Online Driving Behavior Scoring using Wheel Speeds

Marian Waltereit

, Peter Zdankin, Viktor Matkovic, Maximilian Uphoff and Torben Weis

Distributed Systems Group, University of Duisburg-Essen, 47048 Duisburg, Germany

Keywords:

Driving Behavior, Aggressive Driving, Driver Feedback, Wheel Speeds, Controller Area Network.

Abstract:

We present an online scoring algorithm for measuring driving behavior using wheel speeds only. Such an

algorithm can be used to provide drivers with feedback about their driving behavior while driving in order to

reduce aggressive driving, which is a primary cause of trafﬁc accidents. Our algorithm uses a minimal data

set already available through the built-in wheel speed sensors of contemporary cars. Due to the small amount

of data used and the low computational complexity, our algorithm can easily be deployed on single-board

computers. With real driving experiments in a controlled and an uncontrolled environment, we demonstrate

the suitability of our scoring algorithm for identifying aggressive driving and assessing the driving behavior.

1 INTRODUCTION

Vehicular accidents are often caused by aggressive

driving behavior, such as extreme acceleration or de-

celeration (Luo Yong and Li Hui, 2009; Paleti et al.,

2010; Ma et al., 2019). The risk such accidents could

be reduced by giving drivers feedback on their driv-

ing behavior. Without feedback, drivers can typi-

cally only monitor the velocities of their cars to assess

whether they are within legal limits. Other physical

quantities such as the car’s acceleration are difﬁcult to

grasp while driving without further assistance. How-

ever, the acceleration of the car is another indicator of

the quality of the driving behavior, since a moderate

and steady acceleration implies a safer driving style

that endangers other drivers less. A behavioral score,

on the other hand, can be understood more intuitively

and is less of a cognitive burden for drivers. Such a

score can be calculated using physical quantities from

in-vehicle data of contemporary cars and indicates ei-

ther non-aggressive or aggressive driving behavior. If

drivers check their scores regularly, they are able to

notice reductions and adjust their behavior towards a

non-aggressive driving style to raise the score back to

a good rating. Moreover, the awareness of the indi-

vidual driving behavior can be improved.

Our contribution is an online scoring algorithm

for measuring driving behavior using wheel speeds

only. An online algorithm rates the driving behavior

while driving. In contrast, ofﬂine algorithms rate the

https://orcid.org/0000-0001-5480-8783

https://orcid.org/0000-0001-6594-326X

driving behavior retrospectively after the trip. Due to

the mandatory anti-lock braking system (ABS), wheel

speeds can be obtained from built-in wheel speed sen-

sors of contemporary cars via the Controller Area

Network (CAN bus) (Reif, 2011). As a result, our

scoring algorithm can potentially be used in a large

number of today’s cars. We identify wheel speeds

as the minimal data set adequate and required for the

purpose of driving behavior scoring. Thus, our al-

gorithm follows the principle of data minimization as

deﬁned in the EU General Data Protection Regulation

(GDPR) (Council of the European Union and Euro-

pean Parliament, 2016). The small amount of data

used and the low computational complexity make our

algorithm easy to deploy on single-board computers.

The rest of this paper is organized as follows. We

ﬁrst discuss related approaches for measuring driving

behavior in Section 2. In Sections 3 and 4, we de-

scribe the system model and introduce the kinematic

car data used in our paper. We present our scoring al-

gorithm in Section 5. In Section 6, we evaluate our

scoring algorithm with real driving experiments in a

controlled and an uncontrolled environment. Finally,

we conclude the paper in Section 7.

2 RELATED WORK

In behavioral science, the deﬁnition of aggressive

driving is manifold. As Dula et al. (Dula and Geller,

2003) point out, the term is used in different contexts.

In psychology, the term is used to refer to three types

Waltereit, M., Zdankin, P., Matkovic, V., Uphoff, M. and Weis, T.

Online Driving Behavior Scoring using Wheel Speeds.

DOI: 10.5220/0009215604170424

In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020), pages 417-424

ISBN: 978-989-758-419-0

417

of aggressive driving behavior: 1) acts of bodily or

psychological aggression towards other road users, 2)

negative emotions while driving, and 3) risk-taking

driving behavior without intent to harm other road

users. In this paper, we refer to the third type of driv-

ing behavior since it is a measurable behavior that is

reﬂected in in-vehicle data from the CAN bus. Exam-

ples for the third driving behavior type are weaving in

and out of trafﬁc, speeding or changing speed unpre-

dictably (James, 2009).

Several approaches for measuring driving behav-

ior have already been proposed. The methodology

used include questionnaires, fuzzy logic and machine

learning (Imkamon et al., 2008; Castignani et al.,

2015; Ma et al., 2019; Carfora et al., 2019). In the

following, we focus on scoring-based approaches, as

these are most related to our work.

Castignani et al. (Castignani et al., 2015) proposed

a smartphone-based driver proﬁle platform. While

they use accelerometer, magnetometer and gravity

sensor readings as well as GPS data to detect driving

events ﬁrst, information about the weather and time of

day is used to calculate a score based on the events.

The detection of driving events is based on a fuzzy

inference system. However, due to the use of smart-

phones, their approach requires a calibration phase

based on statistical analysis to determine the thresh-

olds for the fuzzy inference system. We use an adap-

tive threshold that is based on physical limitations of

car dynamics and does not require a calibration phase.

Bergasa et al. (Bergasa et al., 2014) developed an

app for smartphones to warn inattentive drivers while

evaluating and scoring driving behavior. They use a

variety of sensors and integrated hardware such as

camera, microphone, GPS and inertial sensors. The

resulting data is used to calculate two types of scores.

The ﬁrst score describes the drowsiness of the driver,

which is calculated from the camera shots using im-

age processing. The second score represents and rates

the distraction of the driver using inertial sensors.

However, the smartphone is used as a ﬁxed vehicle-

mounted device, i.e. the axis of the smartphone’s

acceleration and gyroscope sensors must be aligned

with the corresponding axis of the car. Such a setup

is susceptible to operating errors and external inﬂu-

ences, which can lead to undesirable problems such

as incorrect scoring. In contrast to our approach, they

use ﬁxed thresholds for detecting and rating driving

events. In addition, we use in-vehicle data and do not

require inertial sensors of a smartphone.

Eboli et al. (Eboli et al., 2016) proposed a method-

ology to analyze driving behavior using velocity as

well as longitudinal and lateral accelerations obtained

from a smartphone with GPS to distinguish safe

from unsafe driving behavior. Then, they extended

the methodology by incorporating vertical accelera-

tion (Eboli et al., 2019). In contrast to the in-vehicle

data used in our work, GPS is not always available,

e.g. in tunnels. Nevertheless, in our scoring algorithm

we utilize the safety threshold introduced by Eboli et

al. (Eboli et al., 2016), which is based on physical

limitations of car dynamics.

Carfora et al. (Carfora et al., 2019) proposed an

approach to characterize driving behavior using un-

supervised classiﬁcation algorithms such as k-means.

They calculate aggressiveness indices that are used to

derive a risk index. For this, they use a total of 10

features from CAN bus and GPS sensor readings, e.g.

engine revolutions per minute (RPM) and accelera-

tion. The GPS-based features are used to determine

the type of road and the time at which the car was

driven. Yet again, the problem is that GPS is not al-

ways available.

Abdelrahman et al. (Abdelrahman et al., 2018)

presented a data-driven approach that uses machine

learning algorithms to predict a driver’s accident risk

probability. They calculate the risk probability on the

basis of 12 driving behavior features, such as sudden

braking, already included in the naturalistic driving

data set used. Based on the risk prediction they cal-

culate a ﬁnal driver’s risk score. However, not all of

the features used can be obtained from in-vehicle data

and require external information such as speed limits.

In contrast to our approach, most of the aforemen-

tioned existing approaches require data from various

sensors for measuring driving behavior. Our objective

is to provide a scoring algorithm with a low compu-

tational complexity that uses only a minimal data set

obtained from the car’s CAN bus. Hence, our algo-

rithm follows the principle of data minimization as

deﬁned in the GDPR (Council of the European Union

and European Parliament, 2016).

In above context, Kar et al. (Kar et al., 2019) pro-

posed a scoring algorithm that uses gyroscope and

RPM readings as the minimal data set for scoring

driving behavior. This data is available in all car mod-

els through the on-board diagnostics port. However,

the data set used is less minimal than in our approach.

Using time series forecasting methods, they predict

future gyroscope and RPM values in order to identify

anomalies, i.e. changes in driving behavior. Finally,

they calculate a score based on the prediction errors.

3 SYSTEM MODEL

We assume a system with the following four com-

ponents: a driver, a car, a scoring device, and a dis-

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

418

play. The driver drives a car equipped with a scor-

ing device. This scoring device is capable of calcu-

lating a driving behavior score using wheel speeds

only. As a result, external information such as trafﬁc

conditions or speed limits are not required. The scor-

ing device is connected to the car’s high speed CAN

bus and waits for wheel speed messages broadcasted

over the CAN bus. Using methods as introduced

by Marchetti et al. (Marchetti and Stabili, 2019), the

identiﬁer of wheel speed messages can be automat-

ically identiﬁed. This is useful, as this information

is not standardized for private transport and usually

not published by manufacturers. Using wheel speeds,

the scoring device calculates and updates the driver’s

score while driving. A display is connected to the

scoring device and displays the score in order to give

the driver feedback about his or her driving behavior.

Based on the feedback, the driver is able to improve

his or her driving behavior in order to avoid accidents.

4 KINEMATIC CAR DATA

We utilize time-stamped wheel speeds from the car’s

CAN bus to calculate the kinematic car data. We de-

note the right and left front wheel speeds as w

(t) and

(t). Accordingly, w

(t) and w

(t) represent the

speeds of the right and left rear wheels. We denote a

wheel speed measurement W (t) at time t as:

W (t) = (w

(t), w

(t)), (1)

where the wheel speeds are in ms

−1

We estimate the car’s velocity v(t) at time t by the

mean of the right and left rear wheel speeds w

(t) and

(t) (Carlson et al., 2002):

v(t) =

(t) + w

(t)

(2)

We estimate the yaw rate r(t) of a car at time t

using the car’s rear track width T and the right and

left rear wheel speeds w

(t) and w

(t) (Carlson et al.,

2002):

r(t) =

(t) − w

(t)

(3)

The ﬁrst derivative of the velocity v(t) is the lon-

gitudinal acceleration a

lon

(t). We estimate the car’s

lateral acceleration a

lat

(t) using the velocity v(t) and

the yaw rate r(t), neglecting the sideslip angle (Chen

et al., 2016):

lat

(t) = v(t) · r(t) (4)

The acceleration vector a(t) includes the longitu-

dinal and the lateral acceleration at time t as:

a(t) = (a

lon

(t), a

lat

(t)) (5)

We calculate the orientation-independent total ac-

celeration ka(t)k as the magnitude of the acceleration

vector a(t):

ka(t)k =

lon

(t)

+ a

lat

(t)

(6)

5 SCORING ALGORITHM

We introduce a driving behavior score between 0 and

100 points. A score of 0 points indicates that the driv-

ing behavior is consistently aggressive and a score of

100 points indicates that the driving behavior is con-

sistently non-aggressive. This way the driving behav-

ior can be monitored throughout the trip and drivers

can receive feedback on their respective driving be-

havior. In order to calculate the score, we use wheel

speeds which are typically available at 100 Hz on the

car’s CAN bus. However, the frequency may vary de-

pending on the manufacturer. In this case, we resam-

ple the wheel speeds to 100 Hz.

For our scoring algorithm, we choose a window-

based approach. Based on our experiments, we use

non-overlapping windows ω

with a window size of

1 s. However, if the average velocity of a window is

less than 5 km h

−1

, we discard that window because

the car is idling or barely moving. For each window,

we calculate a window score based on the driver’s cur-

rent driving behavior. Each window score contributes

to the overall driving behavior score.

For each non-overlapping window ω

, we cal-

culate the car’s total acceleration ka(t)k (see Equa-

tion (6)). The total acceleration includes both driving

straight ahead and turning, as it is made up of longitu-

dinal and lateral acceleration. As a result, the total ac-

celeration is particularly suitable for measuring driv-

ing behavior, since accelerations, decelerations and

turnings are sufﬁcient to represent all types of driv-

ing maneuvers (Van Ly et al., 2013).

To measure driving behavior based on the total

acceleration ka(t)k, we leverage a safety threshold

(denoted as θ

) that is based on the physical limita-

tions of car dynamics and was introduced by Eboli et

al. (Eboli et al., 2016). The safety threshold θ

(in

−2

) is calculated using the car’s velocity v(t) (in

kmh

−1

= g ·

0.198 ·



v(t)

100



− 0.592 ·

v(t)

100

+ 0.569

(7)

where g is the gravitational acceleration on Earth

and v(t) ≤ 150 kmh

−1

. The safety threshold value

is deﬁned for velocities up to 150 kmh

−1

(Eboli

et al., 2016). Hence, we use the safety threshold of

150 kmh

−1

for velocities greater than 150 km h

−1

Online Driving Behavior Scoring using Wheel Speeds

419

100

0 0.2 0.4 0.6 0.8 1 1.2

Window score s

Mean quotient ρ

‾‾

of window ω

Piecewise scoring function

Figure 1: Piecewise scoring function used in this paper for

calculating the window score.

The safety threshold θ

deﬁnes a safety domain in

which the total acceleration ka(t)k is considered safe,

i.e. it is physically safe to drive the car under these

conditions (Eboli et al., 2016):

ka(t)k < θ

(8)

If the total acceleration ka(t)k exceeds the safety

threshold θ

, driving is considered unsafe. In gen-

eral, an unsafe driving situation is due to aggressive

driving (Eboli et al., 2016).

For each time step t of the window ω

, we calcu-

late the quotient of total acceleration ka(t)k and safety

threshold θ

(denoted as ρ

ka(t)k

(9)

The quotient ρ

indicates how close the driving behav-

ior is to a physically unsafe driving situation at time

step t. The arithmetic mean of all quotients ρ

of the

window ω

is denoted as ρ

We use the mean quotient ρ

of the window ω

for

calculating the window score s

∈ [0, 100] that indi-

cates the current driving behavior. In detail, we cal-

culate the window score s

∈ [0, 100] by the following

piecewise function (referred to as scoring function):

(

100 · (ρ

− 1)

0 ≤ ρ

< 1

0 otherwise

(10)

The scoring function is depicted in Figure 1. This

scoring function allows to account for the closeness

of the driving behavior within the window to a physi-

cally unsafe driving situation. If the driving behavior

is most safe (i.e. least aggressive), the window score

is close to 100 points. In turn, the window score is

close to 0 points if the driving behavior is most un-

safe (i.e. most aggressive). The less aggressive the

driving behavior, the faster the score increases. This

should motivate drivers to drive less aggressive.

As mentioned before, each window score con-

tributes to the overall driving behavior score. We cal-

culate the overall driving behavior score s

∈ [0, 100]

at time t as the arithmetic mean of all window scores

, . . . , s

calculated up to time t:

∑

j=1

(11)

By using the mean of the window scores, we consider

the behavioral history of a driver throughout the entire

trip. This leads to a fair score, as drivers who have

driven non-aggressive for a long time do not risk their

good scores immediately if they drive aggressive for a

short term. Vice versa, this also applies to aggressive

drivers who drive non-aggressive in the short term.

6 EVALUATION

In order to evaluate our online scoring algorithm, we

ﬁrst conduct a driving experiment in a controlled en-

vironment at our university. Then, we use a freely

available data set (Kwak et al., 2016) recorded in a

driving experiment with ﬁve drivers in Seoul to eval-

uate our algorithm in an uncontrolled environment.

6.1 Controlled Environment

In this section, we examine whether our scoring al-

gorithm can identify aggressive driving behavior. For

this, we conduct a driving experiment in a controlled

environment where the drivers complete a test course

under time pressure. In general, hurried drivers tend

to drive more aggressively (Fitzpatrick et al., 2017).

Thus, we expect a driver’s score to be lower when

the driver is under time pressure. If this is the case,

our scoring algorithm can identify aggressive driving

behavior. Below, we ﬁrst describe the setup of our

driving experiment. Then, we present and discuss the

results.

6.1.1 Experimental Setup

In order to examine whether the driving behavior

score is lower when driving under time pressure, we

set up a test course on the university parking lot. The

test course is visualized in Figure 2 and measures

about 350 m. On this test course, the drivers have to

drive twice through a slalom course and have to make

a change of direction once.

A total of ﬁve drivers participate in this experi-

ment at daytime in rainy weather conditions. Each

driver drives the test course three times. There is

no time limit for the ﬁrst trip and the drivers are in-

structed to drive in a manner appropriate to them-

selves. However, the time needed to complete the

ﬁrst trip is measured. Based on this time, a time limit

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

420

Figure 2: Test course on the university parking lot including

two slalom sections and a change of direction.

is set for the two following trips. The time limit of

the second trip is 90% of the measured time. For the

third trip, the time limit is 75% of the measured time.

The drivers are instructed to complete the test course

within the respective time limits. During the second

and the third trip, the drivers are informed about the

remaining time. However, the driving behavior score

is not displayed to the drivers in any of the three trips

in order to avoid inﬂuencing the driving behavior.

Throughout the experiment, a Raspberry Pi 2

equipped with a PiCAN2 board

is connected to the

car’s high speed CAN bus. To ensure a reproducible

experimental setup, we record the entire CAN bus

data while driving and replay the CAN log ﬁle to a vir-

tual CAN interface on the Raspberry Pi 2 afterwards.

We prototyped our scoring algorithm in Python and

all calculations are performed on a Raspberry Pi 2

while replaying the CAN log ﬁle.

6.1.2 Results

Table 1 summarizes the results of our driving exper-

iment in a controlled environment. For each trip of

each driver, the table shows the measured time, the

time limit, the overall driving behavior score at the

end of the trip (see Equation (11)). Furthermore, the

table provides the arithmetic mean of the overall driv-

ing behavior scores weighted by the measured times

for each driver.

All drivers reduce their driving times from the ﬁrst

to the second and from the second to the third trip

while keeping to the time limits. The average driving

time for the ﬁrst trips is 74 s. For the second and third

trip, the average driving time reduces to 56 s and 50 s

http://skpang.co.uk/catalog/pican2-canbus-board-for-

raspberry-pi-23-p-1475.html (accessed November 29,

2019)

A/2

A/3

B/2

B/3

C/2

C/3

D/2

D/3

E/2

E/3

Times smaller than score of 1st trip

Driver/Trip

Score of 2nd trip

2.09

5.38

3.06 3.06

3.41

Score of 3rd trip

5.22

9.91

3.99

7.39

5.69

Figure 3: Visualization of the change in driving behavior

during the second and third trip of each driver, i.e. how

many times the scores of the second and third trips are

smaller compared to the driver’s score of the ﬁrst trip.

respectively. Based on the ﬁrst trip, we determine the

time limits for the subsequent trips. The time limits

range from 56 s to 75 s for the second and 47 s to 62 s

for the third trip.

As there is no time limit for the ﬁrst trip and the

drivers are instructed to drive in a manner appropriate

to themselves, we can use the overall driving behavior

score of the ﬁrst trip as a baseline to measure the in-

dividual change in driving behavior in the second and

third trip for each driver. For this, we determine how

many times the overall driving behavior scores of the

second and third trips are smaller than the score of the

ﬁrst trip. Figure 3 illustrates the individual change in

driving behavior of each driver. The respective over-

all driving behavior scores of each driver are given in

Table 1. The individual driving behavior of driver B

changes the most in both trips towards an aggressive

driving style compared to all other drivers. The driv-

ing behavior scores of driver B’s second and third trip

(3.74 and 2.03 points) are 5.38 and 9.91 times smaller

than driver B’s baseline score (20.12 points). In the

second trip, driver A’s driving behavior changes least

compared to the other drivers, i.e. by a factor of 2.09

from 11.5 to 3.37 points. For driver C, the individual

driving behavior changes similarly in the second and

third trip. The driving behavior score decreases by

a factor of 3.06 from 31.06 points to 10.16 in driver

C’s second trip. In the third trip, the driving behav-

ior of driver C changes by a factor of 3.99 from 31.06

to 7.79 points. Hence, driver C’s driving behavior is

almost constant during the second and third trip.

In addition to the individual change in the driv-

ing behavior, we also compare the driving behavior

of the drivers with each other. For this, we use the

weighted means of the overall driving behavior scores

given in Table 1. In terms of aggressive and unsafe

driving, driver E has the worst driving behavior in

all three trips with a weighted mean score of 6.18

Online Driving Behavior Scoring using Wheel Speeds

421

Table 1: Results of our experiment in a controlled environment. For each driver, the table shows the measured times, the time

limits, the overall driving behavior scores at the end of each trip and the arithmetic mean of the driver’s scores weighted by

the measured times.

Driver Measured time Time limit Overall driving behavior score Weighted mean score

1st trip 2nd trip 3rd trip 2nd trip 3rd trip 1st trip 2nd trip 3rd trip

A 83 s 67 s 56 s 75 s 62 s 32.26 15.47 6.18 19.71

B 69 s 50 s 47 s 62 s 52 s 20.12 3.74 2.03 10.06

C 80 s 55 s 52 s 72 s 60 s 31.06 10.16 7.79 18.44

D 77 s 55 s 50 s 69 s 58 s 32.39 10.57 4.38 18.1

E 62 s 51 s 45 s 56 s 47 s 11.5 3.37 2.02 6.18

points, followed by driver B with a weighted mean

score of 10.06 points. Drivers C and D have a compa-

rable aggressive driving behavior with weighted mean

scores of 18.44 and 18.1 points respectively. Overall,

driver A’s driving behavior is the least aggressive with

a weighted mean score of 19.71 points.

In summary, the driving behavior scores decrease

with decreasing time limits for all drivers. Thus, a

lower score reﬂects a more aggressive driving behav-

ior, as driving behavior tends to be more aggressive

under time pressure (Fitzpatrick et al., 2017). This

shows that our scoring algorithm is able to identify

aggressive driving.

6.2 Uncontrolled Environment

In this section, we evaluate whether our scoring al-

gorithm correctly assesses driving behavior in an un-

controlled environment, i.e. when the drivers were

not instructed by us and the trips were performed in-

dependently of our work. In particular, we compare

our online scoring algorithm with an ofﬂine cluster-

ing approach to examine whether our algorithm yields

similar results. Below, we describe the experimental

setup and present the results.

6.2.1 Experimental Setup

We use wheel speeds from a freely available data set

recorded in a driving experiment with ﬁve drivers in

Seoul (Kwak et al., 2016). Each driver completed

four comparable trips (about 5.5 km each) in an urban

area, resulting in a total of 20 trips. The wheel speeds

were recorded at 1 Hz during driving. We resample

the wheel speeds to 100 Hz by linear interpolation and

calculate the kinematic car data as described in Sec-

tion 4. However, for one of the trips no wheel speed

data was recorded, thus we can only use 19 of the trips

in our evaluation.

6.2.2 k-Means Clustering

The freely available data set does not contain any in-

formation about the driving behavior of the drivers

during the trips. However, clustering algorithms are

well established to group drivers and their trips ac-

cording to their driving behavior (Mainardi et al.,

2018; Fugiglando et al., 2019; Mantouka et al., 2019).

Thus, we label the driving behavior of the trips based

on k-means clustering, i.e. we group the trips accord-

ing to their underlying driving characteristics. We use

the clustering results to evaluate the results of our on-

line scoring algorithm.

The feature vector of each trip includes a total of

12 statistical features of the trip’s acceleration and

deceleration events, because these events can char-

acterize driving behavior. For example, aggressive

drivers usually accelerate and brake stronger than

non-aggressive drivers. An acceleration event is char-

acterized by an increasing velocity. Accordingly, a

deceleration event is characterized by a decreasing ve-

locity. We calculate the average and standard devia-

tion of the longitudinal acceleration a

lon

(t) and lateral

acceleration a

lat

(t) for each acceleration event and in-

clude the respective averages as features in the trip’s

feature vector. In addition, we include the average and

standard deviation of the car’s velocity v(t) of all ac-

celeration events in the trip’s feature vector. The same

applies to deceleration events.

The silhouette score measures the clustering va-

lidity and can be used to ﬁnd the optimal number of

clusters (Rousseeuw, 1987). Based on the silhouette

score, we cluster the trips into two clusters. We inter-

pret the cluster centers in terms of driving characteris-

tics and deﬁne which cluster represents which kind of

driving behavior, i.e. non-aggressive and aggressive.

We select the cluster with the higher feature values

in the center as aggressive. Then we assign a label

to each trip according to its cluster, resulting in 10

non-aggressive and 9 aggressive trips as illustrated in

Figure 4.

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

422

-2

-1

-6 -4 -2 0 2 4 6

Principal Component 2

Principal Component 1

Non-aggressive cluster

Aggressive cluster

Cluster centers

Figure 4: Results of k-means clustering. For visualization,

k-means clustering was performed on PCA-reduced data.

A/1

A/2

A/3

A/4

B/1

B/2

B/3

B/4

C/1

C/2

C/3

C/4

D/1

D/2

D/3

D/4

E/1

E/2

E/4

Overall driving behavior score

Driver/Trip

Non-aggressive driving behavior

Aggressive driving behavior

Score threshold

Figure 5: Overall driving behavior score of each trip of each

driver for the freely available data set. The bar color/pattern

shows the k-means clustering-based driving behavior labels.

The dashed black line shows the score threshold for classi-

fying the driving behavior based on our scoring algorithm.

6.2.3 Driving Behavior Score

For each trip, we calculate the overall driving behav-

ior score as deﬁned in Equation (11). Figure 5 shows

both the k-means clustering-based labels as well as

the calculated driving behavior scores. We choose

a score threshold of 50 points to classify the driv-

ing behavior as non-aggressive or aggressive. This

score threshold divides the scoring range evenly be-

tween the two classes of driving behavior considered.

A score greater than or equal 50 points is classiﬁed

as non-aggressive and a score less than 50 points is

classiﬁed as aggressive.

As Figure 5 shows, we correctly classiﬁed the 10

non-aggressive and the 9 aggressive trips. Thus, the

score threshold of 50 points provides a good classiﬁ-

cation performance. In our scoring function deﬁned

in Equation (10), the mean quotient ρ

≈ 0.29 yields a

score of 50 points. Thus, we identify a mean quotient

of ρ

≈ 0.29 as a good threshold for distinguishing be-

tween non-aggressive and aggressive driving behav-

ior.

The results show that our scoring algorithm is suit-

able for assessing driving behavior in uncontrolled

environments, as it performs equally well as the k-

means clustering algorithm, i.e. an ofﬂine algorithm.

In addition, our algorithm does not require data from

other trips and works without prior knowledge and is

thus of practical use.

7 CONCLUSION

We presented an online scoring algorithm that rates

the aggressiveness of a driver. This algorithm can

be used to indicate a driver that he or she is taking

too much risk. Our approach solely relies on wheel

speeds which are available on the CAN bus of con-

temporary cars. No additional data like GPS, speed

limits, trafﬁc- or weather conditions are required. Fur-

thermore, our algorithm can score the driving online

while it happens, unlike other approaches that can

compare several trips after they are completed.

We ﬁrst evaluated our scoring algorithm with

a driving experiment in a controlled environment,

where ground truth was known due to the experimen-

tal setup. The results show that our scoring matches

the actual driving behavior. In addition, we compared

our online scoring algorithm with an ofﬂine cluster-

ing approach that took a set of comparable trips as

input. The results show that our online algorithm per-

formed equally well when compared to the ofﬂine al-

gorithm. However, our approach yields a score im-

mediately and does not need a set of comparable trips

and not even the entire trip for scoring it. Therefore,

our approach is of practical use because it is an online

algorithm, has a low computational complexity and

requires only a minimal data set, namely the wheel

speeds.

Future work should include other physical quan-

tities in addition to the total acceleration in order to

improve the measurement of driving behavior. Fur-

thermore, we suggest to compare the presented scor-

ing algorithm with other existing algorithms. For this,

however, a suitable data set must be collected, since

to the best of our knowledge no such data set exists.

We did not study the inﬂuence of displaying the score

on the driver’s driving behavior and leave it for future

work.

ACKNOWLEDGEMENTS

We thank the Chair of Mechatronics of the University

of Duisburg-Essen and in particular Dieter Schramm

Online Driving Behavior Scoring using Wheel Speeds

423

for providing the Ford C-Max for our driving experi-

ment.

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