=Paper=
{{Paper
|id=Vol-158/paper-5
|storemode=property
|title=Design of context-aware systems for vehicles using complex system paradigms
|pdfUrl=https://ceur-ws.org/Vol-158/5.pdf
|volume=Vol-158
|dblpUrl=https://dblp.org/rec/conf/context/Rakotonirainy05
}}
==Design of context-aware systems for vehicles using complex system paradigms==
https://ceur-ws.org/Vol-158/5.pdf
Design of Context-aware Systems for vehicles
using complex system paradigms
Andry Rakotonirainy
Centre for Accident Research and Road Safety - Queensland (CARRS-Q)
Queensland University of Technology
Australia
r.andry@qut.edu.au
Abstract. This paper argues that the driving task exhibits the prop-
erties of complex systems. Driving behavior emerges from the intricate
and complex interactions between the driver, the vehicle and the envi-
ronment. An emerging driving behavior could not necessary be linearly
predicted. However a context-aware system could assist the driver in
augmenting the probability of undertaking safe behavior. Unlike exist-
ing context aware systems which isolate one characteristic such as road
or driver workload and concentrate on it in exclusion of the others fac-
tors, this seminal research proposes a context awareness design which
considers the driver, vehicle and environment as a whole. We focus on
the principles that underlie the system in order to model it with the
view of understanding, predicting and improving driver behavior. This
approach places context-aware design within a wider framework that
takes into account information related to the driver, environment and
vehicle. It recommends approaches for building safety critical context-
aware systems. Such an approach aids in the design of safe in-vehicle
context-aware systems.
1 Introduction
Pervasive computing technology such as sensors, actuators, wireless networks
and processors are commonly used to assist humans to perform tasks. Context-
awareness systems have become a growing area of study for pervasive and ubiqui-
tous research communities. Unfortunately context-aware systems have not been
thoroughly used to assist driving tasks. Intelligent Transport System (ITS) and
Advanced Driver Assistance Systems (ADAS) are growing research fields that use
new technology aiming at improving road safety. Context-aware, ITS and ADAS
research exhibit striking similarities. Unfortunately, ITS and ADAS research are
too often conducted without interactions with the pervasive/ubiquitous research
communities.
Almost 95% of the accidents on the road are attributed to the human errors
as a casual factors. In almost three-quarters of the cases human behavior is
solely to blame. On European roads, 40.000 persons are killed and 1.7 Million
are injured every year [13]. Drivers represent the highest safety risk. Computing
assistance can improve situational awareness and reduce driver errors. Although
context-aware systems have great potential to save lives and prevent injuries on
the road, they have not been integrated to safety critical applications such as
cars yet. Concretely, context-aware systems can improve the driver’s handling of
a car by augmenting the awareness of the cars’ state (e.g. following distance), the
environment (e.g. road conditions) and the physiological and psychological state
of the driver (e.g. available attention level). ITS (Intelligent Transport Systems)
could potentially reduce road crashes by up to 40% [23].
Driving is a complex behavior influenced by a wide range of factors in space
and time. Factors include goals, distraction, errors, expectancies, workload, at-
tention, traffic, vehicle safety features, automaticity, fatigue, memory, capabil-
ities, training and experience. We conceptually consider driving behavior as a
complex system in which the environment, driver and vehicle are influencing
factors. Factors influencing the driving task exceed our ability to design, com-
prehend and control. The emerging property of such a system is the driver’s
behavior (what the driver actually do on the road). Emergence is a well known
property inherent to complex systems [11].
Context aware systems aim to improve the safety of driving behavior. How-
ever, the inclusion of a context aware system in a complex system such as driving
does not warrant safe behavior regardless of how well designed the context aware
system is. This is mainly due to the fact that the emerging property of a complex
system such as driver behaviour cannot be easily predicted.
Our design methodology consists of considering context aware systems as
another component of a complex system. The context aware system influence
the driver behaviors’ outcomes toward a safe behavior. The evaluation of the
emerging driving behavior uses Bayesian networks. Bayesian networks observe
the whole complex system as opposed to focusing on individual components
or the interactions between them. To our knowledge, merging concepts from
context awareness, complex systems and driving behavior models in the view of
improving driver behavior has never been attempted.
Section 2 is a reminder of the definition of context and context aware systems.
Section 3 describes the role of context aware systems in vehicles. Section 4 briefly
describes existing driver behavior models. Section 5 shows how driving behavior
is modelized as a complex system. Section 6 briefly described how the benefits
of a context aware system could be evaluated using complex system paradigms
and Bayesian networks.
2 Context and context-aware systems
Context is any measurable and relevant information that can be used to char-
acterize the situation of an entity (e.g. driver) [3]. Context is highly dynamic in
space and time. Context could be considered as a ”setting” in which interactions
unfold [4]. The setting has the dual role of creating and constraining interactions.
Context-aware systems use Information Communication Technologies (ICT)
to provide a greater awareness of relevant information about the physical worlds
in order to assist the information recipient in the decision making process.
Drivers operate in highly dynamic environments or contexts. Existing context-
aware systems use context such as task at hand, location, user preferences and
device capabilities [6, 25, 2, 9] to deliver relevant information to the user. The
relevance of the information is relative to a particular circumstance or context.
An example would be the context of a technical conversation in which terms
have particular meanings that are different to the common meanings used in the
language [14].
3 Role of context aware systems in driving
Intelligent Speed Adaptation (ISA) is a simple form of in-vehicle context aware
system. ISA limits the speed of the vehicle according to the surrounding context.
The relevant context is reduced to the posted speed limit. It is widely recognized
that ISA potentially provides one of the most effective intervention for reducing
excessive speed.
In-vehicle context aware systems aim at taking into account more contextual
information related to the driving task in order to produce adapted or customized
actions. Driving is a complex, continuous, multitask processing that involves
driver’s cognition, perception and motor movements. Driving tasks are classified
in two categories both of which can be assisted by a context-aware system:
– Primary task: Tasks restricted to longitudinal/lateral vehicle control and
vigilance.
– Secondary task: Other tasks that do not require continual performance.
Monitoring a car requires dynamic allocation of attention to perform tasks.
Drivers’ attention oscillates between the primary and secondary driving tasks.
Drivers’ safety, cognitive and motor workloads introduce new complex factors
in the design of context-aware systems. Augmenting drivers situational awareness
can have various effects such as:
– making the driver aware of critical safety information well ahead and give
the driver enough time to react safely.
– overwhelming the driver with irrelevant information. Information can be
inaccurate or missing and confuse the driver.
– distracting the driver from the main critical driving task.
Context-aware systems often assume that users have the cognitive abilities
to acquire the produced context-aware information. Such assumptions may be
valid in desktop environments but are fundamentally inadequate and potentially
un-safe in driving conditions. Conveyed awareness information requires driver’s
attention in order to register it. Registering information cognitively is not an
effortless task.
Drivers’ psychomotor resources are dynamically allocated to different driv-
ing sub-tasks due to constant interruptions. The allocation and the intensity
of attention is determined by the demands or stimuli (cues) from the environ-
ment (inside and outside the car) and the psycho-physiological capabilities of
the driver. Certain information such as warning about the imminence of a crash
requires the full attention from the driver. The warning is expected to be fol-
lowed by driver’s motor reaction. Other type of cues such as in-coming E-mails
do not necessary need immediate attention.
Human Computer Interaction (HCI) research has studied the ways people
interact with computing devices. Several theories and models have been put for-
ward to analyze human-computer interactions [26, 18, 17, 19, 22]. Psychologists
have studied models of attention in human computer interactions [10, 16] and
in driving conditions [8]. Despite such considerable works, the design of most
context-aware systems have not taken into account the human attention re-
quired to register the information to be delivered from a context aware system.
To our knowledge, none has explicitly integrated context-awareness and human
attention design in automotive environments.
4 Driver behaviour models
Driving behavior models explain and predict the behavior of drivers. Existing
models are largely subjective and based on self-report scales [24]. They strongly
emphasize the driver’s cognitive state and have incorporated important behav-
ioral concepts such as motivation, task capability [5], belief (theory of planned
behavior) [1] or risk assessment. However, motivational models such as risk com-
pensation [28], risk threshold [20] or risk avoidance remain highly subjective
concepts. The cited work simplifies the driving task by focusing on the cognitive
aspect of driving. They do not explicitly take take into account other important
factors or context related to the driver (e.g attention), environment and vehi-
cle. Such simplification is useful for a designer who is an expert in a particular
discipline such as traffic psychology who wants to define a model that brings
all psychology related facts together to explain driving behavior. However the
validity of such a simplified approach is debatable when the broader context
related to environment, driver and vehicle which are very dynamic, vague, not
all known and complex in space and time, are required to explain a behavior.
Recently, statistical models have been used to predict driving manoeuvres
and behaviors. They use Bayesian or HMM (Hidden Markov model) [21] [12].
However these statistical models are based on information related to vehicle and
do not fully integrate information related to the environment and the driver.
5 Modeling driving as a complex system
We aim to build a driver behavior model which reflects real world driving. We
consider driving behavior as an emergent property of a complex system featuring
subsystems representing the driver, the environment, the vehicle and the in-
vehicle context aware system.
5.1 Complex system
A complex system is a system in which the number of states that can be antici-
pated or understood can not be accurately identified or enumerated. A complex
system consists of dependent components or sub-systems. Components exhibit
inter-relationships and interdependence. Some behaviors or patterns emerge from
a complex system as a result of the patterns of relationship between its com-
ponents. The emerging behavior cannot be identified or deduced by observing
individual components of the system. Complex systems research seek to under-
stand (i) how a large number of factors of different types are combined and
(ii) how components influence each other to collectively produce an aggregated
phenomenon (emergence). Complex systems try to understand the nature of
emerging behavior and the conditions which will help it to occur. Emerging
patterns arise from the intricate inter-twining or inter-connectivity of elements
within a system and between a system and its environment [15]. Mathematically
speaking, the emerging phenomena cannot be regarded as the behavior of some
average of individual components. Emergence is rarely a simple, linear cause
and effect relationship between the elements of a complex system. An event may
cause a large effect/deviation on the future behavior the system (e.g butterfly
effect), or no effect at all.
Relationships between components have a history and contain feedback loops.
The effects of a component actions are fed back to the component and this, in
turn, affects the way the component behaves in the future. For example relation-
ships between the driver and vehicle build specific patterns of vehicle handling
which changes with feedback, experience and history. The feedback could be
negative (damping) or positive (amplifying). Feedback are key ingredients of
complex systems. Feedback and behavior form a history which is an important
component of a complex system such as driver behavior.
Complex systems are open systems with boundaries that are difficult to de-
termine. Information is dynamically and constantly being imported and exported
across the system boundaries. The boundaries are often determined by the ob-
server’s needs rather than any intrinsic property of the system itself. Driving
behavior exhibits such open properties. That is, the complete set of factors in-
fluencing the behavior cannot be bounded.
5.2 Complex systems and occupational safety
The previous section models driving task as a complex system featuring three
interacting boundless sub-systems labeled as environment, driver and vehicle.
Several separate theories related to each sub systems has been written to explain
crash and crash risks. Unfortunately each individual theory is restricted in scope.
At another level of analysis, drivers cognitive state could be influenced by
occupational safety. Factors such as safety management, social stressors, anxiety
has been shown to have an influence on accident rate [7]. Nonlinear dynamical
theory (cusp model) has been used to correlate crash rate and occupational safety
parameters [7]. Non linear and complex system theories offer several computa-
tional concepts for modeling and predicting occupational accidents in traspora-
tion [7].
6 Approach to improve driver behavior
This section shows how we use Bayesian network to observe and predict safe
driving behavior.
6.1 Observing individual behavior with Bayesian network
An accurate prediction of driver behavior requires an understanding of a large
number of conditions (contexts) which cannot be quantified with individual ob-
servational measures, such as recording ocular movement, traffic flow, or cog-
nitive activities. Furthermore, such an accurate prediction is impossible in a
complex system such as driving behavior.
However, the probability of a certain behavior to occur, for a given period of
time can be obtained by applying probability theory. We use Bayesian networks
to evaluate the probability of a certain behavior to occur. A Bayesian network is
a graphical representation of the underlying probabilistic relationships of a com-
plex system. A Bayesian network learns and progressively increase the prediction
level of confidence. It is based on solid mathematical foundations.
Our approach consists of gathering relevant contextual information related
to the driver, the vehicle and the environment in the real driving condition with
sensor technology. Such information is fused and analyzed to contextualize an
action as described in Figure 1. These contextualized actions are represented in
a Bayesian Conditional Probability Table (CPT).
The study of a set of individual driving behaviors as a complex system could
reveal common characteristics among different drivers and will allow a greater
understanding of this complexity.
The observation is a learning process that can improve the prediction capabil-
ity. We have pointed out the prevalence of uncertainty in a driving environment.
Thus we use Bayesian learning as a form of uncertain reasoning from observa-
tions. Bayesian learning simply calculates the probability of the occurrence of
an event, given an observation, and makes predictions on that basis.
6.2 How context-aware systems improve driver behavior
The ability of context awareness systems to improve driving behavior relies on
the understanding of the whole driving task, not individual components of the
system. However we need to build the context-aware system which can sense
contextual information about the driver, environment and vehicle. The context
aware system assists the drivers in the decision making process in which they
(i) assess the situation, (ii) identify available options, (iii) determine the costs
and benefits of each and (iv) select the option with the lowest costs and highest
benefits [27].
The description of a method for building a context-aware system is out of
the scope of this paper. It is a middleware exercise, details of the architecture
could be found in [9] and shown in Figure 1. Figure 1 shows that a driving
situation is created by the context aware system. The context aware system
gathers information using different types of sensor technology. It also gathers
psychological information from questionnaire filled by the driver. The context
aware system feeds the information into a bayesian network from which we can
evaluate future behavior.
The evaluation of the context-aware system is fundamentally different from
traditional approaches which mainly consists of performance and usability eval-
uation. Our method for evaluation consists of using the Bayesian network to
observe and understand the driving situation as a whole. Different configura-
tions of the context aware system will be trialled until the whole complex sys-
tem exhibits a higher probability of stable safe behavior. A good context aware
configuration would be a system that provides the high probability of safe be-
havior. The definition of what is a safe driving behavior is an on-going research.
However we take the assumption that a safe behavior is a behavior when the
number of errors is minimal.
Fig. 1. Observing driver behaviour
7 Conclusion
Driving task is a highly complex behavior influenced by a large number of factors.
The use context aware systems in cars aims at improving driver behavior. How-
ever the evaluation of the benefits brought by context-aware systems is difficult
due to the complexity of driving task. This conceptual paper uses complex sys-
tem paradigms to configure the design of a context aware system and evaluates
its benefits.
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