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Preliminary studies on criticalities and opportunities for virtual testing of driving automation



This paper presents some preliminary analyses on the development of new tools for the transport industry, able to deal with the introduction of increasing levels of vehicle automation. Driving assistance is aimed at increasing road safety, but it needs a renewed approach to the research and development process. Indeed, vehicle automation spans several scientific disciplines and it's becoming exceedingly difficult and too costly for a single research innovation team to " go deep " across all technologies and solutions. The only way to ensure an easy, fast, efficient, and scalable introduction of the required innovation is to adopt integrated and complex testing platform for the simulation of automation solutions. To this aim virtual-testing platforms should be conceived to allow different actors to work on different components, possibly at different levels of detail, any of the actors being allowed to sophisticate with a particular simulation issue (e.g. the driver behaviour in presence of Advanced Driving Assistance Systems) knowing that the other components (e.g. the vehicle dynamics) have been (or will be) simulated at the required sophistication level, possibly by another actor of the innovation process. In this paper the authors wishes to contribute to the development of these new-generation tools. Analyses will be carried out in order to identify the key opportunities and criticalities in the development of virtual testing platform for testing driving automation. From these analyses research perspectives will be identified and proposed for future developments.
Preliminary studies on criticalities and opportunities for virtual testing
of driving automation
Dipartimento di Ingegneria Civile Edile ed Ambientale
Dipartimento di Ingegneria Industriale
Università degli Studi di Napoli Federico II
Via Claudio 21, 80125 Napoli
Abstract: - This paper presents some preliminary analyses on the development of new tools for the transport
industry, able to deal with the introduction of increasing levels of vehicle automation. Driving assistance is
aimed at increasing road safety, but it needs a renewed approach to the research and development process.
Indeed, vehicle automation spans several scientific disciplines and it’s becoming exceedingly difficult and too
costly for a single research innovation team to “go deep” across all technologies and solutions. The only way to
ensure an easy, fast, efficient, and scalable introduction of the required innovation is to adopt integrated and
complex testing platform for the simulation of automation solutions. To this aim virtual-testing platforms
should be conceived to allow different actors to work on different components, possibly at different levels of
detail, any of the actors being allowed to sophisticate with a particular simulation issue (e.g. the driver
behaviour in presence of Advanced Driving Assistance Systems) knowing that the other components (e.g. the
vehicle dynamics) have been (or will be) simulated at the required sophistication level, possibly by another
actor of the innovation process. In this paper the authors wishes to contribute to the development of these new-
generation tools. Analyses will be carried out in order to identify the key opportunities and criticalities in the
development of virtual testing platform for testing driving automation. From these analyses research
perspectives will be identified and proposed for future developments.
Key-Words: - Intelligent Transportation Systems; Advanced Driving Assistance Systems; Autonomous
Emergency Braking; Road Safety; Driving Behaviour; Adaptive Cruise Control, Intelligent Speed Adaptation
1 Introduction
Advanced Driving Assistance Systems (ADAS)
have been designed, developed and tested for
several years, and the development of further
devices, mainly aimed to implement the active
safety paradigm, are under way. Studies [1] carried
out in USA show that the great part of the traffic
accidents can be caused both by the driver’s
distraction or inadequateness with respect to some
traffic conditions, and by the incorrect driver’s
interaction with primary and secondary driving
commands or other on-board and personal devices.
Automated driving at different levels has the
potential to decrease accidents by reducing the
impact of the human factor [2]; this will contribute
on the long term to the reduction of road fatalities
[3]. However, the more advanced the system is, the
more complex the integration in the vehicle will be,
and sometimes the overload of information to the
driver can be unproductive. ADAS should be not
seen (not yet) as a substitute of drivers, but as a co-
driver that does not exclude the driver from the
control loop [4], even if his/her direct involvement
in an increasing number of driving tasks can be
replaced by automation [5]. This is even more
crucial [6] when an interaction between the driver
and the automation is needed, for example when the
system acts as a warning. In these events the well-
known out-of-the-loop syndrome [7] has to be
avoided. Indeed, full automation is a long term
objective [8] and full benefits can be gained in the
long run, by gradually shifting the main driving role
from the driver to the vehicle. Moreover, for
acceptance of increasingly higher levels of
interaction and automation, we need to understand
drivers’ needs as well as possible interactions
between the human and the automatic driving
control logics. This is not only required at the pre-
commercial testing phase, but also at an early
development stage. At the time of the development
and adoption of ADAS solutions.
Of course, as the complexity increases, an
increasing number of technical disciplines is
involved in the development of automation
solutions. All related technologies have to be
considered, as well as the consequences on the
vehicle and on the driver. However, also the traffic
and on the traffic propagation on infrastructures and
networks is affected by the introduction of ADAS.
Indeed, different driving mechanisms interact in
terms of flow propagation and this could impact on
how the flow is propagated and on how the network
has to be consequently adapted ( [9], [10]) in order
to take into account these effects. Higher impacts
could be raised at a transportation planning process
too [11], as capacities and performances of
infrastructures can result to have been drastically
In such a context, the only way to ensure an easy,
fast, efficient, and scalable introduction into the
market of the required innovation is to adopt an
appropriate development and testing platform, based
on a flexible architecture, where the (reactive)
driver’s model is central and independent
components can be developed and then exploited in
a multi-actor environment.
In this paper we study the development needs for an
integrated platform based on a multidisciplinary and
modular approach. In section 2 the best-candidate
platform is identified with respect to the desired
characteristics, the main criticalities in setting-up
the platform are verified and a second-best solution
is identified at suggested, at least for an early stage
of development. In sections 3 and 4 two of the main
modules of the platform, the vehicle dynamics and
the driver model, are considered in more details and
suggestions given for future researches and
development stages in order to employ these
modules in a virtual platform for ADAS design.
2 The simulation platform
In order to allow for the needed modular and
multidisciplinary approach, the platform requires
some peculiar characteristic. One of these is the
integration layer; it should have an appropriate level
of flexibility (and/or completeness) that allows for
the integration of different multidisciplinary-
developed modules. An analysis of the available
options has oriented our development toward the
adoption of a driving-simulation environment,
namely Scaner Studio, by Oktal [12], already
installed on the car driving-simulation suite (two
twins compact driving simulators and a high-fidelity
dynamic one) at the University of Naples Federico
II ( [13], [14]). Indeed, it is peculiar of professional
driving simulators to own a very sophisticated
simulation environment for road scenarios and
traffic conditions. Moreover, a detailed
representation of the vehicle dynamics is inherently
required in traffic simulation, in order to ensure a
realistic driving experience. In our case, the Oktal
simulation environment is based on the Callas
platform [15], [16], that allows for a detailed
characterization of the vehicle dynamics. Callas
software is a realistic simulator validated by car-
makers (including PSA), and research institutions
including IFSTTAR (former INRETS). The Callas
model also takes into account vertical dynamics
(suspension, tires), kinematics, elasto-kinematics,
tire adhesion and aerodynamics.
Finally, the driving-simulation environment allows
for co-simulation, that is the integration into the
simulation environment of interacting control logics
thanks to APIs developed in Matlab/Simulink.
The driving-simulation environment is our best-
choice as integration platform. Obviously, it
requires the development and/or fine-tuning of a
great variety of sub-models. Our choice here is to
focus our first attention on two of these models: the
vehicle dynamics and the driver.
Given the early stage of development of our
platform and the chosen modules to be developed, a
simpler integration platform can be employed. In
particular, the Matlab/Simulink environment (that is
contained in the Oktal platform) has been employed
in this pilot work without loss of generality.
As a consequence, the development in the
Matlab/Simulink environment of the driver model
and the vehicle dynamic model will be described in
the following.
3 Driver modelling and automation
ADAS are considered one of the possible ways to
reduce road fatalities. Automation levels have been
classified; for instance, as reported from ERTRAC
[8] SAE (Society of Automotive Engineers)
considers 6 automation level, ranging from 0 to 5.
These approximately correspond to the five levels
(from 0 to 4) by the National Highway Traffic
Safety Administration (NHTSA) [17], with SAE
levels 4 and 5 corresponding to NHTSA level 4.
Level 0 corresponds to no-automation and level 5 to
full-automation. At automation level 1 (Driver
Assistance) the vehicle is able to assist the human
driver in some driving tasks related to both
acceleration/deceleration and steering, but the main
responsibility for monitoring the driving context is
up to the driver, who also is in charge for fallback.
At level 2 (partial automation) the vehicle
accomplishes some specific driving tasks (both
longitudinal and lateral driving), while monitoring
and fallback are in charge to the driver. At level 3
(conditional automation) the vehicle executes
driving tasks and monitors the driving environment,
this applies to specific driving modes and the driver
is requested to resume the control in case of
fallback. At level 4 (high automation) the vehicle is
responsible for all actions (including fallback) but
this is applied to specific driving modes. At level 5
(full automation) the definitions of level 4 apply to
all driving modes. Various levels of driving
automation have already started to be progressively
introduced in the Automotive arena [18]. Level 1 of
automation is diffused since several years (Adaptive
Cruise Control, Lane Keeping Assistance, etc.),
level 2 has emerged (automated parking, adaptive
cruise control with stop-and-go and/or truck
platooning) and level 3 is now discussed (e.g.
combination of adaptive cruise control and lane
Integrated and complex situations are more
challenging in virtual-testing. A combination of
ADAS solutions that work in a synergic way can be
considered, allowing for classifying the overall
solution at level 3. However, the most correct
application of any of the logics and the transition
from one to the other has to be duly consdiered. For
instance, ISA (Intelligent Speed Adaptation), ACC
(Advanced Cruise Control); CWS (Collision
Warning System) and AEB (Autonomous
Emergency Braking) can be applied according to
appropriate surrogate measures of safety, evaluated
at run-time. Moreover, the systems can be applied in
such a way that the driver perceives for most of the
time (that is when the ACC runs) that the
automation is human-like and consistent with
his/her own behaviour. This ensures the driver is
always in-the-loop of the driving control process.
All control algorithms result in the need of applying
accelerations and decelerations to the vehicle. These
represent the stimuli viewed from the vehicle (and
not the human) side. The application of these stimuli
and the resulting effects on the vehicle are simulated
with the support of the vehicle dynamics model
described in next section 4.
An integrated control logic that could be embedded
in the virtual-testing platform could be based on the
run-time evaluation of headway (H) and time-to-
collision (TTC), thanks to continuous measurement
of the follower’s speed and relative speed and
spacing with respect to vehicle ahead. The TTC is
the time after which a collision between the two
vehicles will occur, if the collision course and the
speed difference are maintained [19]. It is worth
noting that H and TTC are also considered surrogate
measures of safety [20] [21] [22], thus these are
appropriate to manage transition between different
solutions that are conceived for different safety-
related conditions and tasks. Modelling H and TTC
and managing them into an automation control-logic
can be adopted as the core of the driving model
developed into the virtual-testing platform. A
particular attention should be also given, according
to the behavioural nature of the searched driving
model to the integration between the ACC and the
ISA. If active, an ISA overrides the driver’s desired
speed and suggests to the ACC the speed posted by
a digital map or by an Infrastructure-To-Vehicle
(I2V) communication system. The need of
reclaiming back the control of the vehicle to the
driver can be asked by the on-board CWS. Indeed, if
for any reason, the headway (H) between the
controlled vehicle and the vehicle ahead is below a
critical threshold, the CWS asks the driver to re-
establish conditions of greater security. The CWS
acts as an actual safety assistant that monitors
unsafe headways and warns the driver for
reclaiming the control of the vehicle, the driver’s
model simulates the response to such a warning. In
the case that not only the headway is below a given
threshold but also the Time-To-Collision (TTC)
decreases under another threshold, the danger is not
only potential but also imminent. In this event, the
reaction has to be as prompt (and intense) as
possible, quicker than the perception and reaction
time of the driver to the CWS warning. This is the
role played by the AEB, which automates the
braking in order to avoid the incident or (more
likely) to reduce impacts, damages and injuries.
For what concerns the ACC, the logic to be applied
by this system has to be specified, as it constitutes a
key element for the driving logic too. It has been
already evidenced by the scientific literature that in
case of highly automated solution an high risk is
related to the reduced ability of the driver to recall
the control of the vehicle in case of failure of the
automatic system [23]. Indeed, even with moderate
automation, as in the case of ACCs, drivers could
experience problems in reclaiming control, maybe
because of an overreliance on the vehicle systems
and/or a reduced situational awareness (SA) [24]. In
order to avoid reduced SA, the ACC logic has to be
designed according, for instance, to the human-like
approach described in [25]. The human-likeness is
ensured by an on-demand calibration process of the
parameters of the linear model, assumed to take
place while the driver still has full control of the
vehicle. This realizes a learning-running approach
that has been shown to be feasible in [25] and [26],
where the linear stimuli-response model has been
validated with respect to both synthetic (laboratory-
generated) and real-word observed car-following
4 The model for the vehicle dynamics
To carry out a more realistic simulation a vehicle
dynamics model has been coupled with the ADAS
The purpose of the implemented vehicle dynamics
model is to simulate, in real time, the vehicle’s
dynamic behaviour, thanks to the continuous
integration of the balance equations regulating the
longitudinal and lateral vehicle motion.
In order to make the simulations as close to reality
as possible, the implemented mathematical model
also includes working dynamics for wheels, tires,
engine and braking system, as well as a control
system for the automatic gearbox activation.
Furthermore, a model able to provide an estimation
of the real time fuel consumption of the considered
vehicle is intended to be developed: such a model is
embedded in the vehicle model discussed in the
following, being a further purpose of the activities
the optimization of the consumption performances
during urban drives.
The implemented model does not only involve
vehicle dynamics, but it stands for a general vehicle-
behaviour model: as a matter of fact, besides the
‘pure’ vehicle dynamics equations, also the
modelling of some other essential vehicle’s
components (such as engine, gearbox, braking
system and others) has been performed. In order to
make the vehicle dynamics model more clear and
readable, it has been organized in different sub-
systems.The main subsystem contains all the
equations describing the vehicle dynamics:
Longitudinal Balance Equation, Lateral and Yaw
Vehicle Dynamics and Vertical Loads
Determination, taking into account load transfers by
means of the handling diagram/phase plane
approach described in [27] and of the influence of
roll stiffness [28]. In the input/output subsystem all
the model’s exchange parameters with the other
subsystems and in particular with ADAS model are
properly adjusted and displayed. In the I/O
subsystem also the errors affecting the signals
referred to the control variables are calculated, with
reference to the target ones, so that the
instantaneous error value can be deduced at any
given time. The error determination is essential for
the vehicle dynamics model, as this variable stands
for the input signal controlling the processor units,
designated to monitor the vehicle’s behaviour and to
make the decisions aiming to adapt its working
conditions to the desired ones (e.g. action of throttle
and braking system). In the control unit subsystem
the vehicle’s real components and systems
behaviour have been introduced, in order to make
the final model as close to reality as possible. In
particular, this subsystem allows the introduction of
the engine’s dynamic behaviour, as well as the
behaviour of the braking system, complete with an
ABS (Antilock Braking System) unit [29]. The ABS
working principle is based on the wheels’ speed
detection (through dedicated sensors fitted on-
board), a signal processing and a set of actuators
directly operating the hydraulic braking system. The
implemented ABS logic can be easily de-activated
by simply operating on dedicated manual switches,
specifically inserted in the model. Thanks to the
ABS logic, it is even possible to take into account
the possibility of tyres’ locking, following to very
intense application of braking and/or to poor
traction driving conditions (e.g. on snow, ice, rain
etc.). As concerns the variations in road frictional
conditions, fundamental in ABS actuation, a specific
physical grip model has been employed [30]. In
ABS block two proportional-integral controllers
have been used in order to estimate the magnitude
of the action it is necessary to perform on the
vehicle’s systems to let it move according to the
ADAS control logics. The input signal for these
controllers is the error magnitude, while the output
signal is the action which has to be carried out in
order to minimize the error itself. In the tyre
subsystem, an essential part of the whole vehicle
model, an evolved MF set of equations [31] able to
simulate the wheel behaviour has been introduced.
In this subsystem it is possible to determine the
tyres actual grip at any single time and to calculate
the longitudinal and lateral interaction forces the
tyre is able to perform. The above mentioned
interaction forces are the friction actions the tyres
exert on the ground, depending both on the local
friction coefficient inside contact patch, strongly
connected to fundamental thermodynamic
phenomena observable at tyre/road interface [32],
[33], both on the vertical force acting on the tyre, so
even depending on the load transfer during vehicle
handling. The knowledge of these forces is
essential, since they deeply affect and influence the
vehicle’s handling and road-holding: this means
these forces are intimately connected to the vehicle
safety, and gives a clue of the importance of this
subsystem and of the equations it aims to introduce.
5 Conclusions
The main aspect related to the development of a
platform for virtual testing of ADAS have been
addressed. The criticalities and opportunities of such
a platform have been analysed by referring to a
complex driving assistance logic, obtained by the
integration of simpler logics in order to reach a
more advanced level of automation. The control
logics have been not detailed, as they have been
shown to be feasible during the research project
( [34] [35]). The transition between the
logics has been modelled by means of a driving
model, aimed at avoiding both potential and
imminent danger conditions still applying a not
robotic and human-like driving style. This could
likely maintains the driver within the control loop of
the vehicle. In the future, more analyses should be
devoted to the acceptability among drivers of the
integrated system. This can be properly done by
supporting the integration of testing procedures in a
DIL (Driver In the Loop) approach, based on
driving simulator, that will complement the testing
platform here proposed.
National Technical Information
Service, «National Motor Vehicle Crash
Causation Survey, DOT HS 811 059,»
Springfield, Virginia 22161, 2008.
iMobility Forum, “Automation in Road
Transport: roadmap,” May 2013.
[Online]. Available:
EPoSS, «European Roadmap: Smart
systems for automated driving,» 2015.
[Online]. Available:
B. D. Seppelt e J. D. Lee, «Making
adaptive cruise control ACC limits
visible,» International Journal of Man-
Machine Studies, vol. 65, n. 3, pp. 192
205, 2007.
N. A. Stanton e M. S. Young, «Driver
behaviour with adaptive cruise control,»
Ergonomics , vol. 48, n. 10, pp. 1294
1313, 2005.
R. Bishop, Intelligent Vehicle
Technology and Trends, Boston: Artech
House, 2005.
J. G. Hollands, Engineering Psychology
and Human Performance (3rd ed.),
Atlantic City, NJ.: Prentice Hall, 2000.
«Automated Driving Roadmap, version
3.0,» 16 February 2015.
M. Gallo, L. D'Acierno e B. Montella,
«Global Optimisation of Signal
heuristic algorithms for solving
real-scale problems,»
Advances in
Intelligent Systems and Computing,
262, pp. 177-193, 2014.
G. E. Cantarella, S. de Luca e A. Cartenì,
Stochastic equilibrium assignment with
variable demand: theoretical and
implementation issues,»
Journal of Operational Research,
241, n. 2, pp. 330-347, 2015.
E. Cascetta, A. Cartenì, F. Pagliara e M.
Montanino, «A new look at pla
nning and
designing transportation systems as
decision-making processes,»
Policy, vol. 38, pp. 27-39, 2015.
Oktal, «Scaner Driving Simulation
Engine,» Oktal, [Online].
[Consultato il giorno 11 05 2015].
F. Galante, F. Mauriello, A. Montella, M.
Pernetti, M. Aria e A. D'Ambrosio,
«Traffic Calming along Rural Highways
Crossing Small Urban Communities:
Driving Simulator Experiment,» Accident
Analysis & Prevention, vol. 42, n. 6, pp.
1585-1594, 2010.
A. Montella, M. Aria, A. D'Ambrosio, F.
Galante, F. Mauriello e M. Pernetti,
«Simulator Evaluation of Drivers’ Speed,
Deceleration and Lateral Position at Rural
Intersections in Relation to Different
Perceptual Cues,» Accident Analysis &
Prevention, vol. 43, pp. 2072-2084, 2011.
G. Schaefer, D. Lechner, Y. Delanne e V.
Schmitt, «CALLAS: A decisive step
toward validity for 3D vehicle
dynamics,» in Proceedings of the 30th
International Symposium on Automotive
Technology and Automation, Florence
(IT), 1997.
J. Stéphant, A. Charara e D. Meizel,
«Vehicle sideslip angle observers,»
Control Engineering Practice, vol. 15, n.
7, p. 803–812, 2007.
NHTSA, «Preliminary Statement of
Policy Concerning Automated Vehicles,»
30 May 2013. [Online]. Available:
M. Van Ratingen, A. Williams, P.
Castaing, A. Lie, B. Frost, V. Sander, R.
Sferco, E. Seger e C. Weimer, «Beyond
NCAP: Promoting New Advancements in
Safety,» in Proceedings of the
International Technical Conference on
the Enhanced Safety of Vehicles, 2011.
Hyden, Traffic conflicts technique:
state of the art, Green Series: Traffic
safety work with video processing a cura
di, vol. 37, H. H. Topp, A cura di,
University Kaiserslautern, Transportation
Department, 1996, pp. 3-14.
A. Tarko, G. Davis, N
. Saunier, T. Sayed
e S. Washington, «Surrogate measures of
safety,» Subcommitee on surrogate
measures of safety, 2009.
K. Vogel, «A comparative analysis of
hotspot identification methods,»
Analysis and Prevention,
vol. 35, pp.
427-433, 2003.
A. Montella, L. Pariota, F. Galante, L. L.
Imbriani e F. Mauriello, «Prediction of
drivers' speed behavior on rural
motorways based on an instrumented
vehicle study,»
Transportation Research
Record, vol. 2434, pp. 52-62, 2014.
N. Strand, J. Nilsson, M. I. Karlsson e L.
Nilsson, «Semi-
automated versus highly
automated driving in critical situations
caused by automation failures.,»
Transportation Research Part F: Traffic
Psychology and Behaviour, vol. 27, n.
pp. 218-228, 2014.
M. Vollrath, S. Schleicher e C. Gelau,
«The influence of cruise control and
adaptive cruise control on driving
A driving simulator study,»
Accident Analysis and Prevention,
43, n. 3, p. 1134–1139, 2011.
G. Bifulco, L. Pariota, F. Simonelli e R.
Di Pace, «Development and testing of a
fully Adaptive Cruise Control system,»
Transportation Research Part C:
Emerging Technologies, vol. 29, pp. 156
170, 2013.
G. N. Bifulco, F. Galante, L. P
ariota e M.
Russo Spena, «Identification of driving
behaviors with computer-
aided tools,» in
AMSS 6th European Modelling
Symposium, Malta, 2012.
F. Farroni, M. Russo, R. Russo, M. Terzo
e F. Timpone, «A combined use of phase
plane and handling diagram method to
study the influence of tyre and vehicle
characteristics on stability,» Vehicle
System Dynamics,
vol. 51, n. 8, p. 1265
1285, 2013.
F. Farroni, R. Russo, M. Russo, M. Terzo
e F. Timpon
e, «On the influence of anti-
roll stiffness on vehicle stability and
proposal of an innovative semi
magnetorheological fluid anti
roll bar,» in
Raad 2012 Proceedings. 21th
International Workshop on Robotics in
-Adria-Danube Region
, Naples,
M. Carro, M. Russo e F. Timpone, «A
Modern Approach to Design
Optimization and Development of
Vehicle Control Systems,» in Mini
Conference on Vehicle System Dynamics,
Identification and Anomalies, Budapest,
F. Farroni, M. Russo, R. Russo e F.
Timpone, «A physical
analytical model
for a real
time local grip estimation of
tyre rubber in sliding contact with road
asperities,» Proceedings of the Institution
of Mechanical Engineers, Part D:
Journal of Automobile Engineering, vol.
228, n. 8, pp. 955-969, 2014.
F. Farroni, Development of a grip &
thermodynamics sensitive tyre/road
interaction forces characterization
procedure employed in high
vehicles simulation, Naples: University of
Naples, PhD Thesis, 2014.
F. Farroni, D. Giordano, M. Russo e F.
Timpone, «TRT: Thermo racing tyre a
physical model to predict the tyre
temperature distribution,» Meccanica,
vol. 49, n. 3, pp. 707-723, 2014.
F. Farroni, A. Sakhnevych e F. Timpone,
«An Evolved Version of Thermo Racing
Tyre for Real Time Applications,» in
World Congress on Engineering
, London,
G. N. Bifulco, L. Pariota, F. Simonelli e
R. Di Pace, «Real time smoothing of car
following data through sensor fusion
techniques,» Procedia -
Social and
Behavioral Sciences, vol. 20, pp. 524
535, 2011.
G. N. Bifulco, L. Pariota, F. Galante e A.
Fiorentino, «Coupling Instrumented
Vehicles and Driving Simulators:
opportunities from the DRIVE IN2
Project,» in 15th IEEE Intern
Conference on Intelligent Transportation
, Anchorage, Alaska (USA),
E. P. Todosoiev, «The Action Point
Model of the Driver Vehicle System,
Report No. 202A-
3,» The Ohio State
University, Engineering Experiment
Station, Columbus, Ohio, 1963.
R. Wiedemann, «Simulation des
Strassenverkehrsflusses,» Tech. Rep.
Institut für Verkehrswesen. Universität
Karlsruhe Heft 8 der Schriftenreihe des
IfV, Karlsruhe, 1974.
G. N. Bifulco, L. Pariota, M. Brackstone
e M. Mcdonald, «Driving behaviour
models enabling the simulation of
Advanced Driving Assistance Systems:
Revisiting the Action Point paradigm,»
Transportation Research Part C:
Emerging Technologies, vol. 36, pp. 352
366, 2013.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Several studies have developed operating speed prediction models. Most of the models are based on spot speed data, collected by radar guns, pavements sensors and similar mechanisms. Unfortunately, these data collecting methods force the users to assume some invalid assumptions in driver behaviour modeling: constant operating speed throughout the horizontal curves and occurrence of acceleration and deceleration only on tangents. In this study an instrumented vehicle with a GPS continuous speed tracking was used to analyze driver’s behaviour in terms of speed choice and deceleration/acceleration performances and to develop operating speed prediction models. The data used in the study were from a field experiment conducted in Italy on the rural motorway A16 (Naples-Avellino). Models were developed to predict operating speed in curves and in tangents, deceleration and acceleration rates to be used in the operating speed profiles, starting and ending points of constant operating speed in a curve, 85th percentile of the deceleration and acceleration rates of the individual drivers, and 85th percentile of the individual drivers’ maximum speed reduction in the tangent-to-curve transition.The study results show that (a) drivers’ speed was not constant along the curves, (b) the individual drivers’ maximum speed reduction was greater than the operating speed difference in the tangent-to-curve transition, and (c) deceleration and acceleration rates experienced by the individual drivers were greater than deceleration and acceleration rates used to draw the operating speed profiles.
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The target of the activities described in the PhD thesis, fixed in collaboration with a motorsport racing team, with a high performance vehicle manufacturing company and with a tyre research and development technical centre is the development of a procedure able to estimate tyre interaction characteristics, reproducing them in simulation environments taking into account the fundamental friction and thermal phenomena concerning with tyre/road interaction. A first tool, called TRICK, has been developed with the aim to process data acquired from experimental test sessions, estimating tyre interaction forces and slip indices. Once characterized the vehicle, filtering and sensors output correction techniques have been employed on the available data, creating a robust procedure able to generate as an output a "virtual telemetry" and, following a specifically defined track driving routine, to provide tyre interaction experimental curves. TRICK virtual telemetry can be employed as an input for the second tool, TRIP-ID, developed with the aim to identify the parameters of a Pacejka Magic Formula tyre model. The advantage of this kind of procedure is the possibility to simulate the behaviour of a tyre without the bench characterizations provided by tyremakers, with the further benefit to reproduce the real interactions with road and the phenomena involved with it, commonly neglected in bench data. Among such phenomena, one of the most important is surely the effect that temperature induces on tyre performances, especially in racing applications. For this reason a specific model, called TRT, has been realized and characterized by means of proper thermodynamic tests, becoming a fundamental instrument for the simulation of a tyre behaviour as close to reality as possible. One of the most useful features provided by the model is the prediction of the so called "bulk temperature", recognized as directly linked with the tyre frictional performances. With the aim to analyse and understand the complex phenomena concerning with local contact between viscoelastic materials and rough surfaces, GrETA grip model has been developed. The main advantage to which the employment of the grip model conducts is constituted by the possibility to predict the variations induced by different tread compounds or soils on vehicle dynamics, leading to the definition of a setup able to optimise performances as a function of tyre the working conditions. The described models and procedures can cooperate, generating a many-sided and powerful instrument of analysis and simulation; the main features of the available employment solutions can be summarised as follows:  full geometric, thermodynamic, viscoelastic and structural characterization of tyres on which the analyses are focused;  estimation of the tyre interaction characteristic curves from experimental outdoor test data;  definition of a standard track driving procedure that employs tyres in multiple dynamic and thermal conditions;  identification of Pacejka Magic Formula tyre models parameters on the basis of the estimated tyre interaction characteristic curves;  estimation of surface, bulk and inner liner tyre temperatures for variable working conditions and real-time reproduction of tyre thermodynamic behaviour in simulation applications;  correlation of tyre thermal conditions with friction phenomena observable at the interface with road;  prediction of tyre frictional behaviour at tread compound and soil roughness variations;  modelling of tyre interaction by means of MF innovative formulations able to take into account grip and thermodynamic effects on vehicle dynamics;  definition of the optimal wheels and vehicle setup in order to provide the maximum possible performances improvement.
Recently, it has been pointed out that transport models should reflect all significant traveler choice behavior. In particular, trip generation, trip distribution, modal split as well as route choice should be modeled in a consistent process based on the equilibrium between transport supply and travel demand. In this paper a general fixed-point approach that allows dealing with multi-user stochastic equilibrium assignment with variable demand is presented. The main focus was on investigating the effectiveness of internal and external approaches and of different algorithmic specifications based on the method of successive averages within the internal approach. The vector demand function was assumed non-separable, non-symmetric cost functions were adopted and implementation issues, such updating step and convergence criterion, were investigated. In particular the aim was threefold: (i) compare the internal and the external approaches; (ii) investigate the effectiveness of different algorithmic specifications to solve the variable demand equilibrium assignment problem through the internal approach; (iii) investigate the incidence of the number of the links with non-separable and/or asymmetrical cost functions. The proposed analyses were carried out with respect to two real-scale urban networks regarding medium-size urban contexts in Italy.
Previous studies have shown adaptive cruise control (ACC) can compromise driving safety when drivers do not understand how the ACC functions, suggesting that drivers need to be informed about the capabilities of this technology. This study applies ecological interface design (EID) to create a visual representation of ACC behavior, which is intended to promote appropriate reliance and support effective transitions between manual and ACC control. The EID display reveals the behavior of ACC in terms of time headway (THW), time to collision (TTC), and range rate. This graphical representation uses emergent features that signal the state of the ACC. Two failure modes-exceedance of braking algorithm limits and sensor failures-were introduced in the driving contexts of traffic and rain, respectively. A medium-fidelity driving simulator was used to evaluate the effect of automation (manual, ACC control), and display (EID, no display) on ACC reliance, brake response, and driver intervention strategies. Drivers in traffic conditions relied more appropriately on ACC when the EID display was present than when it was not, proactively disengaging the ACC. The EID display promoted faster and more consistent braking responses when braking algorithm limits were exceeded, resulting in safe following distances and no collisions. In manual control, the EID display aided THW maintenance in both rain and traffic conditions, reducing the demands of driving and promoting more consistent and less variable car-following performance. These results suggest that providing drivers with continuous information about the state of the automation is a promising alternative to the more common approach of providing imminent crash warnings when it fails. Informing drivers may be more effective than warning drivers.
In this paper the Global Optimisation of Signal Settings (GOSS) problem is studied and a meta-heuristic algorithm is proposed for its solution. The GOSS problem arises when the parameters of all (or some) signalised intersections of a network are jointly optimised so as to minimise the value of an objective function (such as total travel time). This problem has been widely studied elsewhere and several algorithms have been proposed, mainly based on descent methods. These algorithms require high computing times for real-scale problems and usually lead to a local optimum since the objective function is hardly ever convex. The high computing times are due to the need to perform traffic assignment to determine the objective function at any iteration. In this paper we propose a multi-start method based on a Feasible Descent Direction Algorithm (FDDA) for solving this problem. The algorithm is able to search for a local optimal solution and requires lower computing times at any iteration. The proposed algorithm is tested on a real-scale network, also under different demand levels, by adopting different assignment algorithms proposed in the literature. Initial results show that the proposed algorithms perform well and that computing times are compatible with planning purposes also for real-scale networks.
This paper deals with the frictional behaviour of a tyre tread elementary volume in sliding contact with road asperities. Friction is supposed as composed by two main components: adhesion and deforming hysteresis. The target, fixed in collaboration with a motorsport racing team and with a tyre manufacturing company, is to provide an estimation of local grip for on-line analyses and real time simulations and to evaluate and predict adhesive and hysteretic frictional contributions arising at the interface between tyre tread and road. A way to approximate asperities, based on rugosimetric analyses on macro and micro scale, has been introduced. The adhesive component of friction has been estimated by means of a new approach based on two different models found in literature, whose parameters have been identified thanks to a wide experimental campaign previously carried out. The hysteretic component of friction has been estimated by means of an energy balance taking into account rubber viscoelastic behaviour, characterized by means of proper DMA tests, and internal stress / strain distribution due to indentation with road. Model results are finally shown and discussed and the validation experimental procedure is described. The correct reproduction of friction phenomenology and the model prediction capabilities are highlighted making particular reference to grip variability due to changes in working conditions.
Conference Paper
DRIVE IN2 is an automotive research project within the field of Intelligent Transportation Systems, especially Advanced Driving Assistance Systems (ADAS). The project originates from the idea that the development of new ADAS and evaluation of their effect have to take drivers into account, as well as their behavior while driving: the benefits of adopting new in-vehicle technologies depend also on their adoption and usage by drivers. To this aim, the project develops a Driver-In-the-Loop framework to position observation of the drivers at the center of the research activities. Observations are carried out by coupling different research tools, namely instrumented vehicle and driving simulators. The premise and methodological framework of the research project are presented and discussed. Some preliminary activities with particular reference to validating the driving simulation environment are also described.
In the field of Intelligent Transportation Systems (ITS), one of the most promising sub-functions is that of Advanced Driver Assistance Systems (ADAS). Development of an effective ADAS, and one that is able to gain drivers' acceptance, hinges on the development of a human-like car-following model, and this is particularly important in order to ensure the driver is always 'in the (vehicle control) loop' and is able to recover control safely in any situation where the ADAS may release control. One of the most commonly used models of car-following is that of the Action Point (AP) (psychophysical) paradigm. However, while this is widely used in both micro-simulation models and behavioural research, the approach is not without its weaknesses. One of these, the potential redundancy of some of the identified APs, is examined in this paper and its basic structure validated using microscopic driving behaviour collected on thirteen subjects in Italy. Another weakness in practical application of the Action Point theory is the identification of appropriate thresholds, accounting for the perception, reaction and adjustment of relative speed (or spacing) from the leading vehicle. This article shows that this identification is problematic if the Action Point paradigm is analysed in a traditional way (car-following spirals), while it is easier if the phenomenon is analysed in terms of car-following 'waves', related to Time To Collision (TTC) or the inverse of TTC. Within this new interpretative framework, the observed action points can be observed to follow a characteristically linear pattern. The identification of the most significant variables to be taken into account, and their characterisation by means of a simple linear pattern, allows for the formulation of more efficient real-time applications, thereby contributing to the development and diffusion of emerging on-board technologies in the field of vehicle control and driver's assistance.
Conference Paper
Identification of driving behavior is a crucial task in several Intelligent Transportation Systems applications, both to increase safety and assist drivers. Here we identify driving behaviors by means of an analytical model. In order to estimate the model parameters, data are collected with an instrumented vehicle. The paper presents the model, the procedure for the estimation of the parameters and the results of the proposed framework with respect to a pilot experiment to assess the feasibility and potential of the approach. Some practical implementations of the proposed model are presented. In particular, road safety assessment is introduced in greater depth to show the potential of the approach. For this purpose, a modified (and original) version of some surrogate measures of safety is introduced.