Act Normal – Driving Behavior Model Identification
|Title||Act Normal – Driving Behavior Model Identification|
|Summary||Creation of driving behavior model based on relatively sparse CAM message information sent (via 3/4G communication) to and fused in a common server DB.|
|Supervisor||Tony Larsson (HH) F308, Stefan Byttner (HH) E505 and Cristofer Englund (Viktoria Swedish ICT)|
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.
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?
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.
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Time frame: January 15 – June 1.
Prerequisites: Control Theory, Java and MatLab.
Keywords: Driver Assistance, Automated Driving, Statistical Modeling.