Controlling a Signal-regulated Pedestrian Crossing using Case-based Reasoning
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The traffic domain, and in particular the domain of traffic control, is a highly complex and uncertain domain. A large network of roads, signal controlling systems, vehicles, pedestrians and other traffic units makes the domain intractable. There are great amounts of data available from different parts of traffic, thus there is a need for a method that can take advantage of this data in a systematical manner.In this thesis, we present a prototype Case-based Reasoning (CBR) system which purpose is to execute traffic at a signal controlled pedestrian crossing. The system uses pedestrian- and vehicle data to take decisions in real-time. The system is created as an OSGI bundle and uses the CVIS (Cooperative Vehicle-Infrastructure System) framework to enable communication with other traffic systems and traffic units. myCBR is used as a framework for making the process of retrieving and reusing cases easier. Experts from Norwegian Public Roads Administration were an important resource in defining the structure of the cases and for filling the case base with useful cases. Pedestrian data is obtained by using a Kinect sensor, and the Intention-based Sliding Doors system created by Solem, a previous MSc at our group, is integrated for interpreting the intention of pedestrians at the crossing. Vehicle data is obtained by using simulation software called SCANeR Studio.The results of the project showed that the CBR system adapted to the current traffic situation, and that correct cases were retrieved. These tests were performed in a limited test environment, and to evaluate the system properly, tests in a real environment is necessary.