This is a slide for the invited talk at The 4th Workshop on Naturalistic Driving Data Analytics, IEEE IV2017, Los Angeles, 11th June, 2017. This talk summarizes a series of work on a symbolization approach toward naturalistic driving behavior data. Most of the works are conducted by collaboration between DESNO co. and Ritsumeikan university

1. Semantic Segmentation of Driving Behavior Data:
Double Articulation Analyzer and its Application
Tadahiro Taniguchi
College of Information Science & Engineering
Ritsumeikan University
Invited talk at
The 4th Workshop on Naturalistic Driving Data Analytics,
IEEE IV2017, Los Angeles, 11th June, 2017
@tanichu
Machine learning methods for unlabeled naturalistic driving data
Symbolization approach for driving behavior data:

2. Tadahiro Taniguchi @tanichu
• Professor, Emergent System Laboratory,
College of Information Science and Engineering,
Ritsumeikan University, Japan
– 2003-2006: PhD student, Kyoto University
– 2005-2008: JSPS research fellow, Kyoto University
– 2008: Assistant professor, Ritsumeikan University
– 2010: Associate professor, Ritsumeikan University
– 2015-2016: Visiting Associate Professor,
Imperial College London
– 2017: Professor, Ritsumeikan University
– 2017: Visiting General Chief Scientist,
Panasonic Corporation
AI solution center (20% C.A.)
• Research Topics
– Machine learning, Intelligent robotics & vehicle,
Symbol emergence in robotics, Language acquisition

3. Contents
1. Overview of Double Articulation
Analysis of Driving Behavior
2. Applications
 Segmentation and topic modeling
 Prediction of driving behavior
 Large-scale data
3. Deep learning for driving behavior
feature extraction

4. Towards Machine learning-based
Naturalistic driving behavior data analysis
E,g, Nagoya database [Takeda+]
 Because of the massive size and huge diversity of NDD,
hang-crafted and rule-based analysis are not scalable.
 How can we segment driving behavior data and find
semantic units from NDD?
Machine learning-based Semantic Segmentation
Cloud storage/
Internet
without labeling

5. Driving behavior data as
unlabeled multi-dimensional timeseries data
Preprocessed time series data
Time-series data from each sensor

6. Research goal: Extraction of latent states
from unlabeled naturalistic driving data
How can we construct this kind of latent finite state
machine (FSM) from unlabeled naturalistic driving data?
 We can consider naturalistic driving data has
latent (in)finite states.
Perception
Prediction

7. How the recorded naturalistic driving
behavior data are generated??
7
Vehicle dynamics and behavior
Perception
Decision
Maneuver
Environment
Intention
 Driving behavior data include velocity, break
pressure, steering angle and so on. (Note that we
are excluding front-view camera image.)
 They are influenced by driver’s intention and
environmental conditions.
Latent variable
Driving data conditionally depend on driver’s intention and
environmental conditions

8. Discovering latent dynamics of driver’s
intention from observed driving behavior data
8
Vehicle dynamics and behavior
Perception
Decision
Maneuver
Environment
Intention
We have been focusing on the analysis of driving
behavior obtained from CAN to estimate latent
dynamics of drivers’ intention and environment.
Information found here
implicitly involves information
related to environment and
intention.
CAN

9. Working hypothesis:
double articulation structure on
naturalistic driving behavior data
9
5 2 1691 7 2
dw 4 dw 10 dw 1Driving words
Driving letters
Driving behavior
data
Latent variable
representing intention
zt-1 zt zt+1
CAN information
Vehicle dynamics and behavior
Perception
Decision
Maneuver
Environment
Intention

10. Double articulation structure in semiotic data
• Semiotic time-series data often has double
articulation
– Speech signal is a continuous and high-dimensional time-series.
– Spoken sentence is considered as a sequence of phonemes.
– The phonemes are grouped into words, and people give them
meanings.
h a u m ʌ́ tʃ I z ð í s
[h a u ] [m ʌ́ tʃ] [ i z ] [ð í s]
How much is this?Word
Phoneme
Speech
signal
semantic
(meaningful)
meaningless
unsegmented
Does the human brain have a special capability to analyze
double articulation structures embedded in time-series data?

11. 1 2 46 1 27 8 5 10 11 13 14 7
W H A T I S T H I S T H I S I S A P E N
[WHAT] [IS] [THIS] [THIS] [IS] [A] [PEN]
Speech
Motion
Driving
Working hypothesis
Double Articulation Structure in Human Behavior
2017/6/12

12. Basic assumption
”Driving-behavior data has two-layered hierarchical structure.”
 Ex.) “Turning right in an intersection“ is not a simple “rotating a
steering wheel” maneuver, but a complex sequence of maneuvers.
Double articulation structure
Chunk: (a sequence of segments)
Semantically consistent driving behavior unit
Segment:
Physically consistent driving behavior unit
Driving words
Driving letters

13. Analogy between speech signal
and driving behavior data
13
Speech signal Features Word
sequence
Driving behavior dataSpeech data
今⽇は楽し
かった．
・・・・
Driving behavior
h a zIʌu m tʃ
How much isWord
Phoneme 5 2 1691 7 2
dw 4 dw 10 dw 1D. word
D. letter
Speech
data
D.B.
data
Semantic unit
Speech recognition DAA
Extracting driving words as high-level semantic representations that are
representing the driver’s intention and situation concisely
Features Driving word
sequence

14. Nonparametric Bayesian approach
towards finding driving letters and words
• Challenges
– How can a system find the number of driving words
and letters from data?
– How can a system estimate features and
characteristics (emission distribution) of driving
letters?
– How can a system find a list of driving words (i.e,
dictionary)?
– How can a system determine the number of letters
contained in each driving word?
Nonparametric Bayesian approach
in a data-driven manner??

15. Bayesian nonparametrics
(Nonparametric Bayesian approach)
 By assuming infinite dimensional categorical distribution (i.e.,
infinite number of clusters), we can develop a clustering
method that can automatically estimate the number of clusters.
 It can easily deal with “unseen” possible events (driving
behaviors).
 It is useful for modeling data-driving concept formation, motion
segmentation and word segmentation.
K-mixture model
(GMM and etc. etc.)
SBP model
(DPGMM and etc. etc.)
Finite
(means fixed)
Infinite
(means flexible)
(Ordinal) Bayesian model Nonparametric Bayesian model
Dirichlet distribution Dirichlet process

16. Fully unsupervised machine learning method
Time series
data
Bayesian double articulation analyzer
[Taniguchi’11]
Unsupervised word
segmentation (NPYLM)
[Mochihashi ‘09]
Driving
word
sticky HDP-HMM
[Fox ‘08]
Driving
letter
Double articulation
Analysis (segmentation)
Language model
16
Chunk
Segment
Tadahiro Taniguchi, Shogo Nagasaka
Double Articulation Analyzer for Unsegmented Human Motion using Pitman-Yor Language model and Infinite
Hidden Markov Model, IEEE/SICE International Symposium on System Integration, pp. 250 - 255 .(2011)

17. Finding driving letters from NDD:
Sticky hierarchical Dirichlet process-hidden
Markov model (Sticky HDP-HMM)
 HDP-HMM is an HMM that has an infinite number of hidden
states. That can automatically segment continuous time
series data and find the number of clusters (emission
distributions) simultaneously [The+ ’06, Fox+ ‘08]
 (Sticky) HDP-HMM is shown to be a subclass of HDP-HSMM
[Johnson+ ].
β
z1
γ
λ θk
∞
πkα
y1
z2
y2
z3
y3
zT
yT
κ
https://github.com/mattjj/pyhsmm
Fox, Emily B., et al. "An HDP-HMM for systems with state
persistence." Proceedings of the 25th international
conference on Machine learning. ACM, 2008.
Johnson, Matthew J., and Alan S. Willsky. "Bayesian
nonparametric hidden semi-Markov models." Journal of
Machine Learning Research 14.Feb (2013): 673-701.

18. Fully unsupervised machine learning method
Time series
data
Bayesian double articulation analyzer
[Taniguchi’11]
Unsupervised word
segmentation (NPYLM)
[Mochihashi ‘09]
Driving
word
sticky HDP-HMM
[Fox ‘08]
Driving
letter
Double articulation
Analysis (segmentation)
Language model
18
Chunk
Segment
Tadahiro Taniguchi, Shogo Nagasaka
Double Articulation Analyzer for Unsegmented Human Motion using Pitman-Yor Language model and Infinite
Hidden Markov Model, IEEE/SICE International Symposium on System Integration, pp. 250 - 255 .(2011)

19. Unsupervised word segmentation
 Supervised learning-based word segmentation
 Morphological analysis methods in NLP.
 “これはりんごです．” -> “これ|は|りんご|です． ”
=> This is an apple. (Kore wa ringo desu)
 Unsupervised word segmentation
 No preexisting dictionaries are used.
 A nonparametric Bayesian framework for word segmentation
[Goldwater+ 09]
 Unsupervised word segmentation method based on the Nested
Pitman–Yor language model (NPYLM) [Mochihashi+ 09].
S. Goldwater, T. L. Griffiths, and M. Johnson, “A Bayesian framework for word segmentation: exploring the
effects of context.,” Cognition, vol. 112, no. 1, pp. 21–54, 2009.
Daichi Mochihashi, Takeshi Yamada, Naonori Ueda."Bayesian Unsupervised Word Segmentation with Nested
Pitman-Yor Language Modeling". ACL-IJCNLP 2009, pp.100-108, 2009.
Language model
(Vocabulary)
Word segmentation
Updating language model

20. 2017/6/12 20
From Mochihashi’s presentation slide: http://chasen.org/~daiti-m/paper/jfssa2009segment.pdf
Analysis
Fully unsupervised (data-driven) word segmentation
based on Bayesian nonparametrics

21. Double Articulation Analyzer (DAA)
(Conventional DAA)
• Inference
– Approximate Inference
Procedure of
Double Articulation
Analyzer [Taniguchi ‘11]
• Unsupervised learning
– Estimating
• Language model
• Emission distribution
• Segments and chunks
• Conditions
– Unknown number of
words and letters
– Unknown emission
distribution parameters
Nonparametric Bayesian approach
sticky HDP-HMM
[Fox ‘07]
NPYLM
[Moachihashi ‘09]
Tadahiro Taniguchi, Shogo Nagasaka, Double Articulation Analyzer for Unsegmented Human Motion using
Pitman-Yor Language model and Infinite Hidden Markov Model, 2011 IEEE/SICE SII.(2011)
Driving words
Driving letters
Observation

22. Contents
1. Overview of Double Articulation
Analysis of Driving Behavior
2. Applications
 Segmentation and topic modeling
 Prediction of driving behavior
 Large-scale data
3. Deep learning for driving behavior
feature extraction

23. Contextual Scene Segmentation of Driving Behavior based
on Double Articulation Analyzer [Takenaka ‘12]
Kazuhito Takenaka, Takashi Bando, Shogo Nagasaka, Tadahiro Taniguchi, Kentarou Hitomi, Contextual Scene
Segmentation of Driving Behavior based on Double Articulation Analyzer, IEEE/RSJ International Conference on
Intelligent Robots and Systems 2012 (IROS 2012), 4847-4852 .(2012)
 We applied DAA to driving behavior data and showed that it
could determine change points of driving context recognized
by human.

24. Drive Video Summarization based on Double
Articulation Structure of Driving Behavior [Takenaka ‘12]
Kazuhito Takenaka, Takashi Bando, Shogo Nagasaka, Tadahiro Taniguchi, "Drive
Video Summarization based on Double Articulation Structure of Driving Behavior",
ACM multim media 2012, http://www.youtube.com/watch?v=knwiO6dVbnY
 We developed a drive video summarization method
using DAA, and showed it can summarize drive video
naturally for viewers.

25. Unsupervised drive topic finding from
driving behavioral data [Bando ‘13]
 The DAA could segment driving-behavior data into favorably
organized chunks from the viewpoint of topic modeling.
Takashi Bando, Kazuhito Takenaka, Shogo Nagasaka, Tadahiro Taniguchi,
Unsupervised drive topic finding from driving behavioral data, 2013 IEEE Intelligent
Vehicles Symposium,（2013) IEEE-IV’13 Best poster paper award 1st prize

26. Generating Contextual Description from Driving
Behavioral Data [Bando+ ‘13, ‘14]
 We developed a method that can generate annotation automatically for
driving behavior data [Bando ’13b].
 We developed a method that can generate a contextual description of a
whole trip using DAA , drive topic model, and Google map API [Bando ‘14].
Takashi Bando, Kazuhito Takenaka, Shogo Nagasaka, Tadahiro Taniguchi, Drive annotation via multimodal
latent topic model, IEEE/RSJ International Conference on Intelligent Robots and Systems .(2013)
Takashi Bando, Kazuhito Takenaka, Shogo Nagasaka, Tadahiro Taniguchi, Generating Contextual Description
from Driving Behavioral Data, 2014 IEEE Intelligent Vehicles Symposium (IV'14), .(2014)
DAA
Topic model
Generating
annotation
26

27. Automatic Generation of Summarized Driving
Video with Music and Captions [Takenaka+ ‘15 ]
Kazuhito Takenaka, Takashi Bando, Tadahiro Taniguchi
Automatic Generation of Summarized Driving Video with Music and Captions
41th Annual Conference of the IEEE Industrial Electronics Society (IECON), .(2015)

28. Contents
1. Overview of Double Articulation
Analysis of Driving Behavior
2. Applications
 Segmentation and topic modeling
 Prediction of driving behavior
 Large-scale data
3. Deep learning for driving behavior
feature extraction

29. Driver Assistant System
that understand a driver’s intention
“What kind of driving situation is this? What and when will the driver do next?”
Semantic prediction is required for the long-term prediction of driving behavior
Are you going to park
the car soon? Shall I
start self-parking
system?
“What is the driver doing now?”,
“When will the current driving
behavior finish?”
“What will the driver do next?”
“What is the driver doing now?”,
“When will the current driving
behavior finish?”
“What will the driver do next?”

30. Semiotic Prediction of Driving Behavior using
Unsupervised Double Articulation Analyzer
 A prediction method that can predict successive
sequences of driving letters is developed by extending
the DAA.
 It exploits knowledge of driving words
for predicting future driving behavior
 Tadahiro Taniguchi, Shogo Nagasaka, Kentarou Hitomi, Naiwala P. Chandrasiri, and Takashi Bando, Semiotic Prediction of
Driving Behavior using Unsupervised Double Articulation Analyzer, 2012 IEEE Intelligent Vehicles Symposium (IV2012), 849 -
854 .(2012)
 Tadahiro Taniguchi, Shogo Nagasaka, Kentarou Hitomi, Naiwala P. Chandrasiri, Takashi Bando and Kazuhito Takenaka,
Sequence Prediction of Driving Behaviour Using Double Articulation Analyzer, IEEE Transactions on Systems, Man, and
Cybernetics: Systems, Vol.46 (9), 1300-1313,(2015) doi:10.1109/TSMC.2015.2465933
Averaged number of correctly predicted
Histogram

31. “What is the driver doing now?”,
“When will the current driving
behavior finish?”
“What will the driver do next?”
Hypothetical scenario of target application
Next Contextual Changing Point (NCCP)

32. Prediction of Next Contextual Changing Point of Driving Behavior
Using Unsupervised Bayesian Double Articulation Analyzer
[Nagasaka ‘14, Taniguchi ‘14]
 We proposed a prediction method that can determine the next contextual
change point, i.e., the termination time of the current driving word, using
fully nonparametric Bayesian framework using the HDP-HSMM and NPYLM.
32
When will the driver
change his behavior?
 S. Nagasaka, T. Taniguchi, K. Hitomi, K. Takenaka and T. Bando, “Prediction of Next Contextual Changing Point of Driving
Behavior Using Unsupervised Bayesian Double Articulation Analyzer”, IEEE Intelligent Vehicles Symposium. (2014) (oral).
 Tadahiro Taniguchi, Shogo Nagasaka, Kentaro Hitomi, Kazuhito Takenaka, and Takashi Bando, Unsupervised Hierarchical
Modeling of Driving Behavior and Prediction of Contextual Changing Points, IEEE Transactions on Intelligent Transportation
Systems, Vol.16 (4), 1746-1760 .(2014)

33. Predicted probability duration
distribution over next
contextual change point
Proposed method
Linear regression
RNN
 Almost constant duration
distribution was output
 Predicted duration distribution was
dynamically changed on the basis of
the driving context.
Front camera view
Predicted duration distribution

34. Observationtime
Timeline for predicted NCCP
Proposed method
Linear regression
RNN
True termination time of chunks*
 The proposed method predicted the NCCP
of driving behavioral data in a real
environment more accurately than the
compared (supervised learning )methods.
Predicted probability distribution

35. Determining Utterance Timing of a Driving Agent
with Double Articulation Analyzer
[Taniguchi ‘15]
 Is “avoiding the contextual
change points” a good strategy
for determining utterance timing
of a driving agent??
-> supported
better 
Determining Utterance Timing of a Driving Agent with Double Articulation Analyzer,
Tadahiro Taniguchi, Kai Furusawa, Hailong Liu, Yusuke Tanaka, Kazuhito Takenaka, and Takashi Bando, IEEE
Transactions on Intelligent Transportation Systems (2015)
me
me

36. Contents
1. Overview of Double Articulation
Analysis of Driving Behavior
2. Applications
 Segmentation and topic modeling
 Prediction of driving behavior
 Large-scale data
3. Deep learning for driving behavior
feature extraction

37. Application to
a large-scale driving corpus
 DAA was applied to NUDrive (Nagoya
U. database [Takeda+])
 Possibility of application of DAA to
large-scale driving corpus (NDD) was
explored.
Takashi BANDO, Kazuhito TAKENAKA, Masataka MORI, Tadahiro TANIGUCHI, Chiyomi MIYAJIMA, and Kazuya
TAKEDA, Symbolization approach for large-scale driving corpus, IBIS symposium (in Japanese) (2014)
DriverandEnvironmentID Driving word
Bi-clustering result using IRM
The number of
driving words
Frequency of driving words

38. Lane Change Extraction
Masataka Mori, Kazuhito Takenaka, Takashi Bando, Tadahiro Taniguchi, Chiyomi Miyajima, and Kazuya
Takeda, Automatic Lane Change Extraction based on Temporal Patterns of Symbolized Driving Behavioral
Data, 2015 IEEE Intelligent Vehicles Symposium (IV'15), .(2015)
Using segment and
topic information

39. Integrating driving behavior and traffic context
through signal symbolization [Yamazaki+ 16]
 Risk level of lane
change is predicted by
using driving words
and driving topics.
 Co-occurrence chunks
are newly introduced.
Yamazaki, Suguru, et al. "Integrating driving behavior and traffic context through
signal symbolization." Intelligent Vehicles Symposium (IV), (2016).

40. Driving Word2vec:
Distributed Semantic Vector Representation for
Symbolized Naturalistic Driving Data [Fuchida+ 16]
Natural langauge Driving behavior
 Driving behavior has a certain
syntactic structure???
 Can Word2Vec extract
semantic similarity between
words from large-scale NDD
corpus?????
Mikolov, T., Corrado, G., Chen, K., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. Proceedings of
the International Conference on Learning Representations (ICLR 2013)
Yusuke Fuchida, Tadahiro Taniguchi, Toshiaki Takano, Takuma Mori, Kazuhito Takenaka, Takashi Bando, Driving Word2vec:
Distributed Semantic Vector Representation for Symbolized Naturalistic Driving Data, IEEE Intelligent Vehicles Symposium (IV), .(2016
 Natural language has a certain
syntactic structure.
 Word2Vec can extract
semantic similarity and
relationships between words
from large-scale corpus.

41. We evaluate similarity between DW2V and Drive Topic.
Drive Topics
DW2V

42. Similarity between DW2V and Drive topics
 A driving word that is most similar to a
driving word in a sense of DW2V was near to
the word in a sense of Drive Topic.
Random sampling
Temporally neighbor
Significant correlation was found between distances
between two data points in DW2V and Drive Topic
We concluded that the hypothesis was supported
by the experimental result of DW2V

43. Future applications and
research topics
Symbolized
NDD
NDD
DW2V
Drive
topics
Double articulation analysis
Video summarization
Scene retrieval
Prediction of
driving behavior
Anomaly detection
Automatic description
generation
Statistical analysis of
a variety of drivers

44. Update in algorithm for double articulation analyzer
Nonparametric Bayesian DAA (NPB-DAA) [Taniguch+ 16]
(1) Conventional DAA
(2) Nonparametric Bayesian DAA
(NPB-DAA)
Tadahiro Taniguchi, Shogo Nagasaka, Ryo Nakashima, Nonparametric Bayesian Double Articulation Analyzer for Direct Language
Acquisition from Continuous Speech Signals, IEEE Transactions on Cognitive and Developmental Systems.(2016)
Hierarchical Dirichlet process hidden
language model (HDP-HLM) [Taniguchi+ 16]
γLM
Language model
(Word bigram)
γWM
i=1,…,∞
αWM
j=1,…,∞
Word model
(Letter bigram)
z1 zs-1 zs zs+1 zS
Latent words
(Super state sequence)
wi
i=1,…,∞
ls1 lsk lsL
Latent letters
Ds1 Dsk
x1
xt1
s1 xT
Acoustic model
ωj
θj
G
H
yT
Observation
Ds1 Dsk DsL
Duration
βLM
αLM
πLM
i
βWM
πWM
j
xt
2
s1 xt1
sk
xt
2
sk xt
1
sL xt
2
sL
j=1,…,∞
yt
2
sLyt
1
sLyt1
sk yt
2
sk
yt1
s1 yt
2
s1
y1
DsL
zs
zszs
zs
zs
zs
zs
o Performance of DAA is better
x Computationally very expensive

45. Open challenges
 What is the ground-truth of chunks (segments) of driving
behavior data?
 How can we deal with drivers’ and vehicles’ characteristics
in a date-driven manner?
 How can we transfer knowledge learnt from a driver, a
vehicle and an environment to another (, i.e., transfer
learning) ?
 Inventing an effective application of symbolized NDD.
 Reducing computational cost of NPB-DAA and applying it to
NDD.
 Integrating front camera image and GPS information into
symbolization.
Towards efficient utilization of NDD

46. Contents
1. Overview of Double Articulation
Analysis of Driving Behavior
2. Applications
 Segmentation and topic modeling
 Prediction of driving behavior
 Large-scale data
3. Deep learning for driving behavior
feature extraction

47. Feature extraction from naturalistic
driving behavior data
 Even a fully unsupervised learning method, like DAA,
depends on (hand-crafted) feature vectors.
 Driving behavior data recorded in a different car is
different. Which sensor information should we feed
into an analysis method (a machine learning method)?
47
Vehicle dynamics and behavior
Perception
Decision
Maneuver
Environment
Intention
Automatic feature extraction method

48. Using deep sparse autoencoder for extracting feature
representation from driving behavior data [Liu+ 14-16]
48
Deepsparseautoencoder
Low-dimensionalfeature
representation
HaiLong Liu, Tadahiro Taniguchi, Toshiaki Takano, Yusuke Tanaka, Kazuhito Takenaka and Takashi Bando, Visualization of
Driving Behavior Using Deep Sparse Autoencoder, 2014 IEEE Intelligent Vehicles Symposium (IV'14). (2014)

49. Visualization of Driving Behavior with color
representation Using Deep Sparse Autoencoder
Drivingcolormap
RGBcolorspace
 Changes in driving behavior caused
by environmental differences were
represented by difference in colors.
HaiLong Liu, Tadahiro Taniguchi, Toshiaki Takano, Yusuke Tanaka, Kazuhito Takenaka and Takashi Bando, Visualization of
Driving Behavior Using Deep Sparse Autoencoder, 2014 IEEE Intelligent Vehicles Symposium (IV'14). (2014)

50. Essential Feature Extraction of Driving Behavior
 We have applied Deep Sparse Auto Encoder (DSAE) to driving behavior
data to extract “essential” features from driving behavior data [Liu ‘15].
50
The distances calculated using CCA
It was shon that
 DSAE can filter redundant
information, and
 DSAE can extract
consistent information.
HaiLong Liu, Tadahiro Taniguchi, Yusuke Tanaka, Kazuhito Takenaka and Takashi Bando, Essential Feature
Extraction of Driving Behavior Using a Deep Learning Method, 2015 IEEE Intelligent Vehicles Symposium
(IV'15) .(2015) Best student paper

51. Repairing defective driving behavior data [Liu+ 16]
HaiLong Liu, Tadahiro Taniguchi, Kazuhito Takenaka, Yuusuke Tanaka, and Takashi Bando,
Reducing the Negative Effect of Defective Data on Driving Behavior Segmentation Via a Deep
Sparse Autoencoder, IEEE 5th Global Conference on Consumer Electronics, .(2016) IEEE GCCE
2016 Outstanding Paper Award

52. Conclusion (Wrap up)
 We introduced our fully unsupervised learning-
based approach to segmentation and
symbolization of NDD.
 Double articulation analysis for driving behavior
data was explained.
 Applications of DAA, e.g., segmentation, video
summarization, prediction and information
retrieval, were introduced.
 Deep learning for feature extraction was
explained.
To analyze naturalistic driving behavior data in a fully data-
driven manner, we still have many rooms to explore!

53. Information
2017/6/12 53
email: taniguchi@ci.ristumei.ac.jp
Special Thanks
• Ritsumeikan University
• S. Nagasaka, H. Liu, K. Furusawa, Y.
Fuchida, R. Nakashima,
T. Sugihara
• DENSO co.
• K. Takenaka, K. Hitomi,
Y. Tanaka, H. Misawa, M. Mori
• DENSO International America
• T. Bando
• Nagoya University
• K. Takeda, C. Miyajima
• Okayama Pref. University
• N. Iwahashi
Visit http://www.tanichu.com/
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Twitter: @tanichu
Acknowledgement
[Github] NPB-DAA
https://github.com/EmergentSystemLabStudent/NPB_DAA
We are looking for collaborators!
We can share our code if you want.
We are calling for a postdoc and
PhD candidate