Motivation for the use of statistical machine learning techniques in the automotive domain arises from our development of context aware intelligent driver assistance systems, specifically, Driver Workload Management systems. Such systems integrate, prioritize, and manage information from the roadway, vehicle, cockpit, driver, infotainment devices, and then deliver it through a multimodal user interface. This could include incoming cell phone calls, email, navigation information, fuel level, and oil pressure to name a very few. In essence, the workload manager attempts to get the right information to the driver at the right time and in the right way in order that driver performance is optimized and distraction is minimized.
In this chapter we describe three major efforts that have employed our machine learning approach. First, we discuss how we have utilized our machine learning approach to detect and classify a wide range of driving maneuvers, and describe a semi-automatic data annotation tool we have created to support our modeling effort. Second, we perform a large scale automotive sensor selection study towards intelligent driver assistance systems. Finally, we turn our attention to creating a system that detects driver inattention by using sensors that are available in the current vehicle fleet (including forwarding looking radar and video-based lane departure system) instead of head and eye tracking systems.
This approach resulted in the creation of two generations of our workload manager system called Driver Advocate, Driver Advocate that was based on data rather than just expert opinions. The described techniques helped reduce the research cycle times while resulting in broader insight. There was rigorous quantification of theoretical sensor subsystem performance limits and optimal subsystem choices given economic price points. The resulting system performance specs and architecture design created a workload manager that had a positive impact on driver performance [23, 33].
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
E.R. Boer. Behavioral entropy as an index of workload. In Proceedings of the IEA/HFES 2000 Congress, pp. 125–128, 2000.
E.R. Boer. Behavioral entropy as a measure of driving performance. In Driver Assessment, pp. 225–229, 2001.
O. Bousquet and A. Elisseeff. Algorithmic stability and generalization performance. In Proceedings of NIPS, pp. 196–202, 2000.
L. Breiman. Bagging predictors. Machine Learning, 24(2):123–140, 1996.
L. Breiman. Random forests. Machine Learning, 45(1):5–32, 2001.
L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees, CRC Press, Boca Raton, FL, 1984.
D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201–221, 1994.
T.A. Dingus, S.G. Klauer, V.L. Neale, A. Petersen, S.E. Lee, J. Sudweeks, M.A. Perez, J. Hankey, D. Ramsey, S. Gupta, C. Bucher, Z.R. Doerzaph, J. Jermeland, and R.R. Knipling. The 100 car naturalistic driving study: Results of the 100-car field experiment performed by Virginia tech transportation institute. Report DOT HS 810 593, National Highway Traffic Safety Administration, Washington DC, April 2006.
L. Fletcher, N. Apostoloff, L. Petersson, and A. Zelinsky. Driver assistance systems based on vision in and out of vehicles. In Proceedings of IEEE Intelligent Vehicles Symposium, pp. 322–327. IEEE, Piscataway, NJ, June 2003.
J. Forbes, T. Huang, K. Kanazawa, and S. Russell. The BATmobile: Towards a Bayesian automated taxi. In Proceedings of Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.
I. Guyon and A. Elisseeff. An introduction to feature selection. Journal of Machine Learning Research, 3:1157–1182, 2003.
I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh. Feature Extraction, Foundations and Applications. Springer, Berlin Heidelberg New York, 2006.
J. Hankey, T.A. Dingus, R.J. Hanowski, W.W. Wierwille, C.A. Monk, and M.J. Moyer. The development of a design evaluation tool and model of attention demand. Report 5/18/00, National Highway Traffic Safety Administration, Washington DC, May 18, 2000.
R.A. Hess and A. Modjtahedzadeh. A preview control model of driver steering behavior. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 504–509, November 1989.
R.A. Hess and A. Modjtahedzadeh. A control theoretic model of driver steering behavior. IEEE Control Systems Magazine, 10(5):3–8, 1990.
H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer, Boston, MA, 1998.
J.C. McCall and M.M. Trivedi. Driver behavior and situation aware brake assistance for intelligent vehicles. Proceedings of the IEEE, 95(2):374–387, 2007.
V.L. Neale, S.G. Klauer, R.R. Knipling, T.A. Dingus, G.T. Holbrook, and A. Petersen. The 100 car naturalistic driving study: Phase 1-experimental design. Interim Report DOT HS 809 536, Department of Transportation, Washington DC, November 2002. Contract No: DTNH22-00-C-07007 by Virginia Tech Transportation Institute.
A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14: Proceedings of the NIPS 2001, 2001.
N. Oza. Probabilistic models of driver behavior. In Proceedings of Spatial Cognition Conference, Berkeley, CA, 1999.
F.J. Pompei, T. Sharon, S.J. Buckley, and J. Kemp. An automobile-integrated system for assessing and reacting to driver cognitive load. In Proceedings of Convergence 2002, pp. 411–416. IEEE SAE, New York, 2002.
L.R. Rabiner. A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286, 1989.
D. Remboski, J. Gardner, D. Wheatley, J. Hurwitz, T. MacTavish, and R.M. Gardner. Driver performance improvement through the driver advocate: A research initiative toward automotive safety. In Proceedings of the 2000 International Congress on Transportation Electronics, SAE P-360, pp. 509–518, 2000.
B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
C. Schreiner, K. Torkkola, M. Gardner, and K. Zhang. Using machine learning techniques to reduce data annotation time. In Proceedings of the 50th Annual Meeting of the Human Factors and Ergonomics Society, San Francisco, CA, October 16–20, 2006.
P. Smith, M. Shah, and N. da Vitoria Lobo. Adetermining driver visual attention with one camera. IEEE Transactions on Intelligent Transportation Systems, 4(4):205, 2003.
K. Torkkola. Automatic alignment of speech with phonetic transcriptions in real time. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP88), pp. 611–614, New York City, USA, April 11–14, 1988.
K. Torkkola, M. Gardner, C. Schreiner, K. Zhang, B. Leivian, and J. Summers. Sensor selection for driving state recognition. In Proceedings of the World Congress on Computational Intelligence (WCCI), IJCNN, pp. 9484–9489, Vancouver, Canada, June 16–21, 2006.
K. Torkkola, N. Massey, B. Leivian, C. Wood, J. Summers, and S. Kundalkar. Classification of critical driving events. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA), pp. 81–85, Los Angeles, CA, USA, June 23–24, 2003.
K. Torkkola, N. Massey, and C. Wood. Driver inattention detection through intelligent analysis of readily available sensors. In Proceedings of the 7th Annual IEEE Conference on Intelligent Transportation Systems (ITSC 2004), pp. 326–331, Washington DC, USA, October 3–6, 2004.
K. Torkkola, S. Venkatesan, and H. Liu. Sensor sequence modeling for driving. In Proceedings of the 18th International FLAIRS Conference, AAAI Press, Clearwater Beach, FL, USA, May 15–17, 2005.
W. Van Winsum, M. Martens, and L. Herland. The effects of speech versus tactile driver support messages on workload, driver behaviour and user acceptance. TNO-report TM-00-C003, TNO, Soesterberg, The Netherlands, 1999.
C. Wood, B. Leivian, N. Massey, J. Bieker, and J. Summers. Driver advocate tool. In Driver Assessment, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Torkkola, K. et al. (2008). Understanding Driving Activity Using Ensemble Methods. In: Prokhorov, D. (eds) Computational Intelligence in Automotive Applications. Studies in Computational Intelligence, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79257-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-79257-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79256-7
Online ISBN: 978-3-540-79257-4
eBook Packages: EngineeringEngineering (R0)