Full auto-calibration of a smartphone on board a vehicleusing IMU and GPS embedded sensors

Javier Almazan, Luis M. Bergasa, J. Javier Yebes, Rafael Barea and Roberto Arroyo

Abstract— Nowadays, smartphones are widely usedin the world, and generally, they are equipped withmany sensors. In this paper we study how powerfulthe low-cost embedded IMU and GPS could becomefor Intelligent Vehicles. The information given byaccelerometer and gyroscope is useful if the relationsbetween the smartphone reference system, the vehiclereference system and the world reference system areknown. Commonly, the magnetometer sensor is usedto determine the orientation of the smartphone, butits main drawback is the high influence of electro-magnetic interference. In view of this, we propose anovel automatic method to calibrate a smartphoneon board a vehicle using its embedded IMU andGPS, based on longitudinal vehicle acceleration. Tothe best of our knowledge, this is the first attempt toestimate the yaw angle of a smartphone relative to avehicle in every case, even on non-zero slope roads.Furthermore, in order to decrease the impact of IMUnoise, an algorithm based on Kalman Filter and fittinga mixture of Gaussians is introduced. The resultsshow that the system achieves high accuracy, thetypical error is 1%, and is immune to electromagneticinterference.

I. INTRODUCTION

A. Motivation

In recent years, the use of smartphones has grownconsiderably due to their versatility and increasing powerof calculation. Smartphones are more than simple cellphones, they are able to send e-mails, connect to theinternet, store data and perform many tasks such asa computer does, thanks to their suitable software andhardware. They usually have the following sensors: GPS,cameras, magnetometer, proximity sensor, ambient light,wifi, etc. depending on the model. Moreover, most smart-phones have a MEMS IMU (Micro-electromechanicalSystems and Inertial Measurement Unit) composed ofan accelerometer and a gyroscope to estimate the deviceorientation and switch the screen appearance betweenportrait and landscape views. In this paper, we will focuson the study of these two sensors. The widespread useof smartphones in the world, in combination with thefact that they have many sensors and high computationalcapabilities, makes them key tools for intelligent vehicleapplications [1] and inertial navigation systems [2].

Javier Almazan, Luis M. Bergasa, J. Javier Yebes, RafaelBarea and Roberto Arroyo are with Department of Electronics,University of Alcala. Alcala de Henares, Madrid, Spain. e-mail: javier.almazan, bergasa, javier.yebes, barea,roberto.arroyo@depeca.uah.es

Smartphones provide two kinds of measurements. Thefirst ones are relative to the world, such as GPS andmagnetometers. The second ones are relative to thedevice, such as accelerometers and gyroscopes. Knowingthe relation between the smartphone reference systemand the world reference system is very important in orderto reference the second kind of measurements globally [3].In other words, working with this kind of measurementsinvolves knowing the pose of the smartphone in theworld.

Intelligent Vehicles can be helped by using in-vehiclesmartphones to measure some driving indicators. On theone hand, using smartphones requires no extra hardwaremounted in the vehicle, offering cheap and standardsensors for the current vehicles in a direct way. Onthe other hand, accuracy and precision of smartphonelow-cost sensors are very low, although they could besufficient for some Intelligent Vehicles applications. Wehave to bear in mind that with this approach a portabledevice is used and its position is not known a priori.Therefore, knowing smartphone pose in the vehicle ismandatory in order to characterize vehicle pose by usingsmartphone embedded sensors.

B. Related Work

In the field of Intelligent Vehicles, kinematics of thevehicle is used to understand the driver state and drivingstyle. This section introduces different techniques toobtain the kinematics of a vehicle as a starting point tomonitor driving behaviour.

The most common method to estimate vehicle orienta-tion is using GNSS (Global Navigation Satellite System),such as GPS or GLONASS. The heading of the carcan be measured by means of two GPS (front-GPS andrear-GPS) [4] or calculating the difference between twoconsecutive samples using a GPS [5]. These positioningsystems have several limitations: the maximum frequencyis 1 Hz (at 120 Km/h, a vehicle advances 330 m in 1 s)and they do not work under low visibility of the satellites.

Daily et al. show the strong relation between steeringwheel angle and yaw rate of the vehicle using two GPSfixed to the chassis and an IMU in the steering wheel [4].Yao et al. also use a steering angle sensor to checkthe accuracy of the heading angle given by a GPS, inorder to learn lane change trajectories [6]. Hence, thesteering wheel angle is directly related to the kinematicsof the vehicle, in particular, to the heading angle. For

the purpose of steering wheel angle estimation, bus-CANdata obtained through an OBD-II connector is used inmultiple researches [1], [7] and [8]. The main advantageof this approach is that the acquisition frequency ishigher than 1 Hz. However, it has two drawbacks. Firstly,it is an invasive method, because it is mandatory toconnect additional hardware to the vehicle. Secondly,each automobile manufacturer has proprietary protocolsand all of them should be known for a generic and cheapapplication.

Therefore, the widely used method for measuring thekinematics of a vehicle by using portable devices isbased on measuring the Earth’s magnetic fields througha magnetometer in combination with other sensors suchas GPS or IMU. There are some papers in this linein the state-of-the-art to estimate the orientation of asmartphone on board a vehicle for intelligent vehiclepurposes [9], [10] and [11]. The main advantage of thisapproach is that the increased acquisition frequency ishigher than 1 Hz. Nevertheless, the main disadvantage isthe significant influence of electromagnetic interferences.The sensitivity of magnetometers is very high, so thissensor does not work properly in a vehicle, because thereare many electro-mechanical components that corruptthe measurements. Moreover, measurements provides bya compass are relative to the Magnetic North, not tothe Geographic North Pole. The difference between bothis the magnetic declination, which varies with time andplace.

Besides this, there are some works focused on thefusion of IMU and GNSS measurements to overcomethe aforementioned limitations of GNSS. However, theorientation of an IMU in a vehicle must be known apriori to calculate accurate kinematics of the vehicle.Krotak et al. study the acceleration of a vehicle toassess the condition of the driver using only a 3-axesaccelerometer [12], and in [13] Di Lecce et al. introducea system to detect driving patterns using a GPS and a2-axes accelerometer. In both works, the accelerometerreference system matches with the vehicle referencesystem, with the purpose of working with meaningfuldata.

In a general way, we need to know the pose of thesmartphone relative to the vehicle coordinate systemto correctly merge IMU and GNSS measures. In [14],Niu et al. show a car navigation system using inertialsensors of a smartphone, but the device must be fixedon the dash-board roughly aligned with the vehicleframe. In The Ohio State University, Dai et al. proposean IMU-based method to obtain pitch angle and rollangle of the smartphone relative to the vehicle in orderto detect drunk driving [15]. However, they assumethat the smartphone is aligned with the longitudinalaxis of the vehicle, so the main restriction of thissystem is that the yaw angle must be null. Finally,Mohan et al. [16], working for Microsoft, present a full-calibration smartphone system on board the vehicle. But

it only works if the vehicle is on a flat surface (zeroslope), because they base their research on the effect ofgravity and decelerations using a GPS to re-orientate theaccelerometer data.

The developed IMU based methods share the advan-tages of magnetometer based methods, because they aresensors embedded in the smartphone with an acquisitionrate higher than GNSS based methods, nevertheless,none of these works are able to perform a full-calibrationof the smartphone in the vehicle each time that thesmartphone is placed on board the vehicle.

C. Contribution

In this paper, we propose a novel system to estimatefull auto-calibration of a smartphone on board a vehicleusing IMU and GPS embedded sensors. Full calibrationprovides the position (latitude, longitude, altitude) andthe orientation (Euler Angles: pitch, roll, yaw) of thesmartphone relative to the world.

Our main contribution is the method employed toestimate the yaw angle of the smartphone relative tothe vehicle coordinate system. This angle is based onlongitudinal vehicle acceleration during straight sectionsof road, processing IMU measurements. We can do thisbecause we are able to decouple gravity and vehicleacceleration measured by the smartphone. In order todecrease the contribution of the IMU noise, we proposeto use Kalman Filter, in accelerometer and gyroscopemeasurements, instead of other filtering methods. In spiteof filtering the noise, the preliminary yaw angle betweenthe smartphone and the vehicle is inaccurate. Therefore,we propose to apply a fitting mixture of Gaussians toincrease the exactitude of yaw angle. Finally, full auto-calibration of the smartphone is attached merging theprocessing IMU and GPS data.

To the best of our knowledge, this is the first attemptto estimate the yaw angle of a smartphone relative toa vehicle in every case, even on non-zero slope roads,using its embedded IMU based on vehicle acceleration. Incontrast to the magnetometer based methods, our systemis immune to electromagnetic interference. Moreover, thesample frequency is higher than those methods onlybased on GPS. It is set at 20 Hz, at 120 Km/h, a vehicleadvances 1.6 m between consecutive samples, instead of33 m using GPS at 1 Hz.

D. Paper Organization

The rest of the paper is organized as follows: inSection II we introduce the reference systems and thesystem architecture. In Section III, we describe the signalprocessing used to calculate the vehicle’s acceleration andthe estimation of yaw angle based on this acceleration.In Section IV we describe the devices used, we evaluateour system in real conditions over an electromechanicalgoniometer ground-truth and we compare it with amagnetometer-based system. Finally, main conclusionsand future work are described in Section V.

II. SYSTEM OVERVIEW

A. Reference Systems

In order to know the kinematics of a vehicle, forintelligent vehicle applications using a smartphone, wemust be aware of the relative position between the smart-phone and the vehicle and the world. Three referencesystems are defined: the smartphone’s, the vehicle’s andthe world’s. The three reference systems are defined, asdepicted in Figure 1.

(a) Smartphone refer-ence system.

(b) Vehicle refer-ence system.

(c) World referencesystem.

Fig. 1. Reference Systems.

Smartphone gyroscopes provide the Euler angles: roll,pitch and yaw. Roll and pitch angles are measurementsrelative to the world based upon gravity acceleration.Nevertheless, yaw angle is a measurement relative to theposition of the smartphone when that sensor is enabled.As that position is unknown, a novel method to obtainthe yaw angle is explained in next sections.

B. System Architecture

Achieving a full calibration of an object involvesproviding its position and orientation relative to a coor-dinate system. In our case, the position of a smartphonerelative to the world is defined by latitude, longitude andaltitude, and these values are provided by the embeddedgeo-location sensor. Analyzing the exactitude or accuracyof these measurements is not the purpose of this paper,we assume it is sufficient using a geo-location sensorbased on GPS and GLONASS.

Fig. 2. System architecture.

Regarding the orientation, the roll and pitch anglesof the smartphone relative to the world, they are theresult of processing the roll and pitch angles providedby the phone. However, estimating the yaw angle ofthe smartphone relative to the world is more complex.It is the composition of the yaw angle of the vehiclerelative to the world and the yaw angle of the smartphonerelative to the vehicle. The first one, is obtained based

upon two consecutive samples of embedded GPS, and isknown as heading related to the North Pole. The secondone, is estimated using our method to be explained inSection III. Figure 2 shows the architecture of the fullauto-calibration system.

III. YAW ANGLE ESTIMATION

Figure 3 depicts the main parts of the proposedalgorithm. The first step is filtering the accelerometer andgyroscope signals provided by the smartphone (aYS

, aZS,

rollS and pitchS). Then we decouple vehicle accelerationfrom gravity acceleration using trigonometric functions.The next step is to obtain the yaw angle based on a singlesample. Finally, the yaw angle between the vehicle andthe smartphone is estimated using a Gaussian MixtureModel (III-D).

Fig. 3. Yaw angle estimation algorithm.

A. Signal Filtering

The main drawback of low-cost embedded sensors istheir inaccuracy. High quality sensors add a stability stepto provide precise data. However, in our case, the rawaccelerometer data is very noisy, as can be seen in Fig. 4in blue. There are different techniques to process verynoisy signals; digital filters [11], median values over atemporal window [16] or Kalman Filter [20]. This lasttechnique is very useful when the noise is white. We haveexperimentally tested that accelerometer noise is whiteand a KF has been implemented according to Equation 1.

Xt+1 = F Xt + wt

Yt = H Xt + vt (1)

At each sample, the state vector Xt (acceleration,gyro, and their derivatives) is propagated to the newstate estimation Xt+1 multiplying by the constant statetransition matrix F. The measurement vector Yt consistsof the information contained within the state vector Xt

multiplied by the measurement matrix H. Moreover,wt ∼ N (0,Qt) is the process noise which is assumedto be drawn from a zero mean multivariate normaldistribution with covariance Qt. Likewise, vt ∼ N (0,Rt)

is the measurement noise with covariance Rt. Theexperimental value of Q noise is 10−5 for accelerationdata, and 10−1 for gyro data. The value of R noise isidentity matrix. With these values, the synchronizationof the four signals is achieved.

Figure 4 shows the observed input signals in blue, andthe estimated signals in red.

(a) Filtering y-acceleration,aYKF

.(b) Filtering z-acceleration,aZKF

.

(c) Filtering pitch angle,pitchS−W .

(d) Filtering roll angle,rollS−W .

Fig. 4. [Best Viewed in Color]. Kalman filter.

B. Decoupling Between Gravity Acceleration and VehicleAcceleration

According to the reference system introduced inSection II-A, when the smartphone is placed on boardthe vehicle in landscape position, the main weight ofgravity acceleration is along the X-axis. However, itsacceleration along the Y -axis and Z-axis is not null.The data provided by the embedded 3-axes accelerometeris the result of the sum of two components: vehicleacceleration and gravity acceleration. Thus, this sectiondescribes how to decouple both signals.

The gyroscope measures the rotation of the smart-phone in relation to the world. Therefore, we projectgravity acceleration onto each axis applying basictrigonometry, we assume |aG| = 1g. Our system accountsfor rollS−W and pitchS−W angles to obtain gravityacceleration on the Y -axis, aYG

, and on the Z-axis, aZG.

It is depicted in Figure 5 according to Equations 2and 3. In static landscape orientation, the rolls andpitchs angles will be null.

aYG= sin (rollS) |aG| (2)

aZG= − sin (pitchS) |aG| (3)

(a) Gravity acceleration on YS . (b) Gravity acceleration on ZS .

Fig. 5. [Best Viewed in Color]. Extracting gravity acceleration.The world reference system is in black, the smartphone referencesystem is in blue, the gravity acceleration is in red and thesmartphone is in green.

Afterward, vehicle acceleration (aYVand aZV

) iscalculated by subtracting gravity acceleration from thefiltered acceleration (aYKF

and aZKF), using Equations 4

and 5.

aYV= aYKF

− aYG(4)

aZV= aZKF

− aZG(5)

C. Yaw Angle Based on Single Sample

When the vehicle moves in a straight line, no lateralacceleration in the reference system of the vehicle canbe assumed. Therefore, the composition of aYV

andaZV

gives the vector of the vehicle acceleration in thereference system of the smartphone. As a result, thecomputation of the yaw angle between vehicle andsmartphone is calculated. This is showed in Figure 6according to Equation 6.

Fig. 6. [Best Viewed in Color]. Yaw angle based on singlesample. The vehicle reference system is in black, the smartphonereference system is in blue, the vehicle acceleration is in red andthe smartphone is in green.

yaw(V−S)raw=

arcsin(

aZV

|aY ZV|

)if aYKF

>= 0

π − arcsin(

aZV

|aY ZV|

)otherwise

(6)

where

|aY ZV| =

√|aYV|2 + |aZV

|2

The yaw angle is only based on the current sample.We know it as raw yaw angle because it is a preliminaryvalue.

To enforce the fact that the car moves in a straightline, we impose four conditions to apply this method.The first one is that the yaw angle variation of thesmartphone, ∆yawS , will be less than 0.04◦ -thresholdfixed based on sensor noise. Moreover, in order toavoid incorrect estimations of yaw(V−S)raw

, the variationbetween consecutive samples of pitch and roll will beless than one degree (due to bumps or potholes in theroad), ∆rollS ≤ 1◦ and ∆pitchS ≤ 1◦. As we use vehicleacceleration to get the yaw, we set a fourth condition:if the acceleration |aY ZV

| is less than 0.4 Km/h, thesystem does not compute yaw(V−S)raw

because the noisecorrupts the IMU measurements. Figure 7 shows theresults, taking into account all conditions in a sequenceof 3 minutes with many acceleration-brake events. Thedepicted signal is quite unstable, with high variation. InSection III-D a solution is proposed.

Fig. 7. Yaw angle based on single sample with straight lineconditions.

D. Yaw Angle Estimation Based on Stochastic Methods

In spite of filtering the input signals and forcing theaforementioned conditions, the preliminary yaw(V−S)raw

signal is not very accurate. The reason is that the methodused to decouple the vehicle and gravity accelerationis simple and it does not take into account the noiseof the sensors. Consequently, the preliminary signalcontinues to be affected by the effect of vehicle andgravity acceleration. In order to optimally decouple thesetwo contributions, a stochastic method based on fittingmixture models of Gaussian functions is proposed.

Expectation-Maximization (EM) algorithm is a wellestablished maximum likelihood algorithm for fitting amixture model to a set of data [21]. It should be notedthat EM requires a priori selection of model order,namely, the number of M components to be incorporatedinto the model. The probability density function in theform of a Gaussian Mixture distribution with 2 mixturesprovides one Gaussian centered on the best estimate

of yaw angle yaw(V−S), corresponding to the vehiclecontribution, and the other to gravity contribution.

At each significant event, accelerating or breaking,the system stores preliminary values of yaw angle,yaw(V−S)raw

. In order to design an accurate system,the EM algorithm starts to estimate yaw(V−S) after 100values (applying the stochastic method in a set of fewdata does not provide a significant value of yaw angle).We assume this short transition of 100 values is notrelevant for Intelligent Vehicle purposes. For each set ofsamples, the algorithm chooses the model with minimumcovariance and maximum amplitude as the optimum yawangle yaw(V−S). Figure 8 depicts the result of GMM-EMalgorithm based on minimum covariance.

Fig. 8. [Best Viewed in Color]. Normalized histogram ofyaw(V −S)raw

, in blue, and Gaussian models using GMM-EMalgorithm, in red. The estimated value of yaw(V −S) is drawn asa green square. Note: abscissa axis shows yaw(V −S)+90◦

IV. EXPERIMENTAL RESULTS

A. Smartphone Embedded Sensors

In the recent report published by Pew Internet &American Life Project [17], nearly half of Americanadults are smartphone owners. The most used smart-phone operating system in USA is Android (38%)followed closely by iOS (36%). Nonetheless, in contrastto the heterogeneity of the sensors of the smartphonesrunning Android (the sensors depend on the manufac-turer and on the model), latest versions of smartphonesof Apple have the same accelerometer and the samegyroscope. Consequently, these devices have been usedin many researches [1], [2], [11], [14], [18] and [19].Therefore, to carry out the proposed system we chosean iPhone 4 and an iPhone 4S.

Since June 2008 Apple has mounted the LIS331DLHaccelerometer in its devices. It is a MEMS ultra-low power digital 3-axis accelerometer provided bySTMicroelectronics. According to the datasheet, it hasdynamically user selectable full scales of ±2g, ±4g and±8g (Apple configures it in a ±2g range) and it is capableof measuring accelerations with output data rates from0.5Hz to 1kHz. Moreover, the bandwidth is 25Hz andthe acceleration noise density is 218µg/

√Hz.

The L3G4200D gyroscope mounted in the iPhone isalso provided by STMicroelectronics. It is a MEMS ultra-stable 3-axis digital output gyroscope, and it is usedto determine the rotation rate. Integrating this dataover time the three Euler Angles of the smartphone(pitch, yaw and roll) can be estimated. According tothe datasheet of the manufacturer it has a full scaleof ±250dps, ±500dps and ±2000dps and it is capableof measuring rates with a user-selectable bandwidth.Apple configures the rotation rate in a ±250dps range,for a gyroscope noise density of 0.03dps/

√Hz and a

bandwidth of 50Hz.

B. Data Acquisition

The common framework of Apple to read the valuesof the motion sensors could be quite confusing. Theyprovide preprocessed signals without bias, such as thevariable motionRate for the gyroscope, and the vari-able userAcceleration without the influence of gravityacceleration for the accelerometer. However, these signalsare not valid for dynamic environments, because theacceleration of the vehicle is confused with gravityacceleration. Thus, we propose to work with raw dataof the sensors, such as acceleration (x, y, z) and attitude(yaw, pitch, roll). As Shanklin et al. explain in [18], theintegration based method to obtain the Euler Anglesinvolves drift in the measurements. But, surprisingly, itis very low, almost nonexistent for the pitch and rollangles, because iPhone already implements an algorithmto remove the drift using the direction of the gravityobtained from the accelerometer. In our system, themaximum acquisition frequency is set at 20Hz, 20 timeshigher than GPS and most smartphone processors areable to achieve it.

C. Experiments

For the evaluation of the system that has been devel-oped, a high quality electromechanical goniometer is usedas ground-truth. The goniometer is an instrument formeasuring angles with great precision and is commonlyemployed in optical communications. Figure 9 shows theholder mounted in the middle of the windshield of thecar, which matches with the vehicle reference system.

The experiments were performed with two devices(iPhone 4 and iPhone 4S) to assess differences in theprocessing capabilities of each phone. Moreover, to checkthe quality of our system, we carried out sweeps from−50◦ to 50◦, in increments of ten degrees. For eachangle, we drove more than 2 hours. In Table I we showthe results obtained (average error, εavg, and standarddeviation, σ) in more than 44 hours of driving.

Overall, the results achieved are quite accurate. Inspite of both devices having the same sensors, there areslight differences between their results. iPhone 4 is lessprecise than iPhone 4S. The reason is that they havedifferent CPU, iPhone 4S is able to provide samples at20Hz, whereas the acquisition frequency of iPhone 4 is

(a) System mounted on the vehicle. (b) Electromechanicalgoniometer.

Fig. 9. Goniometer based ground-truth.

yawS−ViPhone 4 iPhone 4S

|εavg | σ |εavg | σ

−50◦ 2.23% 1.31% 0.81% 1.69%

−40◦ 1.28% 0.84% 1.05% 1.02%

−30◦ 1.19% 0.71% 1.17% 0.94%

−20◦ 0.84% 0.73% 1.03% 0.76%

−10◦ 0.89% 0.52% 0.29% 0.46%

0◦ 0.93% 0.96% 0.20% 0.43%

10◦ 0.59% 0.72% 0.39% 0.11%

20◦ 0.67% 0.42% 0.72% 0.66%

30◦ 0.77% 0.45% 0.96% 1.57%

40◦ 0.89% 3.23% 1.07% 0.80%

50◦ 1.73% 2.34% 0.62% 2.33%

Typ 1.09% 1.11% 0.76% 0.98%

TABLE I

Error and typical deviation using iPhone 4 and 4S.

near 14Hz, sometimes falling to 3Hz. The typical error,|εtyp|, of iPhone 4 is 1.09% (equivalent to 3.9◦) and thetypical error of iPhone 4S is 0.76% (equivalent to 2.7◦).The standard deviation, σ, is approximately 1%. Noticethat when the yaw angle is less than 10◦, the error ofiPhone 4S is less than 1.5◦.

Moreover, the estimated yaw angle of the smartphonerelative to the vehicle in combination with embedded geo-location sensor based on GPS and GLONASS, achievethe full auto-calibration of a smartphone relative to theworld, as it was depicted in Figure 2. For the evaluationof the whole system, the obtained yaw angle is comparedto the one based on magnetic fields. Figure 10 shows thecomparison between our system and the system basedon magnetometer, magnetic declination is taking intoaccount in order to reference both signals to GeographicNorth Pole. The magnetic-based method shows bias,fluctuations, gaps and offset. Therefore, our proposedsystem has higher accuracy.

Fig. 10. [Best Viewed in Color]. Comparison between oursystem and the system based on magnetometer.

V. CONCLUSIONS AND FUTURE WORK

The proposed system, based on vehicle accelerationmeasurements, is able to estimate the yaw angle of asmartphone relative to the vehicle using the embeddedIMU of an iPhone. In other words, this work haspresented a novel auto-calibration method for low-costIMU. To the best of our knowledge, this is the firstwork that is able to achieve the pose of a smartphonein a vehicle using the low-cost inertial sensors insteadof the digital compass. The main advantage is that ourapproach is robust against electromagnetic interferences.

Furthermore, the Gaussian Mixture Models techniqueapplied to a yaw histogram, significantly improves theaccuracy in yaw angle estimation, because it is able todecouple vehicle acceleration from gravity acceleration.

Further research could be extensive. Knowing the fullpose of the smartphone relative to the world opens thedoor to INS (Inertial Navigation Systems) for intelligentvehicle purposes. Moreover, in the future, our contribu-tion could be applied to detect the kinematics of thevehicle to estimate driver state (inattention, drowsinessor drunken state).

VI. ACKNOWLEDGMENTS

The authors are grateful for the cooperation renderedby the Community of Madrid in the Robocity2030-IICAM-S2009/DPI-1559 project, by the Spanish Ministe-rio de Economıa y Competitividad through the projectADD-Gaze (TRA2011-29001-C04-01) and by Angel Lla-mazares.

References

[1] T. Menard and J. Miller, “Comparing the gps capabilities ofthe iphone 4 and iphone 3g for vehicle tracking using freesimmobile,” in Intelligent Vehicles Symposium (IV), 2011 IEEE,june 2011, pp. 278 –283.

[2] C. K. Schindhelm, F. Gschwandtner, and M. Banholzer,“Usability of apple iphones for inertial navigation systems.” inPIMRC, K. Pahlavan, S. Valaee, and E. S. Sousa, Eds. IEEE,2011, pp. 1254–1258.

[3] J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, andH. Balakrishnan, “The pothole patrol: Using a mobile sensornetwork for road surface monitoring,” in The Sixth AnnualInternational conference on Mobile Systems, Applications andServices (MobiSys 2008), Breckenridge, U.S.A., June 2008.

[4] R. Daily, W. Travis, and D. M. Bevly, “Cascaded observers toimprove lateral vehicle state and tyre parameter estimates.”

[5] I. Constandache, R. R. Choudhury, and I. Rhee, “Towards mo-bile phone localization without war-driving,” in INFOCOM,2010, pp. 2321–2329.

[6] W. Yao, H. Zhao, F. Davoine, and H. Zha, “Learning lanechange trajectories from on-road driving data,” in IntelligentVehicles Symposium, 2012, pp. 885–890.

[7] R. Araujo, A. Igreja, R. de Castro, and R. E. Araujo, “Drivingcoach: A smartphone application to evaluate driving efficientpatterns,” in Intelligent Vehicles Symposium, 2012, pp. 1005–1010.

[8] V. Corcoba Magana and M. Munoz-Organero, “Artemisa: Aneco-driving assistant for android os,” in Consumer Electronics- Berlin (ICCE-Berlin), 2011 IEEE International Conferenceon, sept. 2011, pp. 211 –215.

[9] R. Bhoraskar, N. Vankadhara, B. Raman, and P. Kulkarni,“Wolverine: Traffic and road condition estimation using smart-phone sensors,” in Communication Systems and Networks(COMSNETS), 2012 Fourth International Conference on, jan.2012, pp. 1 –6.

[10] H. Eren, S. Makinist, E. Akin, and A. Yilmaz, “Estimatingdriving behavior by a smartphone,” in Intelligent VehiclesSymposium (IV), 2012 IEEE, june 2012, pp. 234 –239.

[11] D. Johnson and M. Trivedi, “Driving style recognition usinga smartphone as a sensor platform,” in Intelligent Trans-portation Systems (ITSC), 2011 14th International IEEEConference on, oct. 2011, pp. 1609 –1615.

[12] T. Krotak and M. Simlova, “The analysis of the accelerationof the vehicle for assessing the condition of the driver,” inIntelligent Vehicles Symposium (IV), 2012 IEEE, june 2012,pp. 571 –576.

[13] V. Lecce and M. Calabrese, “Experimental system to supportreal-time driving pattern recognition,” in Proceedings of the4th international conference on Intelligent Computing: Ad-vanced Intelligent Computing Theories and Applications - withAspects of Artificial Intelligence, ser. ICIC ’08, 2008, pp. 1192–1199.

[14] X. Niu, Q. Zhang, Y. Li, Y. Cheng, and C. Shi, “Using inertialsensors of iphone 4 for car navigation,” in Position Locationand Navigation Symposium (PLANS), 2012 IEEE/ION, april2012, pp. 555 –561.

[15] J. Dai, J. Teng, X. Bai, Z. Shen, and D. Xuan, “Mobilephone based drunk driving detection,”in Pervasive ComputingTechnologies for Healthcare (PervasiveHealth), 2010 4th Inter-national Conference on-NO PERMISSIONS, march 2010, pp.1 –8.

[16] P. Mohan, V. N. Padmanabhan, and R. Ramjee, “Nericell:rich monitoring of road and traffic conditions using mobilesmartphones,” in Proceedings of the 6th ACM conference onEmbedded network sensor systems, ser. SenSys ’08. NewYork, NY, USA: ACM, 2008, pp. 323–336. [Online]. Available:http://doi.acm.org/10.1145/1460412.1460444

[17] A. Smith, “Nearly half of americanadults are smartphone owners,” [web page]http://www.pewinternet.org/Reports/2012/Smartphone-Update-2012.aspx, 2012.

[18] T. Shanklin, B. Loulier, and E. Matson, “Embedded sensorsfor indoor positioning,” in Sensors Applications Symposium(SAS), 2011 IEEE, feb. 2011, pp. 149 –154.

[19] H. Chan, H. Zheng, H. Wang, R. Gawley, M. Yang, andR. Sterritt, “Feasibility study on iphone accelerometer for gaitdetection,” in PervasiveHealth, 2011, pp. 184–187.

[20] G. Welch and G. Bishop, “An introduction to the Kalman Fil-ter,” University of North Carolina at Chapel Hill. Departmentof Computer Science, Tech. Rep., 2001.

[21] F. Dellaert, “The expectation maximization algorithm,” inTECHREPORT, 2002.