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Rain drastically modifies the visual environment of road users, particularly at night. It changes visibility through its effects on headlights, windshield, pavement and markings. Rain lessens the performance of headlamps and other light sources by filtering part of their luminous power, thus reducing the illuminance on the roadway ahead of the vehicle. Rain affects the capacity of the driver to see through the windshield. Rain also affects visibility by changing the amount of headlight retro-reflected by the road surface toward the driver. The film of water on the pavement makes delineation and pedestrian crossing markings almost invisible by cancelling the retroreflective properties of the beads in the painting materials. The same physical phenomenon makes the pavement appear darker than in dry conditions. This is a brief list of commonplace facts on the visual effects of rain (10). In the following, we seek to explicit the physical and psychophysical ground of these facts, based on scientific references when available. The outline of the paper is as follows: in the first section, we study the visual effects of the falling rain; in the second section, we tackle the visual effects of sprayed water; in the third section, we list the properties of wet materials, specially pavement markings; finally, we propose synthetic diagrams describing the effects of rain on roadway visibility.
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Review of the Mechanisms of Visibility Reduction by
Rain and Wet Road
Nicolas Hautière, Eric Dumont, Roland Brémond, Vincent Ledoux
Keywords: rain, wet road, visibility, windshield
1 Introduction
Rain drastically modifies the visual environment of road users, particularly at
night. It changes visibility through its effects on headlights, windshield, pavement
and markings. Rain lessens the performance of headlamps and other light
sources by filtering part of their luminous power, thus reducing the illuminance on
the roadway ahead of the vehicle. Rain affects the capacity of the driver to see
through the windshield. Rain also affects visibility by changing the amount of
headlight retro-reflected by the road surface toward the driver. The film of water
on the pavement makes delineation and pedestrian crossing markings almost
invisible by cancelling the retroreflective properties of the beads in the painting
materials. The same physical phenomenon makes the pavement appear darker
than in dry conditions.
This is a brief list of commonplace facts on the visual effects of rain [10]. In the
following, we seek to explicit the physical and psychophysical ground of these
facts, based on scientific references when available.
The outline of the paper is as follows: in the first section, we study the visual
effects of the falling rain; in the second section, we tackle the visual effects of
sprayed water; in the third section, we list the properties of wet materials,
specially pavement markings; finally, we propose synthetic diagrams describing
the effects of rain on roadway visibility.
2 Visual effects of the falling rain
2.1 The nature and microstructure of rain
Rain is a population of water droplets falling, interacting with each other and with
the environment. While falling, a rain drop undergoes rapid shape distortions.
This shape is size-dependent. Small drops are usually spherical, but as their size
grows, they tend to a spherical oblate shape. The shape of a rain drop is
described in [2] as the cosine distortion of a sphere at the tenth order:
( )
θ θ
=
 
= +
 
 
10
1
( ) 1 cos
n
n
r a c n (1)
where a is the radius of the undistorted sphere, c
1,…,10
are coefficients which
depend on the radius of the drop, and θ is the elevation polar angle. θ=0
corresponds to the direction of the rain. Shapes of various sized drops are
presented in Figure 1a. Rain drops come in a wide range of sizes. Their size
distribution is often modeled using Marshall-Palmer distribution [16]:
−Λ
=
0
( )
a
N a N e
(2)
where a is the radius of a drop, N(a) the number of drops per volume unit with
sizes between a and a+da, N
0
=0.08 cm
-4
, Λ=82R
-0.21
and R is the rain density in
mm.h
-1
. This distribution is plotted in Figure 1b.
(a) (b)
Figure 1: (a) shapes of rain drops. (b) Marshall-Palmer rain drop size distribution.
Rain drops fall at a constant speed called the terminal velocity. An empirical
study is presented in [11] on the terminal velocity of rain drops for different drop
sizes. This data are approximated in [27] with the following function:
(
)
− ×
= − 3 1,15
1,57 10
9,4 1
a
term
V e (3)
2.2 Light scattering in rain
Some experiments were conducted in an attempt to relate optical extinction on a
long distance to rain density. [26] reports the results from five other
investigations, and concludes that the optical depth τ obtained from the
measurements corresponds within 25% to the value computed on the basis of
different rain drop distributions. The optical depth is computed by integrating the
extinction coefficient k
s
along the optical path L as follows:
τ
=
0
L
s
k dz
(4)
The general relation found between the extinction coefficient k
s
(m
-1
) and rain
intensity R (mm.h
-1
) is the following:
γ
=
s
k aR
(5)
where
a
and
γ
differ with respect to the location and the optical devices used in
the experiments. Experimental curves are plotted in Figure 2.
Finally, [21] measured the back-scattering of a light source in rain. Based on
these measurements, they proposed an empirical model. However, the relevance
of this model was not tested since.
Figure 2: Different experimental curves relating the atmospheric extinction coefficient and the
intensity of the rain.
2.3 Consequences on roadway visibility
Light scattering in rain is rather limited. Using an analogy with the visual effects
of fog, the effects of scattering in rain can be a problem for driving when the
meteorological visibility (V
met
=3/k
s
) falls below 400 m, which is equivalent to a
300 mm.h
-1
rain according to Eq. (5). Such levels of precipitation are seldom
observed.
3 Visual effects of sprayed water
3.1 Visual effects of rain on the windshield
To the best of our knowledge, there is no analytic model for the overall reduction
of visibility induced by rain on the windshield. [9] focus on the appearance of rain
drops. They show that the field of view refracted by a spherical drop is about
165°, and assimilate the drop with a fish-eye lens. The corresponding optical
diagram is presented in Figure 3a.
(a) (b)
Figure 3: Optical diagram illustrating the fish-eye lens effect created by a rain drop.
One can assume rain drops on the windshield to have a similar effect, save for
the deformation of the spherical drops on the windshield. The drops on the
windshield roughly reflect the road environment, which is illustrated in Figure 3b.
On the other hand, several experimental studies on wiper usage focused on
object visibility and seeing distance. Some driver visibility studies were restricted
to stationary vehicles in artificial rain [13][18]. Other studies were conducted in
actual rain. They showed that wipers do not interfere with the perception of the
road scene with respect to saccadic eye movements [6]. Moreover, seeing
distances are significantly reduced when rain intensity increases [3][12][17]. In
particular, [3] investigated the visibility distance of target vehicles under natural
downpours. The observers were onboard a vehicle, and notified when they
detected a target car while their wipers were engaged or recently stopped. These
experiments showed that detection distances decrease significantly with ambient
lighting and that visibility distance decreases as rain intensity increases. Visibility
distance was found to be lower for observers onboard a moving vehicle (vs.
stationary) because of the higher concentration of water on the windshield.
Based on these experiments, a model was proposed for the visibility distance of
cars through the windshield in rain condition in daytime condition. This model can
be simplified by:
(
)
12
0
b
c
c L
D c rt e
= (6)
where c
0
, c
1
, c
2
are strictly positive constant values, rt characterizes the
accumulation of rain water on the windshield, r being the intensity of the rain and
t the time between wiper movements, and L
b
the background luminance.
3.2 Visual effects of water sprayed by other vehicles
The water sprayed by vehicles has undeniable effects on visibility. [8] studied
these effects. Unfortunately, no model came out of it, because of several
experimental difficulties which impeded the identification of prevailing
parameters. Other studies showed that splash and spray is reduced by 95% on
porous asphalt compared to other ordinary pavement surfaces. Figure 4, taken
from [20], shows a heavy vehicle on a road section with and without porous
asphalt. However, such a figure is dubious since it is not backed up with a
measurement protocol. The most rigorous works have been conducted for the
development of heavy vehicle spray reduction devices. Even though these
researches do not directly address driver visibility, the metering systems which
were used to study such devices might be used to investigate this particular
problem. A synthesis of the works conducted before 2000 is proposed in [15].
Figure 4: Water sprayed by a heavy vehicle on ordinary and porous asphalt [20].
4 Light reflections on wet materials
4.1 Water at the surface
The water on a surface (e.g. a puddle on the pavement) makes it specular
because of the smooth air-water interface. Optical interactions on such a surface
are governed by Fresnel equation for dielectric materials:
θ θ
=
1 1 2 2
sin sin
n n (5)
A film of water on a Lambertian surface can also make the surface appear
darker [14]. This is mainly caused by internal reflections at the water-air interface.
Part of the light reflected by the Lambertian surface is reflected back when it hits
the water-air interface. This light is again subject to absorption by the material of
the surface before being reflected again. This leads to a sequence of absorptions
which darkens the surface.
(a) (b)
Figure 5: Illustration of the Fresnel equation: a material with a layer of water on its surface reflects
less light because of internal reflections at the water-air interface.
4.2 Water underneath the surface
The presence of water underneath a surface is another important factor
influencing the appearance of the material. In the case of pulverulent materials,
(sand or limestone), water can penetrate inside holes formerly filled with air. This
modifies the reflection properties of the material, favoring forward scattering [25].
The main reason is that the refraction index of water is higher than the index of
the air, and usually closer to the index of the material. This means that a ray of
light entering the material is less refracted because the refraction index is more
homogeneous when the material is wet. As illustrated in Figure 6, the
consequence is that the ray undergoes more scattering before leaving the
surface. This increases the total amount of absorbed light, and the overall effect
is a material with reduced reflectivity.
Figure 6: Shortest path for a ray of light to enter and exit the material with (left) a 90° mean
scattering angle and (right) a 30° mean scattering angle.
4.3 Consequences on roadway visibility
Rain changes the visual aspect of the road. The road surface appears more
specular or darker, depending on the observation angle. This can be dazzling for
the driver, especially in daytime with the sun at grazing angles, or at night with
opposing headlights. With visual performance impaired by glare, it is more
difficult for the driver to detect hazards. The visibility of retro-reflective road
markings is also particularly impaired. These markings are designed to send
headlight back toward the vehicle. They are usually made of a painting onto
which glass beads with a high refraction index (between 1.5 and 2.5) are
encrusted (Figure 7a). The optical properties of the beads are described in more
details in [29] and [23]. In daytime, on wet roads, retroreflective materials reflect
sunlight, and sometimes appear darker than the pavement. At night, when the
road is slightly wet, the retroreflective efficiency of the beads is reduced, as
illustrated in Figure 7b. When the road is wet and the water layer is higher than
the size of the beads, headlight is mostly reflected at the air-water interface
(Figure 7c), so markings may disappear. This is why all weather markings were
developed. A nice introduction to this particular issue is proposed in [5].
(a) (b) (c)
Figure 7: Optical mechanisms governing retroreflection on pavement markings with encrusted
beads in (a) dry, (b) humid and (c) wet conditions.
5 Conclusion
In this paper, we have provided physical explanations for the effects of rain on
roadway visibility. These explanations are based on either optical or
psychophysical models. We have classified the visual effects of rain into three
main categories. The first category concerns light scattering by rain drops. The
second category concerns splash and spray. It encompasses both rain falling on
the windshield and water splashed by other vehicles. The third category
concerns wet road surfaces, especially road markings, whose appearance is
modified by the water layer.
Figure 8: Visual effects of rain in daytime
In the end, the reduction of visibility caused by rain and sprayed water results
from a combination of these three categories of effects. We can estimate a priori
that the effects of the second and third categories have the highest impact on
visibility, scattering effects being negligible for common intensity downpours. To
give a schematic view of these effects, we propose two diagrams. Figure 8
shows the various effects of rain on daytime visibility: reduced transmission,
atmospheric veil, wet windshield, spray and specular reflections. Figure 9 shows
the nighttime rain situation, with the same effects as in daytime plus specular
reflection overcoming retroreflection on the pavement.
From this review of the literature, we have seen that the mechanisms of visibility
reduction by rain and wet road are numerous. The technical solutions to enhance
the perception of the driver in rainy weather are thus also numerous: adaptive
wipers, adaptive headlights, anti-splash and spray devices, porous pavement,
retroreflective markings… A next step should be to focus on headlights. The
quantitative visibility models should enable to define scenarios and to compute
the necessary power to compensate for the visibility loss, or to find alternative
strategies to compensate for the backscattering of light.
(a)
(b)
Figure 9: Nighttime visibility (a) in clear weather (b) in rainy weather.
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... Therefore, these factors could also influence the subjective driving evaluation. In terms of situational factors, (oncoming) traffic (Schiebl, 2008;Teh et al., 2014) and rain (Ashley, Strader, Dziubla, & Haberlie, 2015;Hautière, Dumont, Brémond, & Ledoux, 2009) appear to be relevant. However, even though rain is the weather condition having the greatest impact on the accident risk, weather in general plays a minor role in terms of accident causes (Dingus et al., 2006). ...
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Lately, the development and implementation of automated driving moved to the center of interest in the automotive industry. In this context, one of the central issues – the configuration of adequate trajectories – is mainly tackled using a technical approach. However, it appears that a technically ideal driving performance does not necessarily coincide with the drivers’ subjective preferences. This study strives to determine thresholds of a subjectively accepted driving performance regarding lateral vehicle control. A second objective is to analyze the influence of selected personal and situational factors on these thresholds. An empirical online survey with 161 participants rating video sequences of driving performances was conducted. The video sequences differed not only with regard to the lateral offset of the ego-vehicle but also concerning the weather (sun/rain) and traffic conditions (existence/driving behavior of oncoming traffic). Additionally, the participants’ driving experience and sensation seeking were considered in the data evaluation. To analyze the data, binary logistic regression analyses were calculated. They revealed that the subjective evaluation of driving performances varies primarily depending on the lateral offset of both the ego-vehicle and the oncoming traffic. The results indicate that regarding the lateral offset certain thresholds of subjectively accepted driving performances do exist. Regarding the development of automated driving systems, two issues need to be considered in order to ultimately guarantee user acceptance. First, the subjective thresholds need to be integrated into the systems’ trajectory planning. Second, the oncoming traffic’s driving behavior has to be considered.
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Road lane markings play an essential role in maintaining traffic order and improving traffic safety and efficiency. Active luminous lane markings have emerged with advances in technology recently. However, it is still not completely clear what impact their application will have on drivers. This paper aimed to study the effectiveness of active luminous lane markings on highways at night. A driving simulation experiment was carried out based on advanced driving simulators at Tongji University. The driving simulation experiment involved 31 participants and 9 simulation scenes with 6 different types of lane markings models and the same 2-way highway segment, which was 5300-m long with four 3.75-m wide driving lanes. The study participants drove through the simulated highway while the vehicle operation data and the driver’s eyes changing data were continuously captured. Overall, the pupil area change rate, steering wheel speed, brake pedal force, gas pedal, lane departure, and operating speed indicators were selected to evaluate the effectiveness of the active luminous lane markings. The results are shown as follows: (1) the active luminous lane markings have excellent visual recognition performance at night. Compared with the passive luminous lane markings, the active luminous markings can reduce the mental and physical loads of drivers, increase the early braking distance significantly, improve the lane-keeping ability and smooth the operating speed; (2) for the specific parameter settings of the active luminous lane markings at night, the yellow lane markings are better than the white ones, the point-line-type lane markings are superior to the conventional-type ones, and the blinking frequency is reasonable to set, at a moderate level, as 40 times per min. The results suggest that there are positive effects of active luminous lane markings on the promotion of highway traffic safety and efficiency at night, providing theoretical support for the popularization and application of active luminous road lane markings.
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Individuelle Mobilität ist ein zentrales Thema menschlicher Gesellschaften. In diesem Kontext entwickelte sich der PKW zum primären Fortbewegungsmittel. Die Ausführung der Fahraufgabe stellt in diesem für Fahrerinnen und Fahrer bereits eine hohe Belastung dar. Trotzdem bearbeiten sie oft zusätzliche fahrfremde Tätigkeiten (FFT). Aufgrund der begrenzten menschlichen Kognitionsressourcen kann diese parallele Bearbeitung mehrerer Aufgaben zu Fahrerablenkung führen. Allerdings sind Menschen relativ zu den gefahrenen Kilometern selten in schwere Unfälle verwickelt. Dies legt nahe, dass sie Fähigkeiten zur Unfallvermeidung besitzen. Hierzu konnte Forschung im Kontext des nicht-automatisierten Fahrens zeigen, dass Fahrerinnen und Fahrer ausgehend von ihrem Situationsbewusstsein in Erwartung einer kritischen Fahrsituation bzw. Fahrleistung proaktiv ihre (kognitiven) Ressourcen regulieren und von FFT auf die Fahraufgabe verschieben. Verschiedene theoretische Modelle beschäftigten sich mit diesem regulativen Fahrerverhalten. Aufbauend auf diesen stammt ein ganzheitliches Arbeitsmodell von Schwalm, Voß und Ladwig (2015; Voß & Schwalm, 2015), welches das regulative Fahrerverhalten als funktionale Verhaltensanpassungen konzeptualisiert. Während Fahrerinnen und Fahrer im nicht-automatisierten Fahren für die sichere Ausführung und Überwachung der Fahraufgabe zuständig sind, ist eine solche dauerhafte Involvierung im automatisierten Fahren je nach Automationsgrad nicht mehr erforderlich. Fahrerinnen und Fahrer können sich mit FFT beschäftigten (ab SAE Level 3) und das Situationsbewusstsein der Fahrerinnen und Fahrer sinkt ab. Dennoch dürfen sie jederzeit in die automatisierte Fahrzeugführung eingreifen bzw. werden bis zu einem bestimmten Automationsgrad sogar als Rückfallebene benötigt (bis SAE Level 3). Aus dieser Kombination eines reduzierten Situationsbewusstseins und den möglichen Fahrereingriffen im automatisierten Fahren ergibt sich die Frage, wie Fahrerinnen und Fahrer es schaffen, in solchen Situationen eine sichere Fahrleistung zu gewährleisten und ob sie zu diesem Zweck auch hier auf die funktionalen Verhaltensanpassungen zurückgreifen können. Die vorliegende Dissertation nimmt sich dieser Thematik an. Es wird die Zielsetzung (a) der theoriebasierten und empirischen Ausarbeitung ausgewählter Komponenten des Arbeitsmodells der funktionalen Verhaltensanpassungen von Schwalm et al. (2015; Voß & Schwalm, 2015) als theoretischer Referenzrahmen der Dissertation sowie (b) der spezifischen Untersuchung der Verfügbarkeit und Ausprägung derselben im Rahmen des automatisierten Fahrens verfolgt. Hierzu wurde das Arbeitsmodell zunächst theoriebasiert detailliert. Es wurde herausgearbeitet, dass die funktionalen Verhaltensanpassungen im Mehrfachaufgabenkontext vor allem in Abhängigkeit der Situationswahrnehmung sowie der subjektiven Fahrleistungsbewertung auftreten. Anschließend wurden Annahmen zur Funktionsweise der funktionalen Verhaltensanpassungen im automatisierten Fahren getroffen. Es wurde postuliert, dass Fahrerinnen und Fahrer im Falle von Übernahmen proaktiv die Bearbeitung von FFT zur Freigabe kognitiver Ressourcen reduzieren, welche anschließend für das sichere Lösen der Fahraufgabe genutzt werden. Diese Annahmen wurden anschließend empirisch geprüft. In einer Fahrsimulationsstudie (Studie 1) wurden die funktionalen Verhaltensanpassungen in Abhängigkeit der Situationswahrnehmung in einer sich verändernden Fahrsituation (Übernahmesituation vom automatisierten zum nicht-automatisierten Fahren) untersucht. Es zeigte sich, dass Fahrerinnen und Fahrer gemäß den theoretischen Annahmen vor einer Übernahme proaktiv die FFT reduzierten, hierüber kognitive Ressourcen freigaben und somit eine sichere Übernahme ermöglichten. Die folgenden Studien untersuchten die Idee, dass solche funktionalen Verhaltensanpassungen ebenfalls bei Abweichungen von subjektiv akzeptierten Trajektorien auftreten können. Zunächst wurde das Konstrukt einer subjektiv angemessen empfundenen Fahrleistung diskriminanz- und faktorenanalytisch geprüft (Studie 2) und Schwellenwerte subjektiv akzeptierter Fahrleistungen hinsichtlich des Lateralversatzes in Abhängigkeit diverser Personen- und Situationsfaktoren bestimmt (Studien 3 und 4). Anschließend wurde in den Studien 5 und 6 die Handlungsrelevanz der Fahrleistungsschwellen im Mehrfachaufgabenkontext des automatisierten Fahrens im Fahrsimulator und unter Realbedingungen auf einer Teststrecke untersucht. Bei Überschreitungen der Schwellenwerte einer subjektiv angemessen empfundenen Fahrleistung zeigten sich dort nicht nur Komforteinbußen, sondern auch die erwarteten funktionalen Verhaltensanpassungen. Im Anschluss an eine proaktive Reduktion der FFT griffen Fahrerinnen und Fahrer vermehrt in die automatisierte Fahrzeugführung ein. Die Eingriffe waren teilweise allerdings nicht optimal bzw. sogar sicherheitskritisch. Die Erkenntnisse der sechs empirischen Studien erlaubten abschließend Schlussfolgerungen zu der Verfügbarkeit funktionaler Verhaltensanpassungen im automatisierten Fahren. Weiterhin wurde zukünftiger Forschungsbedarf, wie die fortführende Modellvalidierung oder die konkrete Gestaltung automatisierter Systeme zur Unterstützung der funktionalen Verhaltensanpassungen, identifiziert. Individual mobility is a central theme of human societies. In this context, the car has become the primary means of transport in which the execution of the driving task already constitutes a high task load for drivers. Nevertheless, they often work on non-driving related tasks. Due to the limited human cognitive resources, this parallel processing of multiple tasks can lead to driver distraction. Yet, people are rarely involved in serious accidents relative to the kilometres driven. Drivers thus seem to have abilities for the avoidance of accidents. Research in the context of non-automated driving can support this claim. Studies could show that drivers - based on their situation awareness - proactively regulate their (cognitive) resources and shift them from the non-driving related tasks to the driving task in case they expect a critical driving situation or driving performance. Various theoretical models dealt with this regulative driver behaviour. Based on these, a holistic working model originates from Schwalm, Voß and Ladwig (2015; Voß & Schwalm, 2015). It conceptualises this regulative driver behaviour as functional behavioural adaptations. While in non-automated driving drivers are responsible for the safe execution and monitoring of the driving task, in automated driving such a permanent involvement in the driving task is no longer necessary, depending on the degree of automation. Drivers can work on non-driving related tasks (from SAE Level 3 on) and drivers’ situation awareness decreases. However, they are allowed to intervene into the automated vehicle guidance at any time or are even required as fall-back option up to a certain degree of automation (until SAE Level 3). This combination of a reduced situation awareness and possible driver interventions in automated driving raises the question of how drivers guarantee a safe driving performance in such situations and whether they can make use of functional behavioural adaptations. This doctoral thesis deals with this topic. The objectives are (a) the theory-based and empirical elaboration of selected components of the working model regarding functional behavioural adaptations from Schwalm et al. (2015; Voß & Schwalm, 2015) as theoretical frame of reference of this doctoral thesis, and (b) the specific investigation of the availability and characteristics of these in the context of automated driving. For this purpose, the working model was detailed theory-based. It was highlighted that the functional behavioural adaptations in the multitasking context particularly occur depending on the perception of a situation and depending on the subjective evaluation of a driving performance. Following, assumptions were made on how functional behavioural adaptations work in the context of automated driving. It was postulated that in case of takeovers, driver proactively reduce activity in non-driving related tasks to release cognitive resources, which subsequently are used for the safe execution of the driving task. These assumptions were empirically assessed. In a driving simulation study (study 1), the functional behavioural adaptations were examined as a function of the perception of a changing driving situation (takeover from automated to non-automated driving). According to the theoretical assumptions, drivers proactively reduced the processing of a non-driving related task before a takeover, released cognitive resources, and thus enabled a safe takeover. The following studies investigated the idea that such functional behavioural adaptations can also occur in case of deviations from a subjectively accepted trajectory. Initially, the construct of a subjectively accepted driving performance was examined by means of discriminant function and factor analysis (study 2). Thresholds of such a subjectively accepted driving performance regarding the lateral offset as a function of various personal and situational factors were examined (studies 3 and 4). Subsequently, studies 5 and 6 investigated the relevance for action of the thresholds in the multitasking context of automated driving in a simulator and under real conditions on a text track. In case the thresholds were exceeded, not only comfort losses but also the expected functional behavioural adaptations occurred. After a proactive reduction of the non-driving related task, drivers often intervened in the automated vehicle guidance. Some of the interventions, however, were not optimal or even safety critical. These insights of the six empirical studies allowed for conclusions regarding the availability of functional behavioural adaptations in the context of automated driving. Furthermore, future research needs were identified, for example a continued working model validation or the design of automated systems which support the functional behavioural adaptations.
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