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Preliminary studies on criticalities and opportunities for virtual testing
of driving automation
MARIA RUSSO SPENA
a
, FRANCESCO TIMPONE
b
, FLAVIO FARRONI
b
a
Dipartimento di Ingegneria Civile Edile ed Ambientale
b
Dipartimento di Ingegneria Industriale
Università degli Studi di Napoli Federico II
Via Claudio 21, 80125 Napoli
ITALY
maria.russospena@unina.it
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
changed.
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
changing).
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
trajectories.
4 The model for the vehicle dynamics
To carry out a more realistic simulation a vehicle
dynamics model has been coupled with the ADAS
model.
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
DRIVE IN
2
( [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.
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