Differences in Driver Behaviour between Race and Experienced Drivers:

A Driving Simulator Study

Naman Singh Negi

, Peter Van Leeuwen

and Riender Happee

Telecom Paris-Tech, Paris, France

Delft University of Technology (TU Delft), Delft, The Netherlands

Keywords:

Driver Behaviour, Race Drivers, Experienced Drivers, Simulator, Steering, Braking, Path Strategy.

Abstract:

Safety is one of the major areas of concerns today in the ﬁeld of automotive development. Different safety

measures have and are being introduced in order to improve driver/passenger and pedestrian safety. Advanced

driver assist systems (ADAS) are therefore becoming increasingly important in their role of reducing driver

crash risk. A shortcoming of the ADAS systems is that the variability in drivers based on skill and experience

is not taken into account and the system is often designed for average or worst case driver performance thereby

compromising on the dynamic behaviour of the vehicle. This study focuses on understanding and quantifying

the differences in drivers. This knowledge of driver differences can be helpful in designing an adaptive ADAS

by introducing the driver into the control loop.

The study investigates differences between race-car drivers and normal (experienced) drivers in a high-speed

driving task. The study analyses simulator data for 17 drivers on the Mallory Park test circuit. The driving

task required the participants to drive around the circuit to achieve the fastest lap times. Analysis showed that

higher steering activity and differences in path strategy were the main reasons for lower lap-times shown by

the expert race drivers compared to the non-expert drivers. Steering metrics like average steering rate, steering

jerk showed higher values for the expert group and distance traveled around the corner showed a different

path strategy adopted by the experts. Both groups showed improvement in performance based on lap-times

across the different sessions. Thus the study shows that expert and non-expert drivers have different steering

behaviour and path strategy, which can be attributed to differences in driving experience, vehicle dynamics

knowledge and vehicle control skills.

1 INTRODUCTION

Through the years of automotive development safety

has been one of the primary areas of concern. In-

appropriate driver behaviour and insufﬁcient skill

are considered the primary cause of road accidents.

(Brookhuis and Smiley, 1987) stated that more than

85% of total accidents could be directly attributed

to the driver (59% driver error and 26% driver im-

pairment due to alcohol or drug consumption, fatigue

etc.). Over the years there have been many regulatory

and technological advances designed to help reduce

the risk of accidents. (Fildes and Lee, 1994) reviewed

several studies and reported a reduction of 8-40% in

road accidents in Belgium, Finland, France, Germany,

UK and South Africa due to reduced speed limits.

Advanced driver assist systems (ADAS) are also

becoming increasingly important in their role of re-

ducing driver crash risk. Studies ((Weiner and Curry,

1980) and (NHTSA, 2009)) have shown that these

systems are beneﬁcial in improving the safety of the

driver and the passengers, but at the same time they

can also make the drivers complacent and relaxed

thereby increasing the probability of a dangerous situ-

ation. Moreover, these regulatory and technical mea-

sures are not driver speciﬁc and often the average

driver performance is used as the benchmark. It can

be argued that the present safety measures are not

designed to address the variance in the skill level of

drivers. Knowledge of driver based differences in

control and behaviour can hence help improve the de-

velopment of ADAS.

Driver perception, knowledge and awareness, and

vehicle control skills affect the driving task compe-

tency. (Fuller, 2005) stated that the total driving com-

petency is based on initial driving skill and the knowl-

edge and vehicle control skills gained through expe-

rience. (Malik, 2011) deﬁned driver competency as

the ability to use driving knowledge and skill for the

successful and safe completion of the driving task i.e.

360

Negi, N., Van Leeuwen, P. and Happee, R.

Differences in Driver Behaviour between Race and Exper ienced Drivers: A Driving Simulator Study.

DOI: 10.5220/0007700003600367

In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 360-367

ISBN: 978-989-758-374-2

proper and timely perception followed by appropriate

action. Previous research has shown differences in

control strategy for expert drivers compared to non-

experts (Katzourakis et al., 2011) on a circular drive

task. Further differences in steering control in a simu-

lated environment were found by (Zhang et al., 2008),

who showed differences in the frequency spectrum

between expert and non-expert drivers during differ-

ent lane change maneuvers. The difference in com-

petency level between experts and non-expert drivers

leads to differences in behaviour and performance.

This research is an extension of previous research

done to classify drivers into groups of non-expert, ex-

perienced and experts. The research aims at objec-

tively estimating the skill level of the drivers using a

predeﬁned task. The present study focuses on analyz-

ing the differences between race drivers (experts) and

normal (experienced) drivers in a high-speed driving

task. An experienced driver is one who has a certain

level of expertise gained through driving experience,

while expert drivers are categorized by high level of

driving proﬁciency (e.g. race car drivers, instructors

in driving schools). The task required the partici-

pants to select the optimal speed and race line, and

provide accurate and consistent control inputs while

going around the corners in order to achieve minimal

lap-time. The study focuses on the steering behaviour

and the path strategy and consistency of both groups

of drivers using a driving simulator test dataset.

2 METHODS

2.1 Apparatus

The experiment was conducted in a race-car simulator

based on the chassis of a Formula Renault 2.0 racing

car used for training purposes (Sim-Delft, 2013). The

steering wheel, brake and throttle pedal were used

from the original car and a direct drive motor pro-

vided force feedback. The throttle and brake pedal

feedback was passive and calibrated to resemble a re-

alistic formula car. The visual system consisted of

three 52-inch LCD screens and provided a 130 de-

gree horizontal and 27 degrees vertical ﬁeld of view.

The simulator was equipped with a steering wheel

mounted dashboard showing speed, engine rpm, lap

and lap sector times. The virtual environment, vehicle

dynamics and force feedback were simulated by rFac-

tor software (v1.255). The rTrainer vehicle model, a

rear wheel driven formula style racecar (115 bhp, 573

kg), was used (Figure 1). All driving aids were dis-

abled and gear shifting was automated. All driving

simulator data was recorded and stored at 100Hz.

Figure 1: RTrainer Car.

2.2 Experiment Instructions

Participants were instructed to drive the fastest lap-

time possible on an unfamiliar racetrack. The Mal-

lory Park circuit was chosen for the experiment. Fig-

ure 2 below shows the outline of the circuit, con-

sisting of: 1) a long right hand corner known as the

Gerard’s bend, which turns through nearly 200 de-

grees, 2) a 180 degrees hairpin corner know as the

Shaw’s Hairpin, and 3) a combination of two fast cor-

ners. The participants drove four sessions of 10 min-

utes and between sessions had a ﬁve-minute break.

Participants received instructions prior to the start of

the experiment about the application of the throttle

and gas pedal and explanations regarding the infor-

mation available on the steering wheel mounted dash-

board. Participants were required to steer, accelerate

and brake (gear shifting was automatic).

Figure 2: Mallory Park Test Circuit.

2.3 Participants

Seventeen drivers (all male), aged from 17 to 26 years

(mean = 20.8, SD = 2.0) participated in the study. The

non-expert group consisted of 10 drivers, students at

the Delft University of Technology having no experi-

ence in racing with an average age of 21.6 years (SD =

1.8). The expert group had 7 drivers, professional rac-

ing drivers from various (international) racing classes

(e.g. Formula 3, GP2 and Porsche Supercup) with an

average age of 19.9 years (SD = 2.0).

2.4 Dependant Measures

To analyze the performance of the participants three

curves were selected on the test circuit:

Differences in Driver Behaviour between Race and Experienced Drivers: A Driving Simulator Study

361

1. 1) Long right hand curve, which turns through

nearly 200 degrees

2. 2) Combination of two fast curves

3. 3) 180 degrees hairpin curve

Figure 3 below shows the selected curve and each

curve was analyzed separately. Data analysis and the

results are shown below. Each session was analyzed

Figure 3: Track breakdown into different curves for analy-

sis.

separately per curve. The data for the three mentioned

curves was selected for every lap and analyzed. The

curve entry and exit point was determined by the X-

Y position of the vehicle, ensuring that it was situ-

ated before the point where drivers start giving any

vehicle control inputs (steering, brake, or throttle) re-

lating to the entry of the corner. Similarly, in deter-

mining the exit point it was veriﬁed that the vehicle

was in straight-line steady state condition (Figure 4)

The steering data was ﬁltered using a low pass Butter-

Figure 4: X-Y position of the selected curves for analysis.

worth ﬁlter (2nd order, 3Hz). All the other data was

ﬁltered using a Butterworth 2nd order 10Hz low pass

ﬁlter. The ﬁrst part of the analysis was to remove road

departures from the data. The cases in which all the

four wheels left the track were considered as "road

departure". This was done separately for all the three

curves and laps with road departures were excluded

from the performance analysis. They were studied

separately to understand the crash behaviour of the

two groups, but have not been included in this study.

The performance of the drivers was analyzed using

different dependant measures discussed in Table 1 be-

low. As the data was found to be non-normal (us-

ing q-q plots), differences between the experts and

the non-experts were assessed using the Wilcoxon-

Mann-Whitney test, which is better at dealing with

non-normal distributions than the t-test.

Table 1: Dependant Measures.

3 RESULTS

3.1 Road Departures

Experts showed a higher percentage of road depar-

tures compared to non-expert drivers in Curve2, ses-

sion 2-4. A signiﬁcant improvement in the num-

ber of road departures over sessions can be seen for

the non-expert drivers in all curves (p=0.031), while

the experts remain at a constant level of road depar-

tures (p=0.343). Table 2 below shows the number of

laps with road departures for every session and both

groups.

Table 2: Number of Road Departures.

3.2 Curve Times

Experts show lower lap-times and curve times com-

pared to the non-experts. Experts are faster than non-

experts on average across all the sessions by a margin

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

362

of 0.8-1.5 seconds in curve 1, 0.4-0.9 seconds in curve

2 and 1.0- 2.3 seconds in curve 3.

Table 3: Average curve-times for all session and all curves.

Both the groups show near signiﬁcant improvement

in lap times from session 1 to 4. The two groups per-

form signiﬁcantly differently in terms of curve times

from Session-2 to Session-4, as can be seen from the

p-values indicated in the table below.

Figure 5: Comparison of curve times for the three curves

from session 1 (top) to 4 (bottom) (blue and red lines rep-

resent the mean curve times for experts and non-experts re-

spectively). Each circle represents the curve time for one

lap during a curve section from the respective participant.

The overall difference in curve-times for all the

three curves between experts and non-expert drivers is

around 2.2 -4.6 seconds whereas the difference in lap-

times is between 3.2-4.7 seconds. This shows that the

experts maintained a higher speed in the straight road

segments also, which maybe because of the higher

corner exit speeds of the experts. Table 3 above does

not indicate the improvement in the average curve

times for curve 1 from session 1 to 4, which can be

seen in Figure 5. Similar differences are shown in

curve 2 and curve 3 also with experts having lower

curve times as compared to non-expert drivers. Both

the groups show improvement across the sessions.

Experts show signiﬁcant improvement in curve-times

in curve 2 and curve 3 between session 1 and session

4. Experts show more consistency in the results as can

be seen from Figure 5.

3.3 Lateral Acceleration

Experts maintain higher levels of lateral acceleration

as they went around the curves during sessions 1 to

session 4 compared to the non-expert drivers. In curve

1 the expert drivers maintain 0.06-0.12 g higher lat-

eral acceleration, in curve 2 the difference is 0.07-

0.14 g and in curve 3 there is 0.07-0.09 g difference

as compared to the non-expert drivers. No signiﬁcant

improvement was seen in the performance of the two

groups in terms of lateral acceleration from session 1

to session 4 for the non-experts whereas the experts

show signiﬁcant increase in lateral acceleration for

curve 1 from session 1 to session 4. non-experts have

Table 4: Average Lateral Acceleration for all sessions and

all curves.

lower values of acceleration probably because their

primary goal is to successfully negotiate the curve

without any road departures, whereas experts try to

keep the vehicle at the limit to achieve the best perfor-

mance. Thus experts also have higher road departures

as compared to non-expert drivers (Table 2).

3.3.1 Steering Performance

Experts show higher steering activity compared to

non-expert drivers based on steering wheel rate and

steering jerk. Steering jerk values are different for the

two groups but only curve 1 (session1 and session 2)

and curve 2 (session 1, session 3 and session 4) show

signiﬁcant differences (p< 0.05). Steering jerk val-

ues shown by the experts were approximately 1.5-2

Differences in Driver Behaviour between Race and Experienced Drivers: A Driving Simulator Study

363

Figure 6: Comparison of average lateral acceleration (g) for

all the curves from session 1 to 4 (blue and red lines rep-

resent the mean curve times for experts and non-experts re-

spectively). Each circle represents the mean lateral acceler-

ation during a curve section from the respective participant.

times higher than the non-expert drivers for curve 1

and 2. Reduction in the average steering jerk values

can be seen for experts from session 1 to 4 for all the

curves but only curve 3 shows signiﬁcant difference

(p<0.05).

Table 5: Steering metrics for all sessions and all curves.

non-expert drivers also show reduction in steer-

ing jerk values but it was not statistically signiﬁcant.

Data also shows high standard deviation among the

experts. non-expert drivers have lower values but also

show smaller deviations in the group, see Table 5

for an overview of the data. Average steering rate

is also higher for the expert group compared to the

non-expert drivers but it is not signiﬁcantly different.

There is a reduction in average steering rate values

for the non-expert drivers from session 1 to 4 but it

is not statistically signiﬁcant. Experts also show re-

duction but only curve 2 shows signiﬁcant difference

(p<0.05).

3.4 Path Strategy

3.4.1 Braking Point

Lower values of standard deviation, especially in

Curve 2, show that experts ﬁnd a suitable braking

point and follow it consistently. As can be seen from

Table 6, the experts brake approximately 94 meters

before the curve 2 entry point during all the 4 ses-

sions. On the other hand the non-expert drivers brake

later and show variation in the braking point through

the different sessions. non-expert drivers also have a

higher standard deviation within a session compared

to the experts. The braking point in Curve 3 is later

for the experts, and reaches signiﬁcance in the last

session. No signiﬁcant difference was seen in experts

or the non-expert drivers from session 1 to 4 (p>0.05).

Table 6: Braking point for all sessions and curves 2&3 (in

curve 1 braking was not always observed).

3.4.2 Path Strategy

In ﬁgures 7 to 9 the path taken by experts and non-

expert drivers is shown for all curves in session 4.

Differences in path strategy between the two groups

can be seen from the graphs. In curve 1, as can be

seen from Figure 7, non-expert drivers try to keep the

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

364

vehicle close to the inside of the track at all times.

Experts on the other hand ﬁrst go towards the out-

side of the curve and then give a sharp steering input

to exit from the curve. As a result, they maintain a

larger distance from the inside of the curve as com-

pared to the non-expert drivers. In curve 2, as seen

Figure 7: Session 4-Curve 1: Path Followed: Blue lines

indicate the individual paths for the experts (right) and non-

expert drivers (center) in session 4, whereas red line (for

experts) and green line (for non-expert drivers) indicates the

mean path of all the laps. Thin black lines indicate the lane

boundaries of the track and the arrows indicate the direction

of travel.

from Figure 8, non-expert drivers give a higher initial

steering input to keep the vehicle closer to the inside

of the track. Experts, on the other hand, have smaller

steering inputs and keep the vehicle closer to the out-

side of the track while exiting the corner, compared to

non-expert drivers. In Curve 3 as seen from Figure 9,

Figure 8: Session 4-Curve 2: Path Followed: Blue lines

indicate the individual paths for the experts (right) and non-

expert drivers (center) in session 4, whereas red line (for

experts) and green line (for non-expert drivers) indicates the

mean path of all the laps. Thin black lines indicate the lane

boundaries of the track and the arrows indicate the direction

of travel.

the non-experts try to remain close to the inside of the

curve while entering and try to maintain a constant

distance from the inside of the corner. The experts

on the other hand, drive away from the inside of the

curve while entering and remain close to the inside

of the curve while exiting. Difference in path strat-

egy is also represented in the distance travelled by the

two groups while negotiating the corner (Table 7). As

can be seen from Table 7, experts take a longer path

in curve 1 (signiﬁcantly different for Session 2 and

Session 4 with p<0.05) which is because they main-

tain a higher distance from the inside of the curve (see

Figure 7) as compared to non-expert drivers. Similar

pattern is seen in curve 2 (signiﬁcantly different for

Session 3 and Session 4 with p<0.05) where the ex-

perts keep the vehicle towards the outside of the curve

Figure 9: Session 4-Curve 3: Path Followed: Blue lines

indicate the individual paths for the experts (right) and non-

expert drivers (center) in session 4, whereas red line (for

experts) and green line (for non-expert drivers) indicates the

mean path of all the laps. Thin black lines indicate the lane

boundaries of the track and the arrows indicate the direction

of travel the track.

while exiting (see ﬁgure 8). Curve 3 does not show

any signiﬁcant difference in terms of distance trav-

elled. There is no signiﬁcant difference for distance

travelled from session 1 to 4 for both the groups.

Table 7: Distance traveled for all sessions and all curves.

4 CONCLUSION

In this study differences between expert race-car

drivers and non-expert drivers were investigated. As

seen from the data presented in this study, experts

show better performance in terms of lap-times and

higher tire utilization as compared to the non-expert

drivers. Higher lateral acceleration shown by the

expert drivers means that they had a higher speed

throughout and exiting the corner. This enabled them

to maintain a higher straight-line speed, resulting in

faster lap-times achieved by the experts as compared

to the non-expert drivers. non-expert drivers had more

road departures in the ﬁrst session, which could be

because they were not familiar with the simulator and

took some time to adjust to driving in the simulator

environment, and getting accustomed to the racing

task and the race-track. In sessions 2 to 4 experts

had higher percentage of road departures as com-

pared to the non-experts, which is possibly because

experts took more risk trying to keep the vehicle on

the limit to achieve best performance as can be seen

from the lower lap-times and higher tire utilization

Differences in Driver Behaviour between Race and Experienced Drivers: A Driving Simulator Study

365

values, compared to the non-expert drivers. Proba-

bly, the primary goal of the non-expert drivers was to

successfully negotiate the curve without any road de-

partures, sacriﬁcing some curve time performance.

The results show differences in steering behaviour

between the two groups. Expert drivers show higher

steering activity in terms of steering rate and steering

jerk. These results are consistent with previous re-

search which showed higher steering activity among

expert drivers in terms of steering wheel angle, av-

erage steering jerk and frequency of steering inputs

((Hollopeter, 2011); (Zhang et al., 2008)). Driving

is a combination of open and closed loop processes.

Precise timing and accurate control inputs can enable

the driver to negotiate the race task in a largely open

loop state. While entering a corner a driver anticipates

the speed and steering angle that should be used, rep-

resenting the feed-forward part of the control loop.

But deviation from the desired path (vehicle position-

ing on the track) due to imprecise control inputs, lag

in the system or changing vehicle and environment

conditions, requires additional control inputs to cor-

rect for the deviations from the desired path. This rep-

resents the feedback control of the drivers. Possible

best performance can be achieved with a combination

of feed-forward and feedback control. Therefore, the

higher steering activity shown by the experts might be

attributed to higher feed-forward and feedback gain

as compared to the non-expert drivers. Lower steer-

ing activity shown by the non-expert drivers might

be correlated to lower feed-forward gain, which re-

sulted in poor vehicle positioning while entering the

corner, and lower feedback gain, which resulted in in-

sufﬁcient correction in vehicle path while taking the

corner. Thus higher steering activity shown by the

experts can possibly be attributed to optimizing the

desired path. Another possible explanation for higher

steering activity especially in the racing task can be

that the experts are not only optimizing the path fol-

lowed but are also trying to keep the vehicle at the

traction limit and hence providing continuous steer-

ing corrections to stabilize the vehicle. Overall this

is evidence that experts have a better developed inter-

nal vehicle model, which enables them to understand

what the current situation demands, what should be

the control inputs, and also how the vehicle will re-

spond to the given control inputs. The promptness in

steering action and the ability to provide faster inputs

could be the result of practice. non-expert drivers on

the other hand might not be capable of giving such

fast inputs, lacking motor control skills, or maybe

they do not dare to give faster inputs, as they do

not know how the vehicle might respond, which is

evidence of inferior vehicle dynamics and response

knowledge. This is consistent with the deﬁnition of

competency ((Fuller, 2005)), which states that com-

petency is a combination of initial personal biolog-

ical characteristics and knowledge and skill gained

through training and experience.

We also see a difference in strategy between the

two groups in terms of braking point and the path cho-

sen to negotiate the corner. The non-expert drivers

show inconsistency in the braking point. This can be

evidence that the non-experts are inaccurate in per-

ceiving the curvature of the corner and hence are un-

able to judge the correct timing and magnitude of con-

trol action. While cornering, non-expert drivers try to

maintain a constant distance from the inside of the

corner whereas the experts tried to follow the rac-

ing lane, keeping towards the outside of the corner

while entering and exiting and going close to the in-

side of the corner in the mid-section. The different

path strategies of the two groups is similar to the one

found by (Treffner et al., 2002).

In summary, it can be concluded that experts who

had greater experience in the racing environment per-

formed better than the non-expert drivers in terms of

lower lap-times. Higher steering activity, different

braking and path following strategy and consistency

in following the chosen strategy signiﬁcantly differ-

entiated the two groups. The data also showed that

lap-time, steering jerk and distance travelled metrics

could be used to differentiate between expert and non-

expert drivers. Steering jerk metric showed the largest

difference between the two groups with experts ap-

proximately 1.5-2 times higher than the non-expert

drivers for curve 1 and 2.

REFERENCES

Brookhuis, K. and Smiley, A. (1987). Alcohol, drugs

and trafﬁc safety. Road users and trafﬁc safety. J.A.

Rothengatter & R.A. de Bruin (Eds.), Assen: Van Gor-

cum, 83-105.

Fildes, B. and Lee, S. (1994). The Speed Review: Road En-

vironment, Behavior, Speed Limits, Enforcement and

Crashes. Report No. CR 127, Federal Ofﬁce of Road

Safety, Canberra, Australia.

Fuller, R. (2005). Towards a general theory of driver behav-

ior. Accident Analysis and Prevention, 37 461-472.

Hollopeter, J. (2011). Response of novice and experienced

drivers to lateral control intervention to prevent lane

departures. Master’s thesis, University of Iowa. Re-

trieved from http://ir.uiowa.edu/etd/984.

Katzourakis, D., Velenis, E., and Happee, R. (2011). Driver

control actions in high-speed circular driving. Pro-

ceedings of the Sixth International Driving Sympo-

sium on Human Factors in Driver Assessment, Train-

ing and Vehicle Design.

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

366

Malik, H. (2011). An assessment system for evalua-

tion of driving competencies. PhD thesis, Queens-

land University of Technology. Retrieved from http://

eprints.qut.edu.au/view/person/Malik,.Husnain.html.

NHTSA (2009). National Highway Trafﬁc Safety Adminis-

tration 2009 Data: Overview - CrashStats. NHTSA.

Treffner, P., Barrett, R., and Peterson, A. (2002). Stabil-

ity and skill in driving. Human movement science,

21. Retreived from http://www.biomedsearch.com/

nih/Stability-skill-in- driving/12620719.html.

Weiner, E. and Curry, R. (1980). Fight-deck automation:

promises and problems. Ergonomics, 23(10), 995-

1011.

Zhang, Y., Lin, W., and Chin, Y. (2008). Driving skill char-

acterization: A feasibility study. IEEE International

Conference on Robotics and Automation.

Differences in Driver Behaviour between Race and Experienced Drivers: A Driving Simulator Study

367