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Created page with "{{StudentProjectTemplate |Title=Act Normal – Driving Behavior Model Identification |Summary=Creation of driving behavior model based on relatively sparse CAM message informa..."

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{{StudentProjectTemplate
|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.
|Author=
|Supervisor=Tony Larsson (HH) F308, Stefan Byttner (HH) E505 and Cristofer Englund (Viktoria Swedish ICT)
|Level=Master
|Status=Open
}}
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.

References:

Wilmink I.R., Klunder G.A., and Van Arem B., "Traffic flow effects of Integrated full-Range Speed Assistance (IRSA)," IEEE Intelligent Vehicles Symposium, vol., no., pp.1204-1210, 13-15 June 2007.

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.

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Slawek: Created page with "{{StudentProjectTemplate |Title=Act Normal – Driving Behavior Model Identification |Summary=Creation of driving behavior model based on relatively sparse CAM message informa..."