TRAFFIC SIMULATION FOR AUTONOMOUS VEHICLES

Human controlled transport (cars and bikes) and autonomous vehicles will have to co-exist for a long time, but it is interesting to explore the consequences for the road system of a fully or largely autonomous vehicle population.  For example, how much road space is required and what sort of external coordination mechanisms (traffic lights, roundabouts, pedestrian crossing controls, one-way streets) are necessary, if any, and what are the implications for road maintenance and car parks (we’ll need some as holding pens for vehicles not in use – or is on street parking, for proximity to point of use, preferable?)… amongst other things.  How many vehicles are needed for a given density of population, a particular road network and a specified quality of service?  The simulation should build on the SUMO framework, augmented with intelligent agent drivers (probably implemented using the Jason platform).

SEMANTIC QUERYING OF LEGACY DATA

The ENLITEN project on household energy behaviours and energy consumption reduction has been collecting a range of data (~10 sensors/household) every 5 minutes from ~70 houses in Exeter since September 2014.  These data are stored in a mysql database on the BUCS servers.  ENLITEN, like many other projects, needs to preserve its data for other researchers to access after the project has finished and after all the researchers employed on the project have left.  SQL table descriptions provide a form of documentation, but the semantics of the data are only present in human ascriptions to the names of the fields.  Furthermore, the SQL structure imposes requirements on any potential user to import the data into a compatible SQL database platform.  The aim of this project is to explore how to lift the data to a semantically annotated form by the creation of a semantic layer of open-linked data (OLD) and to develop the tools to migrate SQL/CSV data automatically to semantic OLD along with accompanying semantic metadata, while preserving the underlying data representations.  A benchmark outcome would be the facility to demonstrate SPARQL queries over a semantically annotated interface to samples of ENLITEN data.   The project will be partially supervised in conjunction with Sukumar Natarajan (Architecture) and Cathy Pink (University’s Research Data Scientist).