Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles

Abstract

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed.

Smart Mobility Ontology: Current Trends and Future Directions

“…Regarding the scope and granularity of ontology, transportation ontologies have been generated at various levels. For example, some ontologies have been developed for specific functionality of Autonomous and Connected Vehicles (ACVs) (Viktorović et al, 2020), including the functionality of sensors in autonomous vehicles, and can be considered as application ontologies. Some other works define the core ontologies related to a specific mobility domain.…”

Recent Trends on IoT Systems for Traffic Monitoring and for Autonomous and Connected Vehicles

“…The special issue is characterized by 11 original research papers [1][2][3][4][5][6][7][8][9][10][11]. The first paper [1], "Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction", is written by M. V. Peppa, T. Komar, Wen Xiao, P. James, C. Robson, Jin Xing, and S. Barr from the University of Newcastle, UK; and the University of Melbourne, Australia.…”

“…The special issue is characterized by 11 original research papers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

“…Paper [ 10 ], titled, “Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles”, is authored by M. Viktorovic, D. Yang, B. De Vriees, from the Technical University of Eindhoven, the Netherlands.…”

“…More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, paper [ 10 ] proposed developing a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, such as connected vehicles. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented.…”