Driving Behavior Signals and Machine Learning: A

Driving Behavior Signals and Machine Learning: A Personalized Driver Assistance System Victoria Martínez, Inés del Campo, and Javier Echanobe Department of Electricity and Electronics University of the Basque Country UPV/EHU Leioa, Spain [email protected], [email protected], [email protected] Abstract—The progressive integration of driver assistance systems (DAS) into vehicles in recent decades has contributed to improving the quality of the driving experience. Currently, there is a need for individualization of advanced DAS with the aim of improving safety, security and comfort of the driver. In particular, the need to adapt the vehicle to individual preferences and requirements of the driver is an important research focus. In this work, an individualized and non-intrusive monitoring system for real-time driver support is proposed. The kernel of the system is a driver identification module based on driving behavior signals and a high-performance machine learning technique. The scheme is suitable for the development of single-chip embedded systems. Moreover, most of the measurement units used in this research are nowadays available in commercial vehicles, so the deployment of the system can be performed with minimal additional cost. Experimental results using a reduced set of features are very encouraging. Identification rates greater than 75% are obtained for a working set of 11 drivers, 86% for fivedriver groups, 88% for four-driver groups, and 90% for threedriver groups. Keywords—machine learning; classification; feature selection; smart car; real-time system; driver assistance systems; ambient intelligence

I. INTRODUCTION In the context of present challenges for society, the aim of the automobile sector in industrialized countries is mainly concerned with the quality of mobility. Concerning individual mobility, the progressive integration of driver assistance systems (DAS) into vehicles in recent decades has contributed to improving the quality of the driving experience. Moreover, there is still room for improvement of these systems until reliable automated driving becomes available. In [1] the authors identify the need for individualization of advanced DAS and human machine interface (HMI) as key aspects in future developments. In particular, the need to adapt the vehicle to individual preferences and requirements of the driver is recognized as an important research focus, while the role of DAS for drivers with special needs, such as elderly people, or first-time drivers, is emphasized. Furthermore, as the driving population is getting older, the availability of individualized This work has been partially funded by the Basque Government under Grant IT733-13, and the Spanish Ministry of Economy and Competitiveness under Grant TEC2013-42286-R.

Koldo Basterretxea Department of Electronic Technology University of the Basque Country UPV/EHU Bilbao, Spain [email protected]

assistance systems, inspired in safety and well-being, is becoming increasingly important [2], [3]. The above perspective, based on individualization of assistance, has already been applied to inhabited environments (e.g. smart homes, hospitals, or public buildings, among others). This approach, known as Ambient Intelligence (AmI), supports the development of intelligent environments from a human-centred perspective [4]. AmI is a mature approach, widely developed during the last two decades, that proposes the integration of intelligence and technology with the aim of supporting people in their everyday lifes and improving their overall well-being. It refers to non-intrusive environments, enriched with pervasive devices (small processors, sensors, actuators, etc.), that are able to take decisions autonomously to benefit the users of the environment [5], [6]. The main goal of AmI is to enhance the quality of life of human beings by making their environments more comfortable and secure. Buildings and transport systems are the environments where people in the developed countries spend most of their time. The car can also be viewed as an intelligent environment (i.e. smart car) and the above ideas concerning AmI can be applied to the vehicle and its occupants with the aim of improving driver performance and overall traffic safety. In the same manner that wireless and mobile systems support vehicle connectivity, ubiquitous and pervasive computing, inherent to AmI technology, will enhance the interaction between the vehicle and the driver [7]. The main challenge consists in adapting AmI concepts to the vehicle, its occupants, and the surrounding environment [2], [8]. In this work, an individualized and non-intrusive monitoring system for real-time driver support is proposed. The ultimate goal of this solution consists in improving comfort, safety, and security in cars. The kernel of the system is a high performance driver identification module based on driving behavior signals and machine learning techniques. The proposed scheme is suitable for the development of single-chip embedded systems and in-car integration [9]. The capability of adapting vehicles to their owners can be implemented with minimal cost by taking more advantage of the large amount of resources -sensors, measurement units, and communication

technology- that are already available in current commercial vehicles.

in order to highlight the powerful potential of the proposed method for in-car integration.

There are several products on the market related to driver identification. For example, [10] commercializes an RFIDbased device for insurance and fleet management that monitors vehicle location and driving behavior. Collected data, along with driver identity, are sent to a server for real-time monitoring. The main disadvantage of this approach is the risk of attempts at impersonation of RFID technology. There are some research projects where biometric measurements are used for driver identification. For example, researchers in [11] use an infra-red camera to perform a face scan of the driver. Then, the data from the scan are compared with the information on all person stored in the identification system. A different biometric approach can be found in [12] where a posture recognition technique, based on pressure sensors integrated into the driving seat, is described. It uses the pelvic bone distance as a biometric trait. This method guarantees universality and collectability of the data, however, the identification performance of the method reported in this research work is still poor. Finally, there has been increasing research activity concerning driver identification using driving behavior signals [13]-[18]. Driving behavior signals, mainly CAN bus signals, and sensor recordings (e.g. gas pedal pressure, brake pedal pressure, vehicle velocity, etc.) were used to develop models of driver behavior. In [17] the authors obtained satisfactory results by means of Gaussian mixture models (GMM), while in [19] support vector machines (SVM), are used. However, both algorithms GMM and SVM are complex algorithms, with high computational demands that rely on an external server. These algorithms are unsuitable for in-vehicle embedded solutions with restrictive design specifications such as high performance, reduced size, and low power consumption.

The rest of the paper is organized as follows: Section II presents the system architecture. In addition, the extreme learning machine algorithm is introduced. Section III describes the data collection used in this work and addresses the selection of the most relevant features. In Section IV representative experimental results are provided. Finally, Section V presents some concluding remarks.

In the present work, a new machine-learning method, known as extreme learning machine (ELM) [20]-[21], is used to identify the driver in real-time. It is an emerging paradigm that has attracted the attention of the research community because it outperforms conventional back-propagation artificial neural networks (BP-ANN) and SVM in two main aspects: the generalization performance of ELM improves the traditional learning algorithm and its learning speed is very high. Moreover, the suitability of a broad set of driving behavior signals to identify the driver is evaluated. Different feature selection techniques were investigated with the aim of reducing the dimensionality of the problem. Subsequently, a hybrid feature selection method that combines a clustering filter with a machine-learning wrapper is applied with the aim of reducing further the system dimensionality, but without losing identification capability. The set of selected signals and variables includes time-domain as well as frequency-domain information. Representative experimental results are provided

II. SYSTEM ARCHITECTURE Fig. 1 depicts a block diagram of the proposed system. The main unit of the system is a real-time monitoring and control module. This module receives information from the measurement units installed in the car and eventually from the user interface. This information is continuously monitored, while different kinds of signals - mainly real-time control signals - are generated. Most of the actions inferred by the system depend on the identity of the driver, so the driver identification procedure is activated whenever a new occupant enters the car. The individualization capability of the system is made possible by integrating a real-time machine-learning core into the device. The core implements an adaptive extreme learning machine algorithm. Autonomy and real-time adaptation are easier to achieve with ELM than using other well-known machine learning techniques, mainly because it involves a single design parameter: the number of neurons of the hidden layer (L). The safety module is conceived of with the aim of improving the functionality of current DAS. The prevention of accidents caused by abnormal driving (e.g. do to drowsiness, stress, etc) is the main goal of this module. The security module is concerned with the protection of the car against theft. A warning signal will be activated, after a few driving minutes, when an unauthorized driver is detected. This signal could be used in different ways, for example, sending a message to the owner of the car. Finally, the comfort module acts on comfort parameters (e.g. position of car seat, position of mirrors, display configuration, lighting, internal temperature, etc.). Since no predefined operational model exists for the habits and needs of drivers in cars, the model is to be acquired by monitoring the actions of each authorized driver and learning through observation. The obtained model is valid only for that driver, and must be continuously updated to cope with the change of his/her habits over time. This module also makes use of the machine-learning core. It is worth noting that the comfort, security, and safety modules will use the proposed identification method, but are not addressed in the paper.

Real-Time System Monitoring and Control

CAN Bus Signals

IMU XYZ accelerometers

Driver Identification In-vehicle Sensors

Comfort Module

In-vehicle actuators

Security Module

Anti-theft system

Safety Module

ADAS

Driver Interface

Extreme Learning Machine Core

Fig. 1. Block diagram of the system architecture for in-vehicle deployment of the personalized DAS.

A. Extreme-learning machine core As can be seen in Fig.2, ELM consists of a single-hiddenlayer feedforward neural network. The weights and biases that link the inputs with the neurons in the hidden layer are random numbers, so they are independent of the target application. The distinctive feature of this algorithm is its learning procedure: training neural networks using ELM is very fast because the hidden layer does not need to be tuned. The weights linking the hidden layer with the output layer are computed by solving a linear equation system. Let us consider a neural network with n inputs, m outputs, and L nodes in the hidden layer, such as that depicted in Fig. 2. The network output for generalized ELM is [21] L

y (x) = ∑ βi hi (x) = h(x)β .

(1)

approximation capability could be used as activation function [21]. B. Learning with ELM The main difference between ELM and traditional learning approaches is that the hidden layer need not be tuned; it is a randomized layer. That is to say, the set of parameters of the hidden nodes (ai , bi ), 1 ≤ i ≤ L, are randomly generated. Learning with ELM is a straightforward procedure that aims at computing the vector of output weights, β in (1), for each output node.

Given a set of K training samples, (x j , t j ), 1 ≤ j ≤ K , where x j ∈ \ n is the jth input vector, and t j ∈ \ m is the corresponding output vector (i.e. the target output), learning is performed by solving (1) for the set of training samples

i =1

Without loss of generalization, a single output node (m=1) is taken in (1). The vector of weights β = [ β1 ," , β L ]T links the hidden nodes (i.e. random nodes) with the output node, and h(x) = [h1 (x)," , hL (x)] is the output vector of the hidden layer for a given input x ∈ \ n . The output of the ith hidden node is hi (x) = f (ai , bi , x) = s (ai x + bi ), a i ∈ \ n , bi ∈ \ ,

T = H ( x)Β

(3)

with H being the hidden layer output matrix

⎡h(x1 ) ⎤ ⎡ h1 (x1 ) " hL (x1 ) ⎤ ⎢ ⎥ # # ⎥⎥ H = ⎢ # ⎥ = ⎢⎢ # ⎢⎣h(x K ) ⎥⎦ ⎢⎣ h1 (x K )" hL (x K ) ⎥⎦ K × L

(4)

⎡ t1 ⎤ ⎢ ⎥ Β = [β1 " β m ]L×m , and T = ⎢# ⎥ ⎢⎣t K ⎥⎦

(5)

(2)

with s (ai , bi , x) being the sigmoid activation function, ai the random weight vector connecting the inputs with the ith hidden node, and bi the random bias of the ith hidden node. Any piecewise continuous function satisfying ELM universal

. K ×m

TABLE I.

Then, the matrix of output weights is

DRIVING BEHAVIOR SIGNALS

Initial signal selection based on correlation coefficients −1

Β = H T,

(6)

where H −1 is the Moore–Penrose generalized inverse of matrix H . Although different methods can be applied to solve (6), in this work, singular value decomposition (SVD) will be used. Hidden layer: random parameters

a1 b1

x1 # xn

#

#

ai

βm

bi

aL

β1

y1

#

# ym

#

bL

Fig. 2. Topology of the single layer feedforward network used in ELM.

III. DATA COLLECTION AND FEATURE SELECTION The data collection used in this work was supplied by the “Drive-Safe Consortium” in Turkey. It was collected in Istanbul with a non-automatic instrumented car equipped with different sensors [16]. The complete data set includes audio and video recordings, CAN-bus signals, pedal-sensor recordings, a frontal laser scanner, and an inertial measurement unit (IMU) with XYZ accelerometers. However, neither video nor audio signals were considered because of their intrusive nature. Table I summarizes the whole set of driving behavior signals referred to in this study. We used a subset of the database with the recording sessions of 11 drivers. The CAN bus and the pedal sensors are sampled at 32 Hz, the IMU recordings at 10 Hz, and the laser range finder measurements at 1 Hz. In this study, all signals are handled jointly, which requires a resampling of all datastreams to the highest frequency. The car route is around 25 km (about 40 minutes), and includes different kinds of sections: city, very busy city, highway, highway with less traffic, a university campus, etc. The car route is the same for all drivers. However, the road conditions differ depending on traffic and weather conditions. Approximately half of the driving sessions include driving disturbances with the aim of reducing the attention of the drivers: signboard and plate reading, different types of dialogs on cell phones, and conversations with passengers. To recreate realistic situations, these driving periods were also considered.

Original set of signals

Nomenclature

Selected signalsa

1

Steering wheel angle

SWA

1

2

Steering wheel relative speed

SWRS

2

3

Vehicle speed

VS

3

4

Wheel speed -front right-

WSFR

3

5

Wheel speed -front left-

WSFL

3

6

Wheel speed -rear right-

WSRR

3

7

Wheel speed -rear left-

WSRL

3

8

Percent gas pedal

PGP

4

9

Engine RPM

ERPM

5

10

Yaw rate (CAN bus)

YR_CAN

10

11

Break pedal pressure

BP

6

12

Gas pedal pressure

GP

7

13

Roll rate

RR

8

14

Pitch rate

PR

9

15

Yaw rate

YR

10

16

X axis accelerometer

XACC

11

17

Y axis accelerometer

YACC

12

18

Z axis accelerometer

ZACC

13

19

Distance to obstacle, 0º

d_000

discarded

20

Distance to obstacle, 90º

d_090

14

21

Distance to obstacle, 180º

d_180

discarded

a.

The signals with the same number are highly correlated, only the highlighted signals are retained.

In order to have an efficient driver identification system, a series of features that are particularly informative for the process are extracted from the collection of measurements. Reducing the number of features (i.e. the dimensionality of the problem) is important in machine learning, mainly when the machine core is to be embedded in a car. Both computation time and storage resources can be drastically reduced with an adequate selection of the subset of useful signals and variables. There are two main categories of feature selection techniques: filter methods and wrapper methods. The former select a reduced subset of variables by evaluating general characteristics of the data (i.e. the selected learning algorithm is not involved in the selection process), while the latter use the performance of the selected machine-learning algorithm to evaluate each subset of variables. Filters measure the relevance of different subsets of features. Usually they order features individually or as nested subsets of features, while the filter assessment is done by means of statistical tests. They are robust against overfitting, but may fail to select the most useful features for a given classifier. On the other hand, wrappers measure the usefulness of feature subsets. They perform an exhaustive search of the space of all feature subsets and use

cross validation to evaluate the performance of the classifier. Wrappers are able to find the most useful features, but they favour overfitting and are very time-consuming. As will be seen, a combination of both filters and wrappers provide a trade-off between usefulness and robustness. A. Correlational relationships between signals First, a simple correlation study was performed with the aim of detecting strong lineal relationships between pairs of signals. Two groups of signals with high correlation coefficients ( r > 0.99 ) have been found. The first group is representative of the speed of the vehicle. This group, labeled with the number 3 (see Table I), includes the vehicle speed itself and the speed of the four wheels (i.e. VS, WSFR, WSFL, WSRR, and WSRL); VS has been selected as representative signal of the group. The second group with high correlations is the pair of signals that measure the yaw rate (i.e. YR_CAN and YR); in this case the CAN bus signal has been discarded because it provides less precision. Other groups with somewhat lower correlation were found, but no other signal was eliminated as a result of the correlational analysis. Finally, only the frontal laser scanner measurement will be used (i.e d_090) because lateral distances to obstacles (d_000 and d_180) seem to be too context-dependant (i.e. it depends on the environment infrastructure and topography) and can blur the performance of the identification.

B. Hybrid feature selection The reduced set of 14 signals is examined in order to obtain a collection of informative features, namely the temporal means and the energies in frequency domain over 128 s frames (4096 samples) for every second (32 samples), giving rise to a set of p = 28 features. The variables representing the temporal means are labeled from 1 to 14, as shown in Table II. The variables designating the energies are numbered from 15 to 28 in the same order. A hybrid feature selection method is then applied to reduce the system dimensionality without losing significant identification capability. The proposal combines a clustering filter based on the nearest shrunken centroids (NSC) procedure for feature selection in high-dimensional problems [22], with a wrapper around the extreme learning machine algorithm.

TABLE II.

FEATURE SELECTION

Labels (numbers) assigned to the variables involved in the hybrid feature selection process and selected subsets: VS12 (light grey + dark grey) and VS07 (dark grey) Time-domain features (mean value)

Frequencydomain features (energy)

Steering wheel angle

1

15

Steering wheel relative speed

2

16

Vehicle speed

3

17

Percent gas pedal

4

18

Engine RPM

5

19

Break pedal pressure

6

20

Gas pedal pressure

7

21

Roll rate

8

22

Pitch rate

9

23

Yaw rate

10

24

X axis accelerometer

11

25

Y axis accelerometer

12

26

Z axis accelerometer

13

27

Distance to obstacle, 90º

14

28

Signals

with si being the pooled within-class standard deviation for each variable:

si2 =

1 n−K

∑ ∑ (x

ij

k

j∈Ck

− xij ) , 2

(8)

s is the median value of the si over the set of variables, and mk = 1 nk + 1 n . In the shrinkage, dik is reduced by soft-thresholding: dik' = sign ( dik ) ( dik − Δ )+

(9)

The NSC method is a modification of the nearest centroid classifier that considers denoised versions of the centroids as the class prototypes. The class centroids are increasingly shrunken towards the overall centroid, and as they are shrunk, some features from the initial set no longer contribute to the classification.

where t+ = t if t > 0 and zero otherwise. And according to (7) the shrunken centroids are calculated as follows:

Let xij be the values for the variables i = 1,…, p, and for the samples j = 1,…, n. And let Ck be the set of the indices of the nk samples in class k = 1,…, K. A t-statistic dik is used to compare each class k to the overall centroid for each variable i:

As the parameter Δ increases, dik for some variables are reduced to zero for all classes, and all centroids xik are shrunk to xi . Those variables are therefore effectively eliminated from the class prediction.

x − xi , dik = ik mk ( si + s )

In this work, the shrinkage is applied from Δ = 0, that is to say, no shrinkage and no variable eliminated, up to Δ = 40, when there are only three variables left and the identification capability over the set of 11 drivers has substantially lowered.

(7)

xik' = xi + mk ( si + s ) dik'

(10)

The training and the test errors are computed for different Δ values in this range. The collection of samples of the route of each driver is divided into a training set consisting of the first two thirds of the itinerary, and a test set comprising the last third. Training and prediction are evaluated both by the nearest shrunken centroids clustering procedure, and by the extreme learning machine method. The nearest shrunken centroids classification of a test sample x* = ( x1* , x2* ,..., x*p ) is done by calculating the standardized distances of sample x* to each shrunken centroid or prototype of class k:

p

δ k ( x) = ∑ i =1

(x

* i

− xik' )

( si + s )

2

(11)

2

The prediction for sample x* is then carried out by a “winner-takes-all” rule that chooses the class for which the distance δk is the smallest. The extreme learning machine training and prediction are performed at each Δ value over the subset of active variables with 50 hidden neurons. In every case, the average accuracy over 10 trials of ELM has been computed to provide more stable results and minimize the effect of randomness. More comprehensive exploratory experiments were previously performed with the aim of selecting the best number of hidden neurons; 50 neurons provide a trade-off between system complexity and performance. VS12 0.7

IV. EXPERIMENTAL RESULTS The driver identification system, with a 50 hidden neuron ELM, has been tested with the whole set of 11 drivers, as well as within subgroups of 3, 4 and 5 drivers - similar to a real-life scenario. The results in Fig. 4 correspond to the average identification rates in all possible subgroups in each category, with the subset of 12 variables (VS12) and the reduced subset of 7 variables (VS07). The subgroups of drivers with the highest rates, calculated from VS07, are {d3,d7,d9}, {d3,d7,d9,d10}, and {d1,d6,d7,d9,d10}. On the other hand, the subgroups with the lowest identification rates present also common drivers: {d2,d3,d4}, {d2,d3,d4,d8}, or {d2,d3,d4,d6,d8}. The identification rates for the whole set of 11 drivers are 76 % and 74.5 % with the feature subsets VS12 and VS07, respectively. Within groups of 3 drivers, the system achieves identification rates of 90.7 % and 89.6 %; within groups of 4 drivers, the rates are 88.4 % and 86.5 %; and the prediction for groups of 5 drivers is 86.1 % with the VS12 set of features, and 83.7 % with the VS07 subset.

VS07

nsc train error nsc test error ELM train error

0.6

Fig. 3 shows the shrinkage results where good performance of the extreme learning machine method becomes noticeable. The evolution of the ELM error is somewhat similar to that of the centroid clustering, but the minimum ELM test error is reached for a higher shrinkage (Δ = 14.4), that is to say, with a smaller subset of variables: 12 out of the initial set of p = 28 variables, which will be called the subset VS12 = {4,5,6,7,8,9,14,18,19,20,21,26}, highlighted in grey in Table II. Moreover, there is a zone with higher shrinkage that shows almost as low ELM test error as the minimum. In this area, defined between Δ = 24 and Δ = 30.4, only 7 variables are left, the subset VS07 = {6,7,8,9,18,20,21}, highlighted in dark grey in Table II: gas and break pedals related variables, roll rate, and pitch rate. We can conclude that this smaller subset is enough to achieve the same degree of accuracy. In the upper x-axis (see Fig. 3), the shrinkage points corresponding to both subsets of features are shown.

ELM test error

0.5 11 drivers

3• driver groups

4• driver groups

5• driver groups

Error

95

0.4

90 85 Identification Rate (%)

0.3

0.2

0.1 14.4 24 Amount of Shrinkage

Δ

30.4

Fig. 3. NSC and ELM training and test errors as a function of the shrinkage parameter Δ. In Δ=14.4, only the subset of variables VS12={4,5,6,7,8,9,14,18,19,20,21,26} remains active. Reached Δ=24, five variables of VS12 have been eliminated; the subset VS07={6,7,8,9,18,20,21} decides prediction until Δ=30.4, when variable 18 is removed.

80 75 70 65 60 55 50

12 features (VS12)

7 features (VS07) Feature Subsets

Fig. 4. Driver identification rates of ELM systems based on feature subset VS12 and feature subset VS07 for different categories of driver groups.

The results obtained with the subset of 7 variables increase the identification rates obtained in [18], with the same data collection, in more than 5% for groups of 3, 4 and 5 drivers. The authors use a multilayer perceptron (MLP) with a single hidden layer and 20 input variables. The results obtained with the MLP are similar to those reported in [17] where a more complex statistical model is used (i.e. Gaussian mixture model). In Fig. 5, the results of a series of experiments of identification are shown. Prediction is assessed every minute throughout the last 10 min section of each driver recording, which is in the testing part of the route for all drivers. In each curve are therefore represented the estimations for different decision window sizes – a circle ‘o’ indicates correct driver selections, while mark ‘x’ indicates wrong predictions.

Driver identification rate (%)

As can be seen, the system fails to do a correct prediction at several points along the route of drivers d2, d3, and d4. These drivers are likely to appear in groups (of 3, 4, or 5 drivers) with low identification rate. On the other hand, for drivers d1, d7, d9, and d10, prediction is about 100 % at almost any point of the evaluated sections. They are often part of the groups with the highest rates. 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0 100 50 0 0

d1 1

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An individualized and non-intrusive monitoring system for real-time driver support is proposed. The kernel of the system is a driver identification module based on driving behavior signals and a high-performance machine learning technique. The proposed scheme is suitable for the development of singlechip embedded systems. Moreover, most of the measurement units used in this research are nowadays available in commercial vehicles, so the deployment of the system can be performed with minimal additional cost. Moreover, low-cost field programmable gate arrays (FPGA) can be used to implement efficient real-time electronic systems for the proposed scheme. A two-step approach for feature selection is proposed. First, correlation coefficients are used as statistical measurements of similarity with the aim of removing redundant and unimportant features. After that, a hybrid filter/wrapper method is applied. It combines a clustering filter, based on the nearest shrunken centroids, with a nonlinear extreme learning machine to evaluate the classification accuracy of the identification. The individualization capability of the system is made possible by integrating a real-time machine-learning core into the device. The core implements an adaptive extreme learning machine algorithm. It has been verified that extreme learning machines provide a robust learning algorithm, free of local minima, and without overfitting problems. Its learning algorithm is very fast and less dependent on human intervention than back-propagation neural networks or other mature machine-learning techniques such as support vector machine. All these characteristics make ELM suitable for autonomous real-time driver identification. The feature selection procedure reveals that the most relevant driving behavior signals are break pedal pressure, gas pedal pressure, roll rate, and pitch rate. Both kinds of features time-domain and frequency-domain contribute to characterize the behavior of the driver. In future work the performance of the driver identification system will be enhanced with new features: the suitability of Cepstral features, in combination with the above time-domain features and frequency-domain features, will be analyzed. In addition, the use of feature extraction techniques, based on principal component analysis (PCA), will be investigated. ACKNOWLEDGMENT

d9 1

V. CONCLUSIONS

The authors would like to thank Dr Hüseyin Abut and the researchers of the Drive-Safe Consortium in Istanbul for providing the data set used to perform the experimentation.

11

REFERENCES

d11 1

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Fig. 5. Identification rate and prediction of ELM system based on feature subset VS12 for last 10 min section of every driver recording (d1, d2,…, d11). Black mark ‘o’ indicates a correct driver selction; red mark ‘x’ indicates wrong predictions.

[1]

[2]

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