 77
Signal Processing - November 2016 - 78
Signal Processing - November 2016 - 79
Signal Processing - November 2016 - 80
Signal Processing - November 2016 - 81
Signal Processing - November 2016 - 82
Signal Processing - November 2016 - 83
Signal Processing - November 2016 - 84
Signal Processing - November 2016 - 85
Signal Processing - November 2016 - 86
Signal Processing - November 2016 - 87
Signal Processing - November 2016 - 88
Signal Processing - November 2016 - 89
Signal Processing - November 2016 - 90
Signal Processing - November 2016 - 91
Signal Processing - November 2016 - 92
Signal Processing - November 2016 - 93
Signal Processing - November 2016 - 94
Signal Processing - November 2016 - 95
Signal Processing - November 2016 - 96
Signal Processing - November 2016 - 97
Signal Processing - November 2016 - 98
Signal Processing - November 2016 - 99
Signal Processing - November 2016 - 100
Signal Processing - November 2016 - 101
Signal Processing - November 2016 - 102
Signal Processing - November 2016 - 103
Signal Processing - November 2016 - 104
Signal Processing - November 2016 - 105
Signal Processing - November 2016 - 106
Signal Processing - November 2016 - 107
Signal Processing - November 2016 - 108
Signal Processing - November 2016 - 109
Signal Processing - November 2016 - 110
Signal Processing - November 2016 - 111
Signal Processing - November 2016 - 112
Signal Processing - November 2016 - 113
Signal Processing - November 2016 - 114
Signal Processing - November 2016 - 115
Signal Processing - November 2016 - 116
Signal Processing - November 2016 - 117
Signal Processing - November 2016 - 118
Signal Processing - November 2016 - 119
Signal Processing - November 2016 - 120
Signal Processing - November 2016 - 121
Signal Processing - November 2016 - 122
Signal Processing - November 2016 - 123
Signal Processing - November 2016 - 124
Signal Processing - November 2016 - 125
Signal Processing - November 2016 - 126
Signal Processing - November 2016 - 127
Signal Processing - November 2016 - 128
Signal Processing - November 2016 - 129
Signal Processing - November 2016 - 130
Signal Processing - November 2016 - 131
Signal Processing - November 2016 - 132
Signal Processing - November 2016 - 133
Signal Processing - November 2016 - 134
Signal Processing - November 2016 - 135
Signal Processing - November 2016 - 136
Signal Processing - November 2016 - 137
Signal Processing - November 2016 - 138
Signal Processing - November 2016 - 139
Signal Processing - November 2016 - 140
Signal Processing - November 2016 - 141
Signal Processing - November 2016 - 142
Signal Processing - November 2016 - 143
Signal Processing - November 2016 - 144
Signal Processing - November 2016 - 145
Signal Processing - November 2016 - 146
Signal Processing - November 2016 - 147
Signal Processing - November 2016 - 148
Signal Processing - November 2016 - Cover3
Signal Processing - November 2016 - Cover4
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