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Generating Semantic Trajectories Using a Car Signal Ontology

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In this paper, we use semantic technologies for enriching trajectory data in the automotive industry for offline analysis. We proposed to re-use a combination of existing ontologies and we designed a Vehicle Signal Specification ontology to provide an environment in which we developed an application that analyzes the variations of signal values and enables to infer the "driving smoothness" that we represent as additional annotations of semantic trajectories.
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Generating Semantic Trajectories
Using a Car Signal Ontology
Benjamin Klotz
EURECOM & BMW Research, New Technologies,
Innovations
Sophia Antipolis, France
klotz@eurecom.fr
Raphaël Troncy
EURECOM
Sophia Antipolis, France
troncy@eurecom.fr
Daniel Wilms
BMW Research, New Technologies, Innovations
Munich, Germany
daniel.dw.wilms@bmw.de
Christian Bonnet
EURECOM
Sophia Antipolis, France
bonnet@eurecom.fr
ABSTRACT
In this paper, we use semantic technologies for enriching trajectory
data in the automotive industry for oine analysis. We proposed
to re-use a combination of existing ontologies and we designed a
Vehicle Signal Specication ontology to provide an environment in
which we developed an application that analyzes the variations of
signal values and enables to infer the “driving smoothness” that we
represent as additional annotations of semantic trajectories.
CCS CONCEPTS
Information systems
Geographic information systems;Sensor
networks;
KEYWORDS
Car Signal ; Ontology ; Semantic Trajectories ; VSS ; SOSA ; SSN
ACM Reference Format:
Benjamin Klotz, Raphaël Troncy, Daniel Wilms, and Christian Bonnet. 2018.
Generating Semantic Trajectories Using a Car Signal Ontology. In WWW
’18 Companion: The 2018 Web Conference Companion, April 23–27, 2018, Lyon,
France. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3184558.
3186962
1 INTRODUCTION
Current and future automotive innovations are based on the inter-
connection of systems such as the vehicle, infrastructure back-ends
and external data sources. Taking autonomous driving as an exam-
ple, it has to rely on an intelligent and dynamic interconnection of
the vehicle’s data with knowledge about its environment despite
the diversity of sources, formats, and sensors involved. To tackle
the challenge of data integration and reuse in vehicular intelligent
interconnections, we use semantic technologies. Already largely
used on the web, especially by search engines which promote the
This paper is published under the Creative Commons Attribution 4.0 International
(CC BY 4.0) license. Authors reserve their rights to disseminate the work on their
personal and corporate Web sites with the appropriate attribution.
WWW ’18 Companion, April 23–27, 2018, Lyon, France
©
2018 IW3C2 (International World Wide Web Conference Committee), published
under Creative Commons CC BY 4.0 License.
ACM ISBN 978-1-4503-5640-4/18/04.
https://doi.org/10.1145/3184558.3186962
schema.org vocabulary
1
, semantic technologies are also more and
more used to integrate physical devices in the Internet of Things
2
and in the automotive industry
3
. Combining ontologies may enable
queries about complex driving contexts, including sensor values,
location, time and external data.
For simplication, we consider that there are two types of data
4
that can be linked to a vehicle: static and dynamic. Static data
corresponds to the car’s attributes such as its model, number of
wheels, dimensions and the list of embedded sensors. Dynamic data
is what car sensors produce on a continuous basis. It depends both
on time and space. In addition, embedded sensors only produce
dynamic data if they are instantiated in the static car attributes.
A number of ontologies can describe cars’ attributes and cong-
uration. This includes auto.schema.org, still in development and
based on four existing ontologies:
Car option ontology
5
for the commercial aspects of oers
for sale or rental. It contains 12 classes and 19 properties.
Vehicle sales ontology
6
(VSO) for describing cars, boats,
bikes, and other vehicles for e-commerce with 33 classes
and 54 properties.
Used cars ontology
7
for describing aspects of used cars for
e-commerce with 22 classes and 46 properties.
Volkswagen Vehicle Ontology
8
for describing Volkswagen-
specic features of automobiles with 30 classes and 50 prop-
erties. Its interest is limited to the domain of the e-commerce
for one brand.
Other ontologies can describe trajectories, and therefore dynamic
spatio-temporal data:
datAcron
9
for describing semantic trajectories as succession
of sub-trajectories associated with points or regions, mostly
associated with the aircraft domain [
8
]. It contains 552 classes
and 273 properties.
1http://schema.org
2http://iot.schema.org
3http://auto.schema.org
4www.automotive-ontology.org
5http://semanticweb.org/wiki/Car_Options_Ontology.html
6http://www.heppnetz.de/ontologies/vso/ns
7http://ontologies.makolab.com/uco/ns.html
8http://www.volkswagen.co.uk/vocabularies/vvo/ns
9http://ai-group.ds.unipi.gr/datacron_ontology/
Baquara2
for describing semantic trajectories as succession
of episodes associated with points or regions, mostly asso-
ciated with the automotive and tourism domain [
6
]. It has
more than 100 classes and 200 properties but is not available
online.
STEP
10
(Semantic Trajectory EPisodes) for describing se-
mantic trajectories with dierent levels of granularity and
attach labels to them [
12
]. It contains 21 classes and 19 prop-
erties. It was developed using ontology design pattern [
7
]
for semantic trajectories [
9
] in order to be combined with
domain ontologies.
Movement Behavior Interpretation is a work aiming at se-
mantically add labels in the domain of tourism to moving
people [2]. The ontology is also not available online.
Some other initiatives tackle specic challenges, especially in
the domain of ADAS (Advanced Driving Assistance Systems) and
context modeling. The Toyota TTI Core ontologies
11
describe ve-
hicle sensors, controls and roads with a core set of signals and
congurations used in ADAS for future autonomous vehicles [
15
].
In the domain of context-aware services, some work have resulted
in ontologies for some essential sensors and entities interacting
with the vehicle [
1
,
11
]. DFKI developed an automotive vocabulary
in order to represent knowledge inside a vehicle and exchange
it with other vehicles [
5
]. It is contextual with sensors, detected
events, as well as user-related with preferences and observations
of behavior. The Ontology for Context Modeling (OCM) is focus-
ing on inferences about a driving context in persuasive computing
environments [14].
A missing aspect from these initiatives is the representation of
a complete car sensor and signal ontology. This gap limits inno-
vations to either automotive domain experts or to a restricted set
of well-known signals and sensors. A second missing aspect is the
representation of semantic trajectories enriched with car attributes
and sensor data for non-specic applications.
In this paper, we aim to answer to the research question: How
should we best combine both static and dynamic car data in order to
annotate semantic trajectories and link that to external knowledge?.
We will focus on two main use cases: generate segments of trajec-
tory annotated according to the evolution of a given signal value,
and a “smooth” driving percentage label attached to a trajectory
when longitudinal and angular acceleration are bound.
Our research is divided into two directions for solving the chal-
lenge of data ow access and querying using RDF. In the rst case,
we only store a sliding window of observations, adding and remov-
ing RDF triples on the y and aim for stream reasoning [
4
], while in
the second case, we never remove triples. We focus in this paper on
the second case since we are interested in having access to history
in our queries.
The remaining of this paper is organized as follows. In Section 2,
we introduce the so-called Vehicle Signal Specication ontology
(VSSo). In Section 3, we present the demonstrated application com-
bining VSSo with SOSA/SSN and STEP. In Section 4, we compare
this combination of ontologies with other initiatives for evaluating
its interest and highlight the usage it enables.
10https://talespaiva.github.io/step/
11http://www.toyota-ti.ac.jp/Lab/Denshi/COIN/Ontology/T TICore-0.03/
2 DESIGN OF THE VSS ONTOLOGY
In order to represent observation of car signals, we adopt the new
SOSA/SSN ontology
12
which is a joint W3C and OGC recommen-
dation [10]. We need to describe car signals and car sensors. Even
though there have been some projects such as [
13
] proposing auto-
motive ontologies, they only partially cover the domain (e.g. assis-
tance and diagnostic of vehicles). The W3C Automotive Working
Group
13
intends to develop Open Web Platform specications for
exposing vehicle signal information and uses the Vehicle Signal
Specication
14
(VSS) from GENIVI as signal vocabulary. The VSS is
a common naming space to decouple the vehicle electrical network
from its original representation to exchange data with third parties.
It contains an extensive set of vehicle parts and signals, dened by
a name, comment, unit and format.
First, we complement the VSS by dening sensor entries for
signals. This is not included in the current specication. Then, in
order to create RDF triples representing the car Attributes,Signals,
Sensors/Actuators,Branches and Units from VSS, we use a converter
from JSON to turtle based on an existing work [
3
] and the rdf-ext
JS library. The generated VSSo ontology
15
contains 311 classes and
6 properties and uses OWL-Lite for its restrictions on sensors and
units. Classes are either
Branches
organized in a tree structure
with the property vss:partOf, or Signals.
This automatic generation is not enough. Several signals share
the same name (e.g. “Switch”) while representing dierent concepts.
Many signals are observable but not actuable, while we dene
vss:ObservableSignal
and
vss:ActuableSignal
as subclasses
of
sosa:ObservableProperty
and
sosa:ActuableProperty
. Some
are not produced nor consumed by a sensor/actuator (e.g attached
to the infotainment system): they will be dened with a
vss:Vir-
tualSensor
to be compliant with the SOSA pattern. For the rest
of them, sensors and actuators are technology-independent inter-
mediate objects. The branch “Vehicle” describes terms related to
the general vehicle and is moved on to of all other branches. The
position of branches (e.g. Mirror.Right.Tilt) is dened as a property
vss:hasPosition of certain branches.
Listing 1 is an extract from VSSo describing
vss:Travelled-
Distance
, a signal measured by a
vss:Odometer
with the unit
unit:Kilometer.
Listing 1: VSSo sample: TraveledDistance signal
v ss : Tr a v e l l e d D i s t a n c e a rd f s : Cl a s s , owl : C l as s ;
r d f s : l a b e l " T r a v e l l e d D i s t a n c e " @en ;
r d f s : com men t " S i g n a l . D r i v e t r a i n . T r a n s m i s s i o n . T r a v e l l e d
D i s t a n c e . O do me te r re a d i n g . " @en ;
r d f s : s u b C l a s s O f v s s : S i g n a l ;
r d f s : s u b C l a s s O f
[ a owl : R e s t r i c t i o n ;
ow l : o n P r o p e rt y s o s a : is O b s e r v e d B y ;
ow l : a l l V a l u e s F r o m v s s : O do me te r . ] ,
[ a owl : R e s t r i c t i o n ;
ow l : o nP ro pe rt y qud t 11: u n i t ;
ow l : a l l V a l u e s F r o m q u dt u ni t 11: K i l o m e t e r . ] .
12https://www.w3.org/TR/vocab-ssn/
13https://www.w3.org/auto/wg/
14https://github.com/GENIVI/vehicle_signal_specication/
15https://github.com/klotzbenjamin/VSSontology
By linking SOSA/SSN to VSSo, we generate RDF triples of obser-
vations of vehicle signals and may attach them to a spatiotemporal
context using the STEP ontology.
3 DEMONSTRATION
In this demonstration, one will control a simulated vehicle, and
will observe signal values that would be automatically transformed
into RDF to enrich a semantic trajectory. This is used to infer labels
about the driver attached to episodes of the trajectory. Labels will
also be visualized: variations of signal values in order to infer a
“smooth” driving label.
We developed a python Flask
16
server combining SOSA/SSN for
observations, VSS for the car signal domain and STEP for the trajec-
tory description. It uses a vehicle conguration le containing a list
of known signals as well as URIs to access their values. The data
source can be real vehicle data in the backend where we access a
subset of pre-selected signals, or a vehicle simulator. In this demon-
stration, we use the OpenXC vehicle simulator
17
which runs on a
local server, provides a control interface (pedals, steering wheel,
gear...), and generates a JSON le of signal values with a frequency
of 100Hz. The following steps are handled by our application:
Add attributes. It reads the vehicle conguration le and gen-
erates a graph representing the static vehicle as it is done in the
e-commerce domain extended with annotations about the signal
and its unit. The vehicle conguration le is parsed to extract a list
of known signals. A blank node is created to represent the vehicle,
and for each signal, the VSS is queried for the signal’s unit, label
and dedicated sensor. This query is done by using triple pattern
fragments
18
which minimize server processing in comparison to
SPARQL. For each signal, the sensor is also a
vso:Feature
of the
vehicle.
Listing 2: Car with a speed sensor as RDF triples
@ pr ef ix q u dt : < h tt p : / / www. q ud t . o rg / 1 . 1 / s che ma / q u dt \ # > .
@ pr e f ix u n i t : < h t t p : / / www. qu d t . o r g / 1 . 1 / vo c ab / u n i t \ # > .
@ pr e fi x r d f s : < h t t p : / / www. w3 . or g / 2 0 0 0 / 0 1 / r d f sc he ma \ # > .
@ pr e f i x s o s a : < h t t p : / / www. w3 . or g / n s / s o s a / > .
@ pr e f ix v so : < h t t p : / / www. p u r l . or g / vs o / ns \ # > .
@ pr e f ix v s s : < h t t p : / / a u t o mo t i v e . e ur e co m . f r / v s s o # > .
v s s : V e h i c l e S p e e d a s o s a : Ob s e r v a b l e P r o p e r t y ;
r d f s : l a b e l " S pe ed " ;
q ud t : U n i t u n i t : K i lo m e t e rP e r H ou r .
[ ] a ge o : F e at u r e , v s o : A u to mo b il e , s o s a : F e a t u r e O f I n t e r e s t ;
vs o : f e a t u r e [ a v s s : S p ee do m et er ,
vso: FeatureValue ;
s o s a : o b s e r v e s v s s : Ve h i c l e S p e e d ] .
Add observations. It reads signal values and extends the static
graph with observations based on observed signal instances. For
a given time span and frequency for a set of known signals, we
request the source to retrieve values. URIs and paths are set in
the conguration le, and the source is expected to be a JSON le.
Then, an instance of type
sosa:Observation
is created and linked
to its signal and sensor. The retrieved value is expressed using the
16ask.pocoo.org/
17https://github.com/openxc/openxcvehicle-simulator
18http://linkeddatafragments.org/
Figure 1: Steering wheel angle visualized with 3 color
known unit, as well as location and time of its FixPoint. Each in-
stance of
sosa:Observation
has the car node as
sosa:FeatureOf-
Interest.
Add trajectory. It creates a trajectory instance, composed of a
raw trajectory (list of FixPoints), and generates labeled segments.
First, an instance of
step:Trajectory
containing a
step:RawTra-
jectory
instance is created with the car node as
step:Agent
. Then,
it is lled with spatiotemporal FixPoints when Observations are
made.
Graph reduction. It extracts trajectories annotated with signal
values by serializing the graph into a CSV le with the format:
(latitude ,lonдitude,t ime,[values])
With a given set of signals, a SPARQL query retrieves a set
of values: (latitude, longitude, time, [signals]). This is especially
relevant for reusing existing trajectory mining algorithms that do
not rely on linked data.
Segment labeling. It adds labels on segments based on simple
rules.
From a graph containing observations, we dene labels based on
simple comparative rules and create
step:Episode
instances in the
step:Trajectory
. They can be qualitative, quantitative or both.
For instance the “smooth” driving label is dened by a longitudinal
and angular acceleration being within given bounds. All segments
that t this requirement are labeled “smooth”, while the others are
labeled “not smooth”.
Plot x points and segments. It displays observation points as
markers, as well as colored segments based on simple comparative
rules and using Google maps with Flask19.
For instance, with a conguration le describing three signals:
VehicleSpeed,EngineSpeed and TravelledDistance, it creates a graph
with an instance of
vso:Automobile
having three sensors linked
by the property
vso:Feature
. We observe the Speed signal and
extract a trajectory annotated with Speed values.
We add a rule to label a segment between two observations, e.g.
Acceleration,Deceleration or ConstantSpeed based on the dierence
of speed between the two observations and display the trajectory
on a map with three colors depending on the segments labels.
19https://github.com/rochacbruno/Flask- GoogleMaps
Table 1: Comparison of the dierent initiatives in regard to the hypothesis: we can enable semantic trajectory enrichments
with signal values for generic applications
Initiative Automotive domain Sensor coverage Semantics Trajectory enrichment Generic
auto.schema.org and its sources Yes Limited Yes No Yes
DatAcron No No Yes Yes No
Baquara2No No Yes Yes No
Movement Behavior No No Yes Yes No
STEP No No Yes No Yes
Toyota TTI Core Yes Limited Yes No Yes
Context-aware services Yes Limited Yes Limited No
DFKI Yes Limited Limited No Yes
OCM Yes Limited Limited No Limited
VSSo+SOSA/SSN+STEP Yes Yes Yes Yes Yes
4 COMPARISON AND USAGE
Our hypothesis being that we can enable semantic trajectory enrich-
ments with signal values for generic applications, there are many
initiatives that solve partially the problem, but not completely as
visible in Table 1. The representations of static car data focus on
the e-commerce domain and do not describe dynamic data. The
trajectory representations tend to be dened for specic applica-
tions and domains and neither cover static nor dynamic car data.
The contextual representations of cars are an interesting trade-o
between static and dynamic car data but have a limited interest in
trajectory representation and work only for non-generic use case
in ADAS.
With respect to our hypothesis, we see that the combination of
VSSo, SOSA/SSN and STEP lls dierent gaps from the existing
initiatives and ts the requirements to enable our use cases: it covers
the automotive domain and its sensor vocabulary with semantics
and trajectory enrichment for generic applications.
This new representation allows web developers to query and
integrate car data with only conventional signal names. Therefore
it enables the development of applications based on the correlation
between what is inferred from car data and external information.
For instance, the correlation between a distraction label, inferred
from car sensors, and events happening around the vehicle, or
between aggressiveness and trac jams.
5 CONCLUSION
In this paper, we have seen how RDF can be used to represent
attributes and signals for a car, and that this solution enables queries
on the complete domain for semantic web developers and non
domain experts. We will improve this demonstration with the future
improvements of auto.schema.org on context-awareness. Future
work will focus on working with online graphs generated on-the-
y and the possibility of interaction with the vehicle for smart
applications.
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ADDITIONAL MATERIAL
GitHub repository:
https://github.com/klotzbenjamin/VSSontology
Screencast: https://youtu.be/LgsNrUNQJdk
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Vehicle architectures have evolved over the past two decades to provide support for data-driven functionalities. The typical approach in this domain has been application-centric, leading to data models that are disparate, repetitive, and hardly maintainable in the long run. As a result, the software complexity increases, while the knowledge remains hidden in the applications' code. We argue that it is essential to enrich the data with standard semantic models to enable a smooth integration of heterogeneous data. In this paper, we propose an ontology-based approach to integrate vehicle-related data. It consists of semantically annotating application-specific data with a well-defined semantic model that considers its streaming-nature. Three applications that use vehicle data are implemented and annotated with the presented procedure. The resulting semantic data is validated with elaborated analytical competency questions that combine application-specific data. Such questions are satisfied with the implementation of queries that follow the patterns of the semantic model. Our work shows that ontology-based data integration is a suitable component for vehicle architectures. The use of this type of integration implies the one-time implementation of queries that are stable over time, reusability of application-specific data, and increased semantics.
... Tandis que les informations issues d'autres capteurs se focalisent sur l'état du véhicule et ses capacités à fournir un service (niveau de carburant, taux de remplissage d'une benne pour un camion à ordure, etc.). De même, la combinaison des coordonnées GPS et des données issues des capteurs des véhicules permet la recherche de comportements de conduite : éco-conduite (Klotz et al., 2018), etc. Cependant, l'envoi par les véhicules de grandes quantités de données chaque seconde entraîne des problématiques classiques liées au big data. Le flux de données arrivant en continu produit une masse de données hétérogènes considérable à traiter. ...
Chapter
Currently, deep learning techniques have become the core of recent research in pattern recognition domain and especially for the handwriting recognition field where the challenges for the Arabic language are stilling. Despite their high importance and performances, for the best of our acknowledge, deep learning techniques have not been investigated in the context of Arabic handwritten literal amount recognition. The main aim of this paper is to investigate the effect of several Convolutional Neural Networks CNNs based on the proposed architecture with regularization parameters for such context. To achieve this aim, the AHDB database was used where very promising results were obtained outperforming the previous works on this database.KeywordsArabic handwritingLiteral amount recognitionOffline recognitionDeep learningResnetVGG
Chapter
How to understand better the knowledge provided by Google results to build future “smart vehicle-centric” applications? What is the knowledge expertise required to build a smart vehicle application (e.g., driver assistance system)? Automotive companies (e.g., Toyota, BMW, Renault) are employing Internet of Things (IoT) and Semantic Web technologies to model the automotive sector. We aggregate this “common sense knowledge” in a automotive dataset which comprises 42 semantics-based projects between 2005 and 2019. The knowledge is already encoded with knowledge representation languages (e.g., RDF, RDFS, and OWL) and supported by the World Wide Web Consortium (W3C). However, only a subset of those projects share their expertise by publishing their ontologies online. For this reason, at the current time or writing, only 16 ontologies are processable. Our innovative Knowledge Extraction for the Automotive Sector (KEAS) methodology analyzes what are the most popular terms required to build a smart car, it provides: (1) a set of keyphrase that are synonyms to smart cars to find domain-specific knowledge, (2) synonyms are used to build a corpus of scientific publications to train the k-means machine learning algorithm, (3) a dataset of smart car ontologies that we collected, is analyzed by the k-means algorithm, and (4) the extraction of the most common terms from the ontology dataset for the automotive sector. Our KEAS findings can be used as a starting point for further domain-specific investigations (e.g., Volvo willing to integrate semantic web) and for future information extraction from structured knowledge.
Poster
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We propose a car signal ontology named VSSo that provides a formal definition of the numerous sensors embedded in car regardless of the vehicle model and brand, re-using the work made by the GENIVI alliance with the Vehicle Signal Specification (VSS). We observe that recent progress in machine learning enables to predict a number of useful information using the car signals and environmental factors such as the emotion of the driver or the detection of dangerous situation on the road. However, there is a lack of a central modeling pattern for describing the dynamic situation of a vehicle, its driver and passengers, moving in an evolving environment. We propose a driving context ontology relying on a patterns composed of events and states to glue together automotive-related vocabularies.
Article
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Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and in this article we analyse its growth. In the first part, we trace the main results obtained so far, by presenting the most prominent studies. We start by an overview of the most relevant studies developed in the context of semantic web, and then we extend the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: in the second part, we present a set of open challenges and issues that stream reasoning will face in the next future.
Thesis
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Location data is ubiquitous in many aspects of our lives. We are witnessing an increasing usage of this kind of data by a variety of applications. As a consequence, information systems are required to deal with large datasets containing raw data in order to build high level abstractions. Semantic Web technologies offers powerful representation tools for pervasive applications. The convergence of location-based services and Semantic Web standards allows an easier interlinking and annotation of trajectories. In this thesis, we focus on modeling mobile object trajectories in the context of the Semantic Web. First, we propose an ontology that allows the representation of generic episodes. Our model also handles contextual elements that may be related to trajectories. Second, we propose a framework containing three algorithms for automatic annotation of trajectories. The first one detects moves, stops, and noisy data; the second one is able to compress generic time series and create episodes that resumes the evolution of trajectory characteristics; the third one exploits the linked data cloud to annotate trajectories with geographic elements from OpenStreetMap. As results of this thesis, we have a new ontology that can represent spatiotemporal phenomena at different levels of granularity. Moreover, our framework offers three novel algorithms for trajectory annotation. The move-stop-noise detection method is able to deal with irregularly sampled traces and do not depend on external data of the underlying geography; our time series compression method is able to find values that summarize a series at the same time that too small segments are avoided; and our spatial annotation algorithm explores linked data and the relationships among concepts to find relevant types of spatial features to describe the environment where the trajectory took place.
Conference Paper
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Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.
Conference Paper
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Within a vehicle driving space, different entities such as vehicles and vulnerable road users are in constant interaction which governs their behaviour. Whilst smart sensors provide information about the state of the perceived objects, considering the spatio-temporal relationships between them with respect to the subject vehicle remains a challenge. This paper proposes to fill this gap by using contextual information to infer how perceived entities are expected to behave, and thus what are the consequences of these behaviours on the subject vehicle. For this purpose, an ontology is formulated about the vehicle, perceived entities and context (map information) to provide a conceptual description of all road entities with their interaction. It allows for inferences of knowledge about the situation of the subject vehicle with respect to the environment in which it is navigating. The framework is applied to the navigation of a vehicle as it approaches road intersections, to demonstrate its applicability. Results from the real-time implementation on a vehicle operating under controlled conditions are included. They show that the proposed ontology allows for a coherent understanding of the interactions between the perceived entities and contextual data. Further, it can be used to improve the situation awareness of an ADAS (Advanced Driving Assistance System), by determining which entities are the most relevant for the subject vehicle navigation.
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Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Now a days, vehicles have become an increasingly important and exciting test bed for ubiquitous computing (UbiComp), however, context-aware vehicle service to enable high adaptation of service to driver, vehicle or even road in ubiquitous environment is still little addressed in the literature. The context management in pervasive computing environments must reflect the specific characteristics of these environments, e.g. mobility, resource-constrained devices, or heterogeneity of context sources. Although a number of context models have been presented in the literature, none of them supports all of these requirements to a sufficient extent at the same time. This paper focuses on building an ontology modeling approach for Context Management in Intelligent Pervasive Middleware for Context-Aware Vehicle Services. To support context-awareness, we embed capabilities of context modeling and context reasoning in an ontology-based context system, which focuses on management of the context and generates a consistent model which promises the common information representation and facilitates a development of context-aware services.
Conference Paper
Motivated by real-life emerging needs in critical domains, this paper proposes a coherent and generic ontology for the representation of semantic trajectories, in association to related events and contextual information, to support analytics. The main contribution of the proposed ontology is twofold: (a) The representation of semantic trajectories at varying, interlinked levels of spatio-temporal analysis, (b) enabling data transformations that can support analytics tasks. The paper presents the ontology in detail, in connection to other well-known ontologies, and demonstrates how data is represented at varying levels of analysis, enabling the required data transformations. The benefits of the representation are shown in the context of supporting visual analytics tasks in the air-traffic management domain.
Conference Paper
In order to use technology to influence human behaviour and promote safer and more fuel efficient behaviour through incentive mechanisms, an instrumented vehicle is developed. The first step is to make it “perceive” the outside world, so extracting knowledge from some data sources such as sensors is crucial. More critically, there is a fundamental need for a standard that would enable knowledge sharing/exchanging among the different entities, e.g., between on-board sensors, in-vehicle controls and traffic management agencies. This paper proposes an Ontology for Context Modelling (OCM) to be used as the world model for driving context representation and reasoning, which can enable a better understanding of traffic context and sensor capability, which is the basis for providing data source to Advanced Driver Assistance Systems (ADAS), V2X (Vehicle-to-Everything) communications and even driving decision making within autonomous vehicles. Through the experiments, we evaluate the capability of the OCM to represent the driving context and the reasoning mechanism to compensate for sensor failures and recognize lane changing and overtaking events. This methodology has significant value for creating standards in autonomous and semi-autonomous cars.
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The analysis of movements frequently requires more than just spatio-temporal data. Thus, despite recent progresses in trajectory handling, there is still a gap between movement data and formal semantics. This gap hinders movement analyses benefiting from available knowledge, with well-defined and widely agreed semantics. This article describes the Baquara2 framework to help narrow this gap by exploiting knowledge bases to semantically enrich and analyze movement data. It provides an ontological model for structuring and abstracting movement data in a multilevel hierarchy of progressively detailed movement segments that generalize concepts such as trajectories, stops, and moves. Baquara2 also includes a general customizable process to annotate movement data with concepts and objects described in ontologies and Linked Open Data (LOD) collections. The resulting semantic annotations enable queries for movement analyses based on application and domain specific knowledge. The proposed framework has been used in experiments to semantically enrich movement data collected from social media with geo-referenced LOD. The obtained results enable powerful queries that illustrate Baquara2 capabilities.
Conference Paper
Cars have been increasingly equipped with technology, meeting the demand of people for safety, connectivity, and comfort. Upcoming HMIs provide access to in-car systems and web services in a personalized manner that facilitates a large array of functionality even while driving, with other passengers also benefiting from an enhanced experience. Such intelligent applications however depend on a solid basis to be effective: Personalization, adaptive HMI, situation-aware intelligent systems -- either of these require semantic knowledge about the user, the vehicle, the current driving situation. Advanced functions coexist with sensors, other functions, and even other vehicles. In such an environment, collaboration can be highly beneficial. Obtaining a common understanding of knowledge and providing a platform to exchange it is essential in order to reach the next level of intelligent in-car systems. This work describes the Automotive Ontology, which is located at the core of such an open platform. We give an overview of design areas relevant to automotive applications, as well as meta aspects that facilitate inference and reasoning.