Act Normal – Driving Behavior Model Identification

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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)
Level Master
Status Open

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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.


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Schakel W.J., Van Arem, B., and Netten, B.D., "Effects of Cooperative Adaptive Cruise Control on traffic flow stability," 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), vol., no., pp.759-764, 19-22 Sept. 2010.

Kamal M. A S, Mukai M., Murata J., and Kawabe, T., "On board eco-driving system for varying road-traffic environments using model predictive control, "IEEE International Conference on Control Applications (CCA), pp.1636-1641, 8-10 Sept. 2010.

Benmimoun M., Pütz A., Zlocki A. and Eckstein L., "Effects of ACC and FCW on Speed, Fuel Consumption, and Driving Safety, "IEEE Vehicular Technology Conference (VTC Fall), pp.1-6, 3-6 Sept. 2012.

Time frame: January 15 – June 1.

Prerequisites: Control Theory, Java and MatLab.

Keywords: Driver Assistance, Automated Driving, Statistical Modeling.