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7A_3_An ontological modelling of communications for an intelligent transport environment
 

7A_3_An ontological modelling of communications for an intelligent transport environment

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Session 7A, Paper 3

Session 7A, Paper 3

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  • I’ve divided my presentation into 5 parts. Firstly, I am going to talk about introduction And then, I’ll explain about ontological approach, Finally, I’ll sum up and conclude. If you have any questions, I’ll be glad to answer them at the end of my presentation.
  • ITS are comprised of advanced communication technologies based on information and electronics integrated into infrastructure and vehicles on a road network. ITS application can support transport services to have Improved safety and reduce vehicle accidents, transport times, and fuel consumption According to the national ITS architecture of the united states, there are four main classes, which are travelers, traffic centers, vehicles, and field. Field means road and road facilities. For communications among them, there are several communication technologies, like using mobile, cable, or something like that. This architecture includes some new communication technologies for vehicle to vehicle communications and dedicated short range communications. DSRC may over short range vehicle to vehicle communications as well. U. S. Department of Transportation call it Vehicle Infrastructure Integration (VII). With Dedicated Short Range Communications (DSRC), vehicle and infrastructure communicate with each other in real-time so that it may improve safety and efficiency on road networks. Vehicle-to-vehicle, vehicle-to-infrastructure communications ad hoc communications in the 5.9 GHz band chosen after the spectral and propagation characteristics low latency, high data transfer rate, high mobility communications Intelligence is the capacity to understand semantics, to reason, to make decisions, and to solve problems using knowledge and experience (WordNet, 2006). Intelligent vehicles and infrastructure intelligent agents are flexible and autonomous computational entities that can communicate with other agents in a dynamic and unpredictable environment (Luck et al., 2005) the capacity to understand semantics, to reason, to make decisions, and to solve problems using knowledge and experience (WordNet, 2006). COOPERATIVE COLLISION WARNING Emergency warning system for vehicles Cooperative Forward Collision Warning Intersection collision avoidance Approaching emergency vehicle warning (Blue Waves) Vehicle safety inspection Transit or emergency vehicle signal priority Electronic parking payments Commercial vehicle clearance and safety inspections In-vehicle signing Rollover warning Probe data collection Highway-rail intersection warning Electronic toll collection
  • Intelligence the capacity to understand semantics, to reason, to make decisions, and to solve problems using knowledge and experience (WordNet, 2006). Calculation. Charles Babbage's Analytical Engine, 1871. This was the first fully-automatic calculating machine. Information processing. With widespread digital storage and manipulation of non-numerical information from the 1960s onwards. With the growth of the Internet and the World Wide Web over the last fifteen years, we have reached a position where a new metaphor for computation is required: computation as interaction. In this metaphor, computing is something that happens by and through communication between computational entities. intelligent agents are flexible and autonomous computational entities that can communicate with other agents in a dynamic and unpredictable environment (Luck et al., 2005) Computing is an activity that is inherently social rather than solitary Applications are no longer monolithic, functioning on one machine for single user applications, or distributed applications managed by a single organisation (such as today’s Intranet applications), but instead are societies of components. The components are providing services to one another. The components are not necessarily activated by human users Components may enter and leave different societies at different times and for different reasons Computing is an social activity The emerging model of software as a service Applications are societies of components The components form coalitions with one another to achieve particular temporary objectives are not necessarily activated by human users may carry out actions in an automated and coordinated manner when certain conditions hold Agents autonomous and intelligent software entities autonomous and Intelligent vehicles and infrastructure
  • Studies of suitable semantic contents over these communication channels are still limited. Communication between Agents Knowledge sharing and exchange is particularly important in multi-agent systems (MAS). An agent is usually described as a persistent entity with some degree of independence or autonomy that carries out some set of operations depending on what he perceives. An agent usually contains some level of intelligence, so it has to have some knowledge about its goals and desires. The whole multi-agent system is created to be capable of reaching goals that are difficult to achieve by an individual agent or a monolithic system. In multi- agent systems, an agent usually cooperates with other agents, so it should have some social and communicative abilities. In order to communicate, agents must be able to: deliver and receive messages - at this physical level, agents must communicate over agreed physical and network layers to be able to deliver and receive strings or objects that represent messages parse the messages - at the syntactic level, agents must be able to parse messages to correctly decode the message to its parts, such as message content, language, sender, and also must be able to parse the content of the message understand the messages - at the semantic level, the parsed symbols must be understood in the same way, i.e., the ontology describing the symbols must be shared or explicitly expressed and accessible to be able to decode the information contained in the message For multi-agent systems the first physical level as well as the second syntactic level is well standardized by the Foundation for Intelligent Physical Agents (FIPA), for example by agent management specification and agent communication language specification. As for the third level, semantics, standard exists that describe thecontent languages and that describe usage of ontologies. Several objectives were derived to achieve the research aim like follows. Build an ontology to support several contexts in an ITS environment Represent dynamics and interactions of objects on a road network in an ontology Extract decision-supporting information for vehicles and road facilities using context reasoning from an ontology Implement ontology application which supports vehicles, road features, and their interactions in an ITS setting
  • Web Ontology Language (OWL) built on top of Resource Description Framework (RDF) and RDF Schema Support extended vocabulary for cardinality, richer property characteristics SPARQL Protocol and RDF Query Language (SPARQL) a recursive acronym a query language for graph-based data that is stored or viewed as RDF SPARQL Inferencing Notation (SPIN) SPARQL-based expression inferencing and constaint checking
  • In a single framework VE hicle and IN frastructure road elements, intersections, roundabouts, and vehicles classes, properties, axioms, and instances hierarchical relations and semantic relations I found a definition of ITS from a white paper from Great Britain Depart for Transport. It says ITS is combinations of {computers, databases, maps, sensors} for vehicles and infrastructure From this definition, I can see some convergence among Transport System, GIS, Pervasive Computing. Geosensor network can be an good example of the convergence. So we will look at ontology itself and ontology in GIS, pervasive computing, and transport system.
  • I found a definition of ITS from a white paper from Great Britain Depart for Transport. It says ITS is combinations of {computers, databases, maps, sensors} for vehicles and infrastructure From this definition, I can see some convergence among Transport System, GIS, Pervasive Computing. Geosensor network can be an good example of the convergence. So we will look at ontology itself and ontology in GIS, pervasive computing, and transport system.
  • I found a definition of ITS from a white paper from Great Britain Depart for Transport. It says ITS is combinations of {computers, databases, maps, sensors} for vehicles and infrastructure From this definition, I can see some convergence among Transport System, GIS, Pervasive Computing. Geosensor network can be an good example of the convergence. So we will look at ontology itself and ontology in GIS, pervasive computing, and transport system.
  • That brings me end of presentation. To sum up, For future work, In conclusion, did it manually, but in the future, I’m going to turn it to automatic code based on an simulation environment
  • Thank you for listening. Any questions? I’m afraid I don’t understand what you mean. Could you please rephrase that? Are you saying that …. ?

7A_3_An ontological modelling of communications for an intelligent transport environment 7A_3_An ontological modelling of communications for an intelligent transport environment Presentation Transcript

  • An Ontological modelling of communications for an intelligent transport environment Seong Kyu Choi UCL Civil, Environmental and Geomatic Engineering
  • Outline
    • Introduction
    • Ontology in transport domain
    • An ambulance scenario
    • VEIN ontology
    • Inferencing and querying
    • Conclusion and future work
    An Ontological modelling of communications for an intelligent transport environment
  • Intelligent Transport Systems
    • Road transport
      • towards Intelligent Transport Systems (ITS)
      • advanced information, communication, and sensing technologies
      • improve safety, mobility, efficiency, and productivity
    • Dedicated Short Range Communications (DSRC)
      • V2V communications & V2I communications
    Introduction * http://www.geek.com/wp-content/uploads/2009/02/dsrcrsu.jpg * http://www.comnets.rwth-aachen.de/uploads/ RTEmagicC_car2car_consultium.jpg.jpg * Dulmage et al, 2006 OBU OBU * ADT, 2007 5.86 - 5.92GHz Operating Band 10MHz Nominal Bandwidth >  60 mph Mobility < 1km Range < 50ms Latency 3-27Mbps Data transfer rate
  • Agent-based
    • Intelligent Transport Systems
    • Agent technology
      • Independent and Intelligent vehicles and infrastructure
        • flexible and autonomous computational entities that can communicate with each other in a dynamic and unpredictable transport environment
    Introduction Vehicles and infrastructure coalition (society)
    • are providing services to one another
    • are not necessarily activated by human users
    • may carry out actions
    • in an automated and coordinated manner
    • when certain conditions hold
    • The vehicles and infrastructure themselves
    • form coalitions with one another
    • to achieve particular temporary objectives
  • Research Objectives
    • Suitable semantic contents over these communications
      • for V2V and V2I communications based on DSRC
      • share situation-specific information and knowledge
      • resolve local situations in an ITS setting
    Introduction physical level deliver and receive messages DSRC syntactic level parse the messages semantic level understand the messages Gap Ontology language Domain ontology
    • Communications among intelligent vehicles and infrastructure
  • What is an ontology?
    • A formal representation of a set of concepts within a domain and the relationships between those concepts.
      • provides a shared vocabulary to model a domain
      • classes, properties, axioms, instances
      • triples (subject-predicate-object)
        • Wine hasMaker Winery
        • Red rdf:type WineColor
    Why ontology?
    • It is used to reason about the properties of that domain, and may be used to describe the domain.
    • Ontologies are used in artificial intelligence , the Semantic Web, systems engineering, software engineering, etc.
    Introduction
  • Ontologies in Transport domain
    • Lorenz et al., 2005
      • Ontology of Transport Networks (OTN)
      • As an encoding of Graphic Data Format (GDF)
      • Mostly designed for describing road infrastructure
    • Hornsby and King, 2008
      • Four kinds of motion relations
        • isBehind, inFrontOf, driveBeside, passBy
      • Different semantics from difference combinations
        • isBehind relations between target vehicles and reference vehicles
      • Mostly designed for vehicles
    • Need a single framework
      • Describing spatiotemporal relations among vehicles and infrastructure
        • may assist their communications
        • r esolving specific situations
    Ontology in transport domain
  • Comunication electronically extending the driver’s visibility An ambulance scenario Vehicles can act as information sources, information relays , and recipients of information APPROACHING EMERGENCY VEHICLE WARNING EMERGENCY VEHICLE SIGNAL PREEMPTION ROAD CONDITION WARNING LOW BRIDGE WARNING WORK ZONE WARNING IMMINENT COLLISION WARNING CURVE SPEED ASSISTANCE STOP LIGHT ASSISTANT (2) INTERSECTION COLLISION WARNING HIGHWAY/RAIL COLLISION AVOIDANCE COOPERATIVE COLLISION WARNING [V-V] GREEN LIGHT - OPTIMAL SPEED ADVISORY COOPERATIVE VEHICLE SYSTEM – PLATOONING COOPERATIVE ADAPTIVE CRUISE CONTROL VEHICLE BASED PROBE DATA COLLECTION INFRASTRUCTURE BASED PROBE DATA COLLECTION INFRASTRUCTURE BASED TRAFFIC MANAGEMENT TOLL COLLECTION TRAFFIC INFORMATION TRANSIT VEHICLE DATA TRANSFER TRANSIT VEHICLE SIGNAL PRIORITY EMERGENCY VEHICLE VIDEO RELAY MAINLINE SCREENING BORDER CLEARANCE ON-BOARD SAFETY DATA TRANSFER
  • Snapshots of two vehicles’ movement An ambulance scenario
  • Classes for VEIN ontology VEIN ontology
  • Object properties for spatiotemporal relations among vehicles and infrastructure VEIN ontology Domain Object property Range VEhicle SpatialRelations:isLocatedBehind VEhicle VEhicle SpatialRelations:isLocatedOn Road_Element Vehicle Vein:nextRoadElementOfTheRoute Road_Element Road_Element NetworkRelations:isConnectedTo Road_Element Traffic_Light_Controller Vein:controls Traffic_Light Traffic_Light Vein:servers Road_Element
  • Instances and relations among them VEIN ontology
  • An inference rule and new triples for road elements’ connectivity Inferencing and querying An inference rule in the Road_Element class the coloured values are generated automatically A set of new triples generated based on the rule
  • SPARQL queries to support the scenario Inferencing and querying
  • SPARQL queries to support the scenario Inferencing and querying Result SPARQL query Purpose To find vehicles on r1 select ?car where { ?ambulance rdfs:label &quot;A1&quot;. ?ambulance SpatialRelations:isLocatedOn ?currentroad. ?car SpatialRelations:isLocatedOn ?currentroad. ?ambulance SpatialRelations:isLocatedBehind ?car. } V2 To find vehicles on r2 select ?car where { ?ambulance rdfs:label &quot;A1&quot;. ?ambulance SpatialRelations:isLocatedOn ?currentroad. ?ambulance :nextRoadElementOfTheRoute ?nextroad. ?currentroad NetworkRelations:isConnectedTo ?connectedroad. ?nextroad NetworkRelations:isConnectedTo ?connectedroad. ?car SpatialRelations:isLocatedOn ?connectedroad. } V1 , V3 To find vehicles on r3 select ?car where { ?ambulance rdfs:label &quot;A1&quot;. ?ambulance :nextRoadElementOfTheRoute ?nextroad. ?car SpatialRelations:isLocatedOn ?nextroad. } V4
  • Conclusion and future work
    • Describe traffic situations with an ontology model
      • in a single framework
      • support interactions among vehicles and infrastructure
      • using geoobjects and their spatiotemporal relations
      • extract new information from existing semantic relations
        • using inference rules and SPARQL queries
        • find initial communication targets based on SPARQL queries
      • work in progress
    • Future work
      • answer back from vehicles
      • an agent-based simulation
        • support automatic and dynamic agents
        • develop a mechanism to share and update the ontology
        • find the proper coverage and update interval of ontology queries
    Conclusion and future work
    • Thank you!
    • Any questions, comments?