Research on the knowledge-based approach for “drivers/controllers” of autonomic systems and self-driving cars
“Description of traffic situations and analysis of the relevant characteristics of the situations.”
Description of the research topic:
The comprehension of a traffic situation plays a major role in driving a vehicle. It transforms perceived raw information into interpretable information. This forms a basis for future projection, decision making and action performing, such as navigating, maneuvering and driving control, within the driving control loop.
The aim of this research is to provide a generic traffic situation description capable of supplying various ADAS (Autonomic Driver Assistance System) with relevant information about the current driving and traffic situation of the ego vehicle („our” car) and its environment. With this information ADAS should be able to perform reasonable functions and actions and approach visionary goals such as injury and accident free driving, substantial assistance in arbitrary situations up to even autonomous driving.
Knowledge-based Traffic Situation Description
Most complex traffic situations seem to be those at intersections. Their understanding is influenced by a variety of object and relation types such as intersecting roads with lanes and markings, allowed and forbidden paths, vehicles coming from different directions and different kinds of road signs (see figure on the top left). Ontologies are well suited for modeling these multi-object traffic situations and for performing logic reasoning to check consistency of its knowledge and to reason about object types, relations and to e.g. apply traffic rules. Description logic (DL) is a language for building such ontologies.
Description Logic Based Traffic Situation Description for ADAS
This research is widely concerned with the investigation and validation of applicability and capability of ontology based traffic situation description.
The interpretation and use of the complex networks originated out of the analyses of Big Data (traffic data) should exploit the ontological approach as a model-driven environment for understanding.
Thesis supervisor: Bálint Molnár
How to Apply?
If you are interested apply here: [PhD] Doctoral School of Informatics – Eötvös Loránd University (elte.hu)
For more information visite the following website: Doctoral School of Informatics (elte.hu)
Funded: Not Funded
Master Degree: Required
Duration: 4 Years
Full/Part Time: Full Time
Starting Date: 06 September 2021
Deadline to Apply: 31 May 2021
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