Real-time driving danger-level prediction

Engineering Applications of Artificial Intelligence

Volumn 23, Issue 8, 2010, Pages 1247-1254

  Wang, Jinjun ^a   Xu, Wei ^a   Gong, Yihong ^a  

^a NEC LABORATORIES AMERICA   (United States)

Author keywords

Danger level prediction; Driving safety monitoring; Functional safety; Reinforcement learning; Sequential supervised learning

Indexed keywords

CONDITIONAL RANDOM FIELD; DANGER-LEVEL PREDICTION; DRIVER'S BEHAVIOR; DRIVING SAFETY; DRIVING STATE; FUNCTIONAL SAFETY; MULTIPLE SENSORS; NON-INTRUSIVE; PHYSIOLOGICAL DATA; PREDICTION ACCURACY; PREDICTION SYSTEMS; PROTOTYPE SYSTEM; SEQUENTIAL SUPERVISED LEARNING; STATISTICAL MODELING; TEMPORAL PATTERN; USER STUDY; VEHICLE DYNAMICS;

FORECASTING; HIDDEN MARKOV MODELS; PHYSIOLOGICAL MODELS; REINFORCEMENT LEARNING; SUPERVISED LEARNING;

LEARNING ALGORITHMS;

EID: 77958072470     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2010.01.001     Document Type: Article

References (26)

1. Anon, 1999. Percolse and eyetracking: challenge and opportunity. Technical Report, Applied Science Laboratories.
2. Awake consortium (ist 2000-28062), 2004 〈 http://www.awake-eu.org 〉.
3. R. Bellman Dynamic Programming 1957 Princeton University Press Princeton
4. L. Bergasa, J. Nuevo, M. Steloand, R. Barea, and M. Lopez Real-time system for monitoring driver vigilance IEEE Transactions on Intelligent Transportation System 1 2006 63 77
5. D. Bertsekas, and J. Tsitsiklis Neuro-dynamic Programming 1996 Athena Scientific
6. Boverie, S., Giralt, A., Quellec, J.M.L., Hirl, A., 1998. Intelligent systems for video monitoring of vehicle cockpit. In: International Congress and Exposition ITS. Advanced Controls and Vehicle Navigation Systems, pp. 15.
7. D.o.T. National Highway Traffic Safety Administration, 2000. Proposed driver workload metrics and methods project, Washington, DC, USA.
8. Harmon, M., Harmon, S., 2000. Reinforcement learning: a tutorial, Wright State University 〈 http://www-anw.cs.umass.edu/harmon/rltutorial/frames. html 〉.
9. Healey, J., Picard, R., 2000. Smatcar: detecting driver stress. In: Proceedings of ICPR'00, pp. 218221.
10. Healey, J., Picard, R., 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation System, 6, 156166.
11. Ji, Q., Yang, X., 2002. Real-time eye, gaze and face pose tracking for monitoring driver vigilance. Real-time Imaging 357377.
12. Q. Ji, Z. Zhu, and P. Lan Real-time nonintrusive monitoring and prediction of driver fatigue IEEE Transactions on Vehicular Technology 4 2004 1052 1068
13. Kircher, A., Uddman, M., Sandin, J., 2002. Vehicle control and drowsiness, Swedish National Road and Transport Research Institute.
14. Matsumoto, Y., Zelinsky, A., 2000. An algorithm for real-time stereo vision implementation of head pose and gaze direction measurements. In: Proceedings of IEEE International Conference on Face and Gesture Recognition (ICFGR)'00, pp. 499505.
15. Murphy, K., 〈 http://people.cs.ubc.ca/murphyk/software/hmm/hmm.html 〉.
16. Petrushin, V.A., 2000, Hidden Markov models: fundamentals and applications, In: Symposium for Electronics Engineer, Online.
17. Schmidt, M., Swersky, K., Murphy, K., 〈 http://people.cs.ubc.ca/ murphyk/software/crf/crf.html 〉.
18. Shih, S.-W., Wu, Y.-T., Liu, J., 2000. A calibration-free gaze tracking techniques. In: Proceedings of ICPR'00, pp. 201204.
19. Singliar, T., Hauskrecht, M., 2006. Towards a learning incident detection system. In: Proceedings of ICML'06 Workshop on Machine Learning for Surveillance and Event Detection.
20. Soria, J., 2002. Llega el poloto tecnolgico. Revista Trfico, Direccin General de Trfic. Ministerio de Interior, pp. 2021.
21. Ueno, H., Kaneda, M., Tsukino, M., 1994. Development of drowsiness detection system. In: Proceedings of the Vehicle Navigation and Information Systems Conference, pp. 1520.
22. Victor, T., Zelinsky, A., 2001. Automating the measurement of driver visual behaviours using passive stereo vision. In: Proceedings of International Conference Series on Vision in Vehicles (VIV)'01.
23. Wang, J., Gong, Y., 2008. Recognition of multiple drivers' emotional state. In: Proceedings of IEEE ICPR'08.
24. Wang, J., Gong, Y., 2009. Normalizing multi-subject variation for drivers' emotion recognition. In: Proceedings of ICME'09.
25. Wong, W., Moore, A.W., Cooper, G., Wagner, M., 2002. Rule-based anomaly pattern detection for detecting disease outbreaks. In: Proceedings of American Association of Artificial Intelligence Conference.
26. Zhou, Y., Xu, W., Ning, H., Gong, Y., Huang, T., 2007. Detecting unsafe driving patterns using discriminative learning. In: Proceedings of IEEE ICME'07, pp. 14311434.