- 1.Mei Chen, Todd M. Jochem, and Dean A. Pomerleau. AURORA: A vision-based roadway departure warning system. In Proceedings of the IEEE Conference on Intelligent Robots and Systems, Pittsburgh, PA, August 1995. Google ScholarDigital Library
- 2.U. Franke, D. Gavrila, S. Gtrzig, F. Lindner, F. Paetzold, and C. Wthler. Autonomous driving goes downtown. IEEE Intelligent Systems, 13(6):40-48, September/October 1998. Google ScholarDigital Library
- 3.Yolanda Gil. Acquiring Domain Knowledge for Planning by Experimentation. Doctoral dissertation, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1992. Google ScholarDigital Library
- 4.Dorota A. Grejner-Brzezinska. Positioning accuracy of the GPSVan. In Proceedings of the 52nd Annual National Technical Meeting of the Institude of Navigation, pages 657-665, Palm Springs, CA, 1995.Google Scholar
- 5.Simon Handley, Pat Langley, and Folke Rauscher. Learning to predict the duration of an automobile trip. in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 219- 223, New York, 1998. AAAI Press.Google ScholarDigital Library
- 6.John A. Hartigan. Clustering Algorithms. Wiley series in probability and mathematical statistics. Wiley, New York, 1975. Google ScholarDigital Library
- 7.Pat Langley, George Drastal, R. Bharat Rao, and Russell Greiner. Theory revision in fault hierarchies. In Proceedings of the Fifth International Workshop on Principles of Diagnosis, pages 166-173, New Paltz, NY, 1994.Google Scholar
- 8.David E. Moriarty and Pat Langley. Learning cooperative lane selection strategies for highways. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 684-691, Madison, WI, 1998. Google ScholarDigital Library
- 9.Dirk Ourston and Raymond J. Mooney. Theory refinement combining analytical and empirical methods. Artificial Intelligence, 66:311-344, 1994. Google ScholarDigital Library
- 10.Douglas John Pearson. Learning Procedural Planning Knowledge in Complex Environments. Doctoral dissertation, University of Michigan, Ann Arbor, Mi, 1996.Google Scholar
- 11.Dean A. Pomerleau. RALPH: Rapidly adapting lateral position handler. In Proceedings of the IEEE Symposium on Intelligent Vehicles, Detroit, Michigan, September 1995.Google ScholarCross Ref
- 12.Christoper A. Pribe and Seth O. Rogers. Learning to associate driver behavior with traffic controls, in Proceedings of the 78th Annual Meeting of the Transportation Review Board, Washington, DC, January 1999.Google Scholar
- 13.Seth Teller. Automated urban model acquisition: Project rationale and status. In Proceedings of the Image Understanding Workshop, pages 455-462, Monterey, CA, 1998.Google Scholar
- 14.Xuemei Wang. Learning Planning Operators by Observation and Practice. Doctoral dissertation, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1996.Google Scholar
- 15.Shawn Weisenburger and Christopher Wilson. An integrated vehicle positioning system for safety applications. In Proceedings of the 56th Annual National Technical Meeting of the Institute of Navigation, San Diego, CA, 1999.Google Scholar
- 16.Christopher K. H. Wilson, Seth Rogers, and Shawn Weisenburger. The potential of precision maps in intelligent vehicles. In Proceedings of the IEEE International Conference on Intelligent Vehicles, pages 419-422, Stuttgart, Germany, October 1998.Google Scholar
- 17.W. Ziegler, U. Franke, R. Renner, and A. Ktihnle. Computer vision on the road: A lane departure and drowsy driver warning system. In 4th Mobility Technology Conference, SAE, S~o Paulo, Brazil, October 1995.Google ScholarCross Ref
Index Terms
- Mining GPS data to augment road models
Recommendations
Mining uncertain data
As an important data mining and knowledge discovery task, association rule mining searches for implicit, previously unknown, and potentially useful pieces of information—in the form of rules revealing associative relationships—that are embedded in the ...
Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results
Many association rule-mining algorithms have been proposed in the last few years. Their main drawback is the huge amount of generated patterns. In spatial association rule mining, besides the large amount of rules, many are well-known geographic domain ...
LAREDAM - considerations on system of local analytical reports from data mining
ISMIS'08: Proceedings of the 17th international conference on Foundations of intelligent systemsLAREDAM is a research project the goal of which is to study possibilities of automatic formulation of analytical reports from data mining. Each such report presents answer to one analytical question. Lot of interesting analytical questions can be ...
Comments