Agent-Based Driver Abnormality Estimation
For enhancing current driver assistance and information systems with regard to the capability to recognize an individual driver’s needs, we conceive a system based on fuzzy logic and a multi-agent-framework. We investigate how it is possible to gain useful information about the driver from typical vehicle data and apply the knowledge on our system. In a pre-stage, the system learns the driver’s regular steering manner with the help of fuzzy inference models. By comparing his regular and current manner, the system recognizes whether the driver is possibly impaired and betakes in a risky situation. Furthermore, the steering behavior and traffical situation are continuously observed for similar pattern. According to the obtained information, the system tries to conform its assistance functionalities to the driver’s needs.
KeywordsFuzzy Inference System Test Person Steering Behavior Steer Wheel Angle Headway Distance
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- 2.Bellifemine, F., Caire, G., Trucco, T., Rimassa, G.: JADE programmer’s guide (June 2007)Google Scholar
- 3.Boer, E., Rakauskas, M., Ward, N., Goodrich, M.: Steering entropy revisited. In: Proceedings of the 3rd International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp. 25–32 (2005)Google Scholar
- 5.Dragutinovic, N., Twisk, D.: Use of mobile phones while driving - effects on road safety. Technical Report SWOV report R-2005-12, SWOV Institute for Road Safety Research, The Netherlands (2006)Google Scholar
- 6.Forsman, A., Nilsson, L., Törnos, J., Östlund, J.: Effects of cognitive and visual load in real and simulated driving. Technical Report VTI report 533A, VTI Swedish National Road and Transport Research Institute (2006)Google Scholar
- 8.Kamal, M., Kawabe, T., Murata, J., Mukai, M.: Driver-adaptive assist system for avoiding abnormality in driving. IEEE Transactions on Control Applications, 1247–1252 (2007)Google Scholar
- 10.Macdonald, W., Hoffmann, E.: Review of relationships between steering wheel reversal rate and driving task demand. Human Factors 22, 733–739 (1980)Google Scholar
- 11.Nakayama, O., Futami, T., Nakamura, T., Boer, E.: Development of a steering entropy method for evaluating driver workload. Society of Automotive Engineers Technical Paper Series: 1999-01-0892 (1999)Google Scholar
- 12.Poitschke, T., Ablassmeier, M., Reifinger, S., Rigoll, G.: Multifunctional VR-Simulator Platform for the Evaluation of Automotive User Interfaces. In: Proceedings of 12th International Conference on Human-Computer Interaction HCI Interantional 2007, Beijing, P.R. China (2007)Google Scholar
- 13.Santana-Diaz, A., Hernandez-Gress, N., Esteve, D., Jammes, B.: Discriminating sensors for driver’s impairment detection. In: 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine & Biology (2000)Google Scholar
- 15.Verwey, W., Veltman, J.: Detecting short periods of elevated workload: A comparison of nine workload assessment techniques. Journal of experimental psychology: Applied 2, 270–285 (1996)Google Scholar
- 16.Zylstra, B., Tsimhoni, O., Green, P., Mayer, K.: Driving performance for dialing, radio tuning, and destination entry while driving straight roads. Technical Report Technical Report UMTRI-2003-35. The University of Michigan Transportation Research Institute, Ann Arbor, MI (2003)Google Scholar