|Summary||Driving Behavior Model Identification|
|Supervisor||Tony Larsson, Stefan Byttner, Cristofer Englund|
Vehicle driving can be assisted to make the driving more cooperative, safer and energy efficient. A hypothesis is that if we “act normal” we can drive more safely and energy efficient and thus if a driver deviates too much from this norm the driver should be informed. To distinguish abnormal driver behaviors from the more normal a model of driving and traffic behavior with acceptable deviations is needed.
Research Questions: 1) How combine and process context awareness messages in a server sent from vehicles via the 3/4G radio communication network? 2) How create a space-time mapping of the driving behavior? 3) What message information and periodicity is needed to sufficiently map the “normal” behavior and deviation intervals along a road?
Expected Results: 1) A system architecture level description. 2) A smartphone client “app” that periodically sends CAM messages to a server via the 3/4G network. 3) A server “app” that creates a norm model for each road segment at different time intervals, i.e. a model logged in a space-time map. 4) A smartphone client “app” that compares a driver’s behavior to the norm for a specific road-time segment acquired from the server and gives a warning message if a dangerous deviation is detected. 5) The system consisting of the above “apps” tested in a limited scenario like a few blocks in a city or a few km of a rural 2-lane road with a few crossings. 6) An analysis of the relations between sampling accuracy, periodicity and detection sensitivity.