Objective: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. Method: Using a “model tracing” methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model's simulated behavior with a driver's actual observed behavior and thus continually infers the driver's unobservable intentions from her or his observable actions. Results: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. Conclusion: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. Application: By providing robust real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.
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References
1.
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111, 1036-1060.
Anderson, J.R., Corbett, A.T., Koedinger, K., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4, 167-207.
4.
Barfield, W., & Dingus, T. (Eds.). (1997). Human factors in intelligent transportation systems. Mahwah, NJ: Erlbaum.
5.
Chovan, J.D., Tijerina, L., Alexander, G., & Hendricks, D.L. (1994). Examination of lane change crashes and potential IVHS countermeasures (Tech. Rep. No. DOT-HS-808-701/DOT-VNTSC-NHTSA-93-2) . Cambridge, MA: U.S. Department of Transportation, Volpe National Transportation Systems Center.
6.
Corbett,A. T., &Anderson, J.R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction , 4, 253-278.
7.
Donges, E. (1978). A two-level model of driver steering behavior . Human Factors, 20, 691-707.
8.
Frederiksen, J.R., & White, B.Y. (1990). Intelligent tutors as intelligent testers. In N. Frederiksen, R. Glaser, A. Lesgold, & M. G. Shafto (Eds.), Diagnostic monitoring of skill and knowledge acquisition (pp. 1-25). Hillsdale, NJ : Erlbaum.
9.
Hetrick, S. (1997). Examination of driver lane change behavior and the potential effectiveness of warning onset rules for lane change or "side" crash avoidance systems. Unpublished master's thesis, Virginia Polytechnic Institute and State University, Department of Industrial Systems and Engineering, Blacksburg, VA.
10.
Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse, K. (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (pp. 256-265). San Francisco, CA: Morgan Kaufmann.
11.
Jaeger, S., Manke, S., Reichert, J., & Waibel, A. (2001). Online handwriting recognition: The NPen++ recognizer . International Journal on Document Analysis and Recognition , 3, 169-180.
12.
Kuge, N., Yamamura, T., Shimoyama, O., & Liu, A. (2000). A driver behavior recognition method based on a drive model framework. In Proceedings of the Society of Automotive Engineers World Congress 2000 (pp. 469-476). New York: Society of Automotive Engineers.
13.
Land, M., & Horwood, J. (1995). Which parts of the road guide steering? Nature, 377, 339-340.
14.
Lee, S.E., Olsen, E.C.B., & Wierwille, W.W. (2004). A comprehensive examination of naturalistic lane changes (Tech. Rep. DOT-HS-809-702). Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration.
15.
Mandalia, H.M., & Salvucci, D.D. (2005). Using support vector machines for lane change detection. In Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting (pp. 1965-1969). Santa Monica, CA: Human Factors and Ergonomics Society .
16.
McCall, J., Wipf, D., Trivedi, M.M., & Rao, B. (in press). Lane change intent analysis using robust operators and sparse Bayesian learning. IEEE Transactions on Intelligent Transportation Systems.
17.
Michon, J. A. (Ed.) (1993). Generic intelligent driver support system. London: Taylor & Francis.
18.
Oliver, N., & Pentland, A.P. (2000). Graphical models for driver behavior recognition in a SmartCar. In Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (pp. 7-12). Piscataway, NJ: IEEE.
19.
Olsen, E.C.B. (2003). Modeling slow lead vehicle lane changing. Unpublished doctoral dissertation, Virginia Polytechnic Institute and State University, Department of Industrial and Systems Engineering, Blacksburg, VA. Retrieved January 17, 2007 from http://scholar.lib.vt.edu/theses/available/etd-12032003-152916/
20.
Pentland, A., & Liu, A. (1999). Modeling and prediction of human behavior. Neural Computation, 11, 229-242.
21.
Rabiner, L.R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE , 77, 257-286.
22.
Rubin, K.S., Jones, P.M., & Mitchell, C.M. (1988). OFMspert: Inference of operator intentions in supervisory control using a blackboard architecture . IEEE Transactions on Systems, Man, and Cybernetics, 18, 618-637.
23.
Salvucci, D.D. (2004). Inferring driver intent: A case study in lane-change detection. In Proceedings of the Human Factors Ergonomics Society 48th Annual Meeting (pp. 2228-2231). Santa Monica, CA: Human Factors and Ergonomics Society.
24.
Salvucci, D.D. (2006). Modeling driver behavior in a cognitive architecture . Human Factors, 48, 362-380.
25.
Salvucci, D.D., Boer, E.R., & Liu, A. (2001). Toward an integrated model of driver behavior in a cognitive architecture. Transportation Research Record , 1779, 9-16.
26.
Salvucci, D.D., & Gray, R. (2004). A two-point visual control model of steering. Perception, 33, 1233-1248.
27.
Salvucci, D.D., & Liu, A. (2002). The time course of a lane change: Driver control and eye-movement behavior. Transportation Research Part F , 5, 123-132.
28.
Salvucci, D.D., & Siedlecki, T. (2003). Toward a unified framework for tracking cognitive processes. In Proceedings of the 25th Annual Conference of the Cognitive Science Society (pp. 1023-1028). Mahwah, NJ : Erlbaum.
29.
Tappert, C.C., Suen, C.Y., & Wakahara, T. (1990). The state of the art in on-line handwriting recognition . IEEE Transactions on Pattern Analysis and Machine Intelligence , 12, 787-808.
30.
Tijerina, L. (1999). Operational and behavioral issues in the comprehensive evaluation of lane change crash avoidance systems. Journal of Transportation Human Factors, 1, 159-176.
31.
Tijerina, L., Garrott, W.R., Stoltzfus, D., & Parmer, E. (2005). Van and passenger car driver eye glance behavior during the lane change decision phase. In Proceedings of the 84th Annual Meeting of the Transportation Research Board [CD-ROM]. Washington, DC: Transportation Research Board.
32.
Waibel, A., & Lee., K. (Eds.) (1990). Readings in speech recognition . San Mateo, CA: Morgan Kaufmann .
