Driving Events Identification and Operational Parameters

Correlation based on the Analysis of OBD-II Timeseries

Dimitrios Rimpas

and Andreas Papadakis

Department of Electrical and Electronics Engineering, University of West Attica, Egaleo, Athens, Greece

Department of Electrical and Electronics Engineering Educators, School of Pedagogical and Technological Education,

Athens, Greece

Keywords: OBD-II, Driving Event Identification, Gear Change, Timeseries, Correlation.

Abstract: On board diagnostics, OBD-II, allowsmonitoring and understanding of the engine operations through

continuous access to engine sensors, detection and diagnosis of errors. In this work, we select a set of OBD-

II parameters, Short-Term Fuel Trim, Manifold Absolute Pressure, Absolute Throttle Position, Revolutions

Per Minute, Calculated Engine Load, Engine Coolant temperature, Vehicle Speed, Catalytic Converter

Temperature, to create a set of driving timeseries. A subset of the values belongs to an existing OBD-II dataset

with automatic transmission, while the other subset has been retrieved from scratch, using OBD-II, with

manual transmission and during characterized driving conditions (cruising, idle and accelerations). We have

designed and implemented a set of rules, to recognise three driving events, i.e., idle, gear change, and

accelerations in both manual and automatic transmission. The frequency of these events in combination with

the parameter values have led to the identification of driving style differences and the impact in fuel

consumption. In addition, we have investigated the correlation among the (OBD-II) driving operational

parameters during the three driving modes (idle, cruising and acceleration) using the catch22 timeseries

analysis framework. The implemented mechanisms are extensible, in terms of considered vehicles, for

constant parameter monitoring and cloud-based storing, paving the way for transparent engine status, service

maintenance history and other added value services.

1 INTRODUCTION

Monitoring engine operations has attracted

researchers to understand the behaviour and improve

certain characteristics of the engine operations.

Gilman presented a driving assistant system, Driving

Coach, which monitors parameters to help increase

fuel efficiency depending on the driving style

(Gilman et al., 2015). Logged OBD-monitored data

can be transmitted to a telematics centre via mobile

network. This data is used for a prognostics model, as

built on correlations among fault codes

(cautions/warnings from sensors in the vehicle), to

prevent potential components breakdown (Szalay et

al., 2015).

In our previous work we have obtained and

analysed data through CANBus to calculate the

impact of different driving styles on fuel consumption

https://orcid.org/0000-0001-5832-759X

https://orcid.org/0000-0003-1890-5898

(Rimpas et al., 2020). OBD-II data are also used for

provisional or direct maintenance strategies (Peppes

et al., 2021; Kulakov et al., 2021; Gajek, 2016; Prytz

et al., 2015) by detecting the diagnostic trouble code

(DTC) (Beig et al., 2020; Oluwaseyi and Sunday,

2020; Jhou et al., 2013). This can facilitate tracking

errors and performing repairs in terms of costs and

work. Driving and maintenance records can be stored

in a trustworthy manner, providing information for

the vehicle performance and reliability. Advice on

safe and economical driving has been provided

through data analysis for driver assistance and risk

prediction and management (Pan et al., 2017). In this

work, we select a set of key driving parameters, as

retrieved by OBD-II readings, to form driving

timeseries. An existing third-party dataset is used

(

Kwak

et al., 2016), while we also create our own sets

of vehicle operation in different driving modes.

Rimpas, D. and Papadakis, A.

Driving Events Identiﬁcation and Operational Parameters Correlation based on the Analysis of OBD-II Timeseries.

DOI: 10.5220/0011036400003191

In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2022), pages 257-264

ISBN: 978-989-758-573-9; ISSN: 2184-495X

257

The main objectives are two-fold:

a) To design and validate rules for the

identification of three driving fundamental

conditions (idle, gear change in manual and

automatic transmission, acceleration).

b) To identify and quantify, based on analysis

tools, the existence or lack of relationships

(correlation) among driving parameters (such

as engine load, RPM, and STFT).

The main paper contribution includes the event-

identification rules and their evaluation, in automatic

and manual transmission modes. These rules can

support labelling / characterization of existing

timeseries. The identified events can support the

identification of differences in driving styles in an

explainable and quantitative manner. This acts

complementarily with AI-based works which pursue

driving style identification but perform that in an

opaque manner (in terms of the timeseries analysis

and the interim results). Another contribution of the

paper is the identification of the correlation of the

operational parameters during different driving

modes, using an open, well-known analysis

framework (Ezzini et al., 2018). This is

complemented by the explanation of the correlations

identified from the perspectives of fuel efficiency,

and engine preservation.

The structure of the paper is as follows: Section 2

discusses the methodology, describing the

operational parameters to be monitored, the event-

recognition rules, and the correlation analysis

framework. Section 3 describes the identification of

driving events per driver, and the association with the

driving style. It identifies correlations among the

operational parameters under different driving

modes. Section 4 discusses the results achieved, the

conclusions and the planning for future work.

2 METHODOLOGY

2.1 Datasets and Equipment

We have initially considered the dataset consisting of

OBD-II readings from 10 drivers, performing a

specified route, starting from Korea university to

Seoul world cup stadium and vice versa. Each driver

has completed about 46 km of both residential and

high road mostly with light traffic. The vehicle

employed has been KIA Soul (1600T) with automatic

transmission (the dataset is referred as ‘KIA dataset’).

The total duration has been 23 hours resulting in over

90,000 values. The OBD-II scanner CarbigsP has

been used with Android software. We have observed

that the driving pattern of drivers #1, #3, #4, #7, and

#10 has more frequent events of interest and less

idling segments. For each of them a set of 500 values

has been used per trip including all interesting states.

As the dataset lacks labelling in terms of driving

mode (for example cruising, idle, acceleration), we

have created our own dataset consisting of these

modes. For each of these modes we have investigated

the correlation among operational parameters. The

new (own) dataset has been captured on a Toyota iQ

1000cc (1KR-FE engine) model with a manual

gearbox. The dataset has been based on a single trip

and a single driver. The test has been performed so

that the driving conditions and the temperature (25

Celsius) are similar to those of existing dataset. The

distance covered, has been a 5-kilometer residential

roadway with mixed traffic. ELM327 scanner with

ScanMaster 2.1 software has been utilized, connected

through cable to a laptop with quad-core CPU, 8GB

RAM and 256GB of SSD drive. Sampling has been

performed with fixed time intervals of 0.8sec.

2.2 Selection of Engine Operation

Parameters

The selection of engine parameters has been based on

their importance in the engine operations and their

fundamental nature. They refer to a physical measure,

while other parameters are dependent upon them.

They are also common, and they can be retrieved

using typical OBD-II scanners. The first objective has

been whether the selected parameter set, and relevant

processing can lead to a) the characterization of

driving style, and b) the understanding of the vehicle

status for preventive maintenance and error

diagnostics. These parameters include the following,

and they are summarized in Table 1along with the min

and max values.

1. The Short-Term Fuel Trim (STFT) as the fuel

adjustment by the ECU to handle engine load.

2. The Manifold Absolute Pressure (MAP),

revealing the pressure of the air intake.

3. The Absolute Throttle Position (ATP), as the

percentage of the acceleration pedal pressed

by the driver

4. The engine speed as Revolutions Per Minute

(RPM), indicating the motor stress.

5. The Calculated Engine Load (CEL).

6. The Engine Coolant Temperature (ECT) in the

engine block.

7. The Vehicle Speed, in Km per hour.

8. The Fuel Consumption and the Instant Fuel

Consumption as a common value.

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258

9. The Lambda Equivalence Ratio (LER), as the

air-fuel ratio.

10. The Catalyst Temperature (CT), the catalytic

converter temperature in degrees Celcius.

Table 1: Selected operational parameters for automatic-

manual gearbox, including minimum and maximum values.

Parameter Min Max Unit

Automatic transmission (KIA Dataset)

STFT -9 10.2 %

MAP 0 145 kPA

ATP 18 84.3 %

RPM 648 5,529 rpm

CEL 18 97 %

ECT 86 99

C

Vehicle s

eed 0 111 Km/h

Manual transmission (Own Dataset)

STFT -10 6.5 %

MAP 17 95 kPA

ATP 14.9 40 %

RPM 738 3,869 r

CEL 20 96 %

ECT 80 90

C

Vehicle s

0 72 Km/h

LER 0.83 1.24 -

CT 502 650

C

The measurements series include the selected

parameters, the timestamp, and the driver. The

Lambda Equivalence Ratio (LER) variance has been

(statistically) insignificant, as new engines keep the

air-fuel ratio close to stoichiometric value (LER≈1),

so LER has not been considered further.

2.3 Event Identification Rules

Each driving set consists of discrete, sequential

events, related to the driver intentions, the engine and

road conditions. While they do not form an exclusive

list, typical events include a) the gear change, b) the

acceleration and c) the engine idling. The

identification of such events (framed with the

‘default’ cruising conditions) can quantitatively

characterize a driving session.

For the event identification, the timeseries are

sequentially split into small (time) segments with hop

length of 1 for gear change and of 2 for idle and

acceleration. The behaviour of specific parameters is

checked per case, typically comparing the difference

of the values between the beginning and the end of

each segment. The objective has been to make the

The Load Value measured from CarBigsP app

corresponds the Calculated Engine Load (CEL).

rules straightforward and involve only the necessary

parameters.

The overall workflow for a) event identification

and b) the calculation of the operational parameter

correlation is presented in Figure 1.

Figure 1: Workflow for event identification and engine

operation parameter correlation.

Gear change is characterized by a mild drop in

RPM and Vehicle Speed and a decrease in throttle

position (approaching the idle throttle position).

These observations are expressed as relationships (1),

(2) and (3). Timepoint START is the value at the

beginning of the segment and END denotes the

corresponding value at the segment end.

RPM(START)-RPM(END)>0.06*RPM(END)

(1)

VS(START)-VS(END)≥VSdrop,

with VSdrop=0

(2)

Thr(END)-ThrAtIdle < 0.6*ThrIdle,

with ThrAtIdle=18

(3)

Acceleration is characterized by increased MAP,

throttle position and Calculated Engine Load (CEL)

values. For each segment, their mean values are

compared with a threshold MAP, throttle position and

Calculated Engine Load (CEL)

values. E.g.

mean(blockMAP(:)) denotes the mean of all MAP

values of the segment (block). Another condition is

the increasing vehicle speed (implemented through

the verification that the values are ‘sorted’).

mean(blockMAP(:))≥ minAccelMAP,

with minAccelMAP=40

(4)

(mean(blockThr(:))≥ minAccelThr,

with minAccelThr=20

(5)

(mean(blockALV(:))≥ minAccelALV

with minAccelALV=4

(6)

issortedrows(blockVS(:))

Driving Events Identiﬁcation and Operational Parameters Correlation based on the Analysis of OBD-II Timeseries

259

Idle is characterized by lower RPM values than a

threshold.

mean(blockRPM(:))<=RpmIdleMax

(7)

While these rules can be applied upon different

vehicle models, custom weights and thresholds may

apply. The implementation consists of a) timeseries

segmentation and b) rule validation with the aim to

identify the events. The implementation platform has

been Matlab 2021.

2.4 Calculation of the Correlation

among Operational Parameters

Engine operation parameters are measured from

different modules and pertain to different physical

quantities, including temperature, engine load,

acceleration, speed, and fuel consumption. Some of

them are associated with each other, as they monitor

the operation of a single engine, while others are

independent. The correlation among them can also

change, depending on the driving mode, i.e., cruising,

acceleration and idle. Verification of correlation (or

lack of it) can pave the way for additional verification

of measurements, rules for anomaly detection as well

as prediction models.

To identify the correlation among different

parameters, we employ timeseries analysis methods

to identify and evaluate the relationship and

correlation among engine parameters under different

driving conditions. Specifically, we extract and

compare a set of 22 representative features from each

of the measured parameters using the catch22

framework. Such features should have strong

classification performance and minimal redundancy.

They have been selected from a superset of 4791

features from the highly comparable timeseries

analysis toolbox using statistical prefiltering,

performance filtering and redundancy minimization

(Lubba et al., 2019). The features include histograms,

period between successive extreme events, minimum

of autocorrelation function, centroid of Fourier power

spectrum, time-reversibility.

After the extraction of the features, the Euclidean

distance per feature pairs has been calculated and

normalized in [0, 1]. If the distance is closer to zero,

the correlation is stronger and vice versa (distance

closer to 1 denotes independence between the

parameters).

3 RESULTS

3.1 Driving Events Identification

3.1.1 Automatic Gearbox

Regarding gear change identification, we have

compared the events resulting from the rules with

ground truth, considering the Continuously Variable

Transmission (CVT) gearbox and the manual

transmission. CVT consists of two variable-diameter

pulleys driving each other via a belt (Jawad and Ali,

2020). The diameter ratio of the pulleys determines

the current gear ratio. Hence, no gears are required

achieving smooth shifting and high durability.

Possible adjustments involve intake pressure and load

values criteria, as well as the fact that gear change in

automatic transmission does not involve withdrawal

of the acceleration pedal. However, the risk of

converging with deceleration status can affect

classification accuracy.

Table 2 presents the event identification results as

applied on the dataset with automatic gearbox (KIA).

In each cell the first number is the number of the

identified events, the second is the number of

correctly identified (true positive) and the third the

actual number of events.

Table 2: Events identified in the automatic transmission per

driver (KIA dataset).

Gear Changes Accelerations Idles

D1 127 (126,134) 47 (46,50) 7 (6,7)

D3 158 ( 155,163) 57 (55,60)_ 2 (2,2)

D4 182 (180,187) 21 (19,22) 12 (11,13)

D7 75 ( 72,79) 25 (24,27) 5 (5,5)

D10 164 ( 160,170) 48 (46,50) 9 (8,9)

Total 706 (693,733) 198 (190,209) 35 (32,36)

Regarding accelerations, the position of the

acceleration pedal is affecting air intake and load

values with these three parameters closely related.

The time intervals of acceleration and the absolute

throttle value suggests the style of acceleration (mild

or abrupt). The accuracy reaches 91%, while the rest

(9%) is recognized as cruising condition, as the driver

accelerates at a very slow rate with low RPM, for fuel

efficiency. However, the recognition of these patterns

is challenging even for an experienced engineer.

Regarding idle identification, 89 % accuracy was

achieved, with errors found at low load and throttle

values condition correlation. Idle state is identified

when the throttle position is stable (13.3 to 14.7%).

Challenges emerge at low engine temperature like

cold start, where engine runs richer to achieve optimal

operating temperature quickly (Zheng et al., 2020).

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260

Additionally, idling in traffic is initially accompanied

with higher engine load, because the brake booster is

filling with air to enhance braking potential.

Regarding gear changes reaches 95%. With

automatic cars, if a steady vehicle speed increment is

achieved, gear changes progress normally, so engine

speed is sustained at optimal range and torque (Malik

and Nandal, 2021). This is the main pattern, affecting

fuel consumption and extending engine life, provided

that those accelerations are not sharp with increased

throttle values.

In terms of driving characterization, accelerations

and gear changes can be associated with aggressive

driving. Drivers 1, 3 and 10 have similar driving

styles according to accelerations, while they differ in

the frequency of gear changes. This is affecting fuel

consumption and engine stress. Drivers 4 and 7 have

similar acceleration patterns with different gear

change sequence, which implies aggressive driving

for driver 7. For the experiment, idle suggest stopping

at traffic lights.

3.1.2 Manual Gearbox

For manual transmission we have considered three

typical driving modes a) cruising, b) acceleration,

employing our own dataset (Toyota IQ). The values

are sampled with a period of 800 ms. The cruising set

is characterized by relatively long periods of stable

speed with none to mildly active throttle, while the

acceleration style being characterized by repeating

acceleration (from 0 to approximately 70km/h). The

dataset consists mainly of cruising and acceleration

(with idle status only in the beginning of the trip).

Both aggressive and mild accelerations are taken into

consideration. Τhe duration of cruising has been

double that of acceleration. The gear changes have

been recorded as ‘ground truth’ and compared with

events identified by the code.

Table 3 indicates the identified interesting events

per driving mode. The number of accelerations is

equivalent, while the individual gear changes are

approximately 50% less frequent in the acceleration

driving mode. This lower frequency can be explained,

as the car has been tested specifically for performance

(so higher engine speed is required for more

horsepower).

Table 3: Events identified in manual transmission per

driving mode (own dataset).

Gear Changes Accelerations Idles

Cruising

(6.54 min)

37 (36,40) 20 (18,22) 1 (1,1)

Accel

(3.17.5 min)

19 (18,21) 15 (14,17) 1 (1,1)

Total 56 (54,61) 35 (32,39) 2 (2,2)

The corresponding accuracies for gear changes is

88.5%, for accelerations 82% and for idles 100%. In

principle, the impact upon the operational parameters

due to a gear change is less intense in the manual

transmission and this proves that a fine-tuning of the

weights may achieve better accuracy. Driving with a

manual gearbox, requires specific timing in shifting

to avoid damaging the gears and clutch. The driver

should weigh in the vehicle speed, road incline (if

available) and engine load to shift (change gear)

within optimal torque range, for maximum

performance and fuel efficiency. Even in this case,

engine speed (RPM) drop is adequate and easily

noticeable while in automatic transmission

everything is smoothly handled by the control module

with no interruption.

3.2 Fuel Consumption Calculation

To calculate the fuel consumption, since the MAF

sensor is not present, the MAP value equivalent is

used. Hence, the following equations (8) – (11) are

used for automatic and manual transmission

(Meseguer et al., 2017; Lightner, 2011):

IMAP= 𝑅𝑃𝑀 ∗





(8)

MAF=





∗





∗𝐸𝐷∗





(9)

Fuel flow (l/hr) =

∗,

∗

(10)

Fuel Cons. (lt/100km) =

 

 

∗ 100 (11)

IMAP: Intake manifold Sub-parameter

MAF: The equivalent mass air flow in g/s

IAT: Ambient temperature, for KIA dataset of 25C,

based on weather reports according to stated season

VE: Volumetric efficiency of the engine – a typical 85%

for both new engines applied (Nutter, 2017)

ED: Engine displacement – 1,591 for Kia Soul and 997

for Toyota iQ (UltimateSpecs, 2021)

MM: Average molecular mass of air (28.97 g/mole)

R: Ideal Gas Constant (8.314 J/ K * mole)

AFR: Stoichiometric Air Fuel Ratio (14.7)

Fd: Gasoline fuel density, 770kg/m

(Acea, 2019)

At Idling, the fuel consumption is not meaningful

to calculate as the vehicle speed equals to zero, and

only fuel flow is computed. In our previous work, it

Driving Events Identiﬁcation and Operational Parameters Correlation based on the Analysis of OBD-II Timeseries

261

has been verified that the calculated fuel consumption

values are aligned (as mean) with those retrieved

from OBD-II (with a percentage of 99%). In Section

4, we discuss the fuel consumption measurements in

relation with the driving style.

3.3 Correlations among Engine

Operation Parameters

As discussed in Section 2.2., the following

parameters have been considered for the estimation of

the correlation: 1) Calculated Load Value (CEL), 2)

Engine Coolant Temperature (ECT), 3) STFT, 4)

Intake MAP, 5) Absolute throttle Position (ATP), 6)

Lambda Equivalence Ratio (LER), 7) Catalyst

Temperature (CT), 8) Engine RPM, and 9) Vehicle

Speed. Table 4 presents the five more and the five less

correlated parameters in pairs for the three situations

(cruising, idle and acceleration). Each row includes

the correlated pair, while the number in parenthesis is

the ‘correlation index’ belonging to [0, 1]. The closer

the index is to zero, the more significant the

correlation, while 1 indicates independence of the two

variables.

Table 4: Correlation calculated in the three driving modes.

Cruise Idle Accel

Most correlated parameters (decreasing correlation)

#1 MAP, STFT (0.063) RPM. LER

(0.11)

MAP, CEL

(0.015)

#2 MAP, ATP (0.12) RPM, MAP

(0.18)

MAP, ATP

(0.034)

#3 MAP, LER (0.13) LER, MAP

(0.22)

MAP, LER

(0.047)

#4 STFT, LER (0.14) CT, ATP (0.35) ATP, CEL

(0.048)

#5 STFT, ATP (0.16) CT, CEL (0.47) CEL, LER

(0.056)

Least correlated parameters (decreasing independence)

#1 STFT, CT (1) ECT, MAP (1) CEL, CT (1)

#2 STFT, RPM (1) ECT, ATP (1) RPM, LER (1)

#3 STFT, Speed (1) ECT, LER (1) Speed, LER (1)

#4 ECT, LER (0.99) ECT, CT (1) CT, LER (0.99)

#5 ECT, MAP (0.98) ECT, RPM (1) CT, ATP (0.95)

In cruising, the highest correlation is identified

between MAP and the parameters STFT, ATP

(Absolute Throttle Position) and LER (Lambda

Equivalence Ratio). Similarly, STFT correlates with

LER and ATP. On the other hand, the CT (Catalyst

Temperature), the RPM and the Speed appear

independent of the STFT, where the lack of

correlation almost reaches the maximum value (i.e.,

1). CT is also independent of LER and MAP.

In the idle status, the correlation is decreased due

to its distinctive features. Indeed, during idling there

is a constant fuel flow via a forced operation, the

battery is not charging properly, engine lubrication is

deficient, and the catalytic converter has low

temperature. During acceleration strongest

correlations appear. MAP is correlated with CEL, the

ATP and the LER. Similarly, the CEL is correlated

with the ATP and the LER.

4 DISCUSSION

In this section, we combine the observations of

Sections 2 and 3 to discuss the impact of the driving

style on fuel consumption and vehicle lifecycle. Table

5 includes the mean values (from KIA dataset) of

MAP, RPM, speed, load (CEL), ATP and fuel

consumption.

Table 5: Engine operation parameters (mean values) and

fuel consumption.

Engine operation parameters and fuel consumption

(mean values)

Num. MAP RPM Speed CEL ATP Fuel

1 62.1 1,701.4 37.8 48.3 24.2 12.4

3 38,2 1,877.4 52.6 50.7 28.3 7

4 13.2 1,311.5 21.7 38.9 22.7 5.16

7 53.9 1,642 40.5 42.5 20 9.68

10 56.1 1,743.4 43,3 40.8 23.7 10.3

uis 36 2,064.3 38.3 38 19 5.2

Acc. 47,6 2,097.2 39.5 50.1 22 7.4

Driver 1 has the most ‘aggressive’ driving

behaviour according to Tables 4 and 5. Although

MAP value is not the highest, the engine speed

reaches a maximum of 5.5k RPM which is high and

typically contributes to higher fuel consumption (as

indicated in Table 5). The mean value of engine load

is constrained due to idles states (7 idles have been

identified, e.g., due to traffic). Driver 4 is softer on

acceleration, achieving a larger number of gear

changes so engine is kept at optimal range. We notice

that minimum MAP value is zero, like driver 3 and

that is an indication that cruising was exploited to

highest level, so fuel efficiency is maximum. MAP

and ATP are strongly collated to fuel trims (STFT),

as indicated in Table 5. So, if they are kept low while

cruising, fuel consumption is limited. The analysis of

these data results in Table 6. Load values are the same

for each driver as they are associated with specific

route limitations. Drivers 3 and 4 achieve zero MAP

values as both exploit cruising status.

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Table 6: MAP, RPM, CEL and ATP minimum and

maximum values for driving style characterization.

Differentiation of Data

–

Kia Soul

Num MAP RPM CEL ATP

Min Max Min Max Min Max Min Max

1 21 99 648 5529 18 91 18 78

3 0 145 665 3692 18 91 18 82

4 0 106 657 3718 18 91 18 78

7 22 99 660 2829 18 91 18 42

10 20 100 663 3483 18 91 18 82

Considering the values of Table 6, the driving

style of each driver is characterized in Table 7. Even

though driver 3 has the highest MAP, load and

throttle values, fuel economy efficiency is second

best, as cruising status is vastly utilized. In this case,

the stressing upon the vehicle engine is limited,

contributing to the extension of the engine lifecycle.

Table 7: Driver aggression categorization.

Most aggressive driver from left to right

1 10 7 3 4

Similar pattern is followed in our own experiment

where the acceleration mode is characterized by

greater MAP, load, and ATP values. This results in

almost 50% higher fuel consumption than cruising,

up. This is aligned with analysis performed in our

previous work (Rimpas et al., 2020), where constant

acceleration has been associated with engine stressing

and decreased fuel efficiency.

5 CONCLUSIONS – FUTURE

WORK

The consideration of the OBD-II periodic readings as

timeseries measurements has allowed us to apply

relevant methodologies a) for driving event

identification and b) for parameter correlations. We

consider that such as consideration can open new

perspectives (in terms of methodologies and tools) in

engine operation monitoring and added value

services. The event identification rules have proved

to be robust, providing reliable results for different

drivers, driving modes and automatic / manual

transmission. The characterization of the driving style

has been verified through parallel calculation of fuel

consumption. Similarly, correlation of engine

operation parameters correlation per driving mode

has been coherent.

As future work we consider further elaboration

OBD-II readings. These can include the identification

of non-typical values, in the context of preventive

maintenance. Sensor operation can allow for prompt

vehicle and engine inspection and prevent future

malfunctions. The availability of readings, available

in the cloud or locally and offered in a trustworthy

manner can allow access to a complete and coherent

history of the vehicle (Voulkidis, 2022), interesting to

potential buyer and state agencies, revealing poor

maintenance or unhandled malfunctioning. From

another perspective, this work can be combined used

with more complex (and opaque) AI/deep learning

approaches for the creation of labelled timeseries and

to enhance the ‘explainability’ of the results.

ACKNOWLEDGEMENTS

The authors acknowledge financial support for the

dissemination of this work from the Special Account

for Research of ASPETE through the funding

program ‘Strengthening ASPETE’s research.

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