Abstract
Risky driver behaviours such as sudden braking, swerving, and excessive acceleration are a major risk to road safety. In this study, we present a learning method to recognize such behaviours from smartphone sensor input which can be considered as a type of multi-channel time series. Unlike other learning methods, this Genetic Programming (GP) based method does not require pre-processing and manually designed features. Hence domain knowledge and manual coding can be significantly reduced by this approach. This method can achieve accurate real-time recognition of risky driver behaviours on raw input and can outperform classic learning methods operating on features. In addition this GP-based method is general and suitable for detecting multiple types of driver behaviours.
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References
National road safety strategy 2011-2020. Australian Transport Council (May 2011)
A.D. of Infrastructure and Transport, “Road deaths australia 2011 statistical summary” (May 2012), http://www.bitre.gov.au/publications/2012/files/RDA_Summary_2011.pdf
Shahabi, C., Yan, D.: Real-time pattern isolation and recognition over immersive sensor data streams. In: Proceedings of the 9th International Conference on Multi-Media Modeling, pp. 93–113 (2003)
Guralnik, V., Srivastava, J.: Event detection from time series data. In: Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data mining, KDD 1999, pp. 33–42. ACM, New York (1999)
Wahlstrom, E., Masoud, O., Papanikolopoulos, N.: Vision-based methods for driver monitoring. In: 2003 IEEE Proceedingsof Intelligent Transportation Systems., vol. 2, pp. 903–908. IEEE (2003)
Oliver, N., Pentland, A.P.: Graphical models for driver behavior recognition in a smartcar. In: Proceedings of the IEEE Intelligent Vehicles Symposium, IV 2000, pp. 7–12. IEEE (2000)
Horovitz, O., Krishnaswamy, S., Gaber, M.M.: A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety. Intelligent Data Analysis 11(1), 89–108 (2007)
Lu, H., Yang, J., Liu, Z., Lane, N., Choudhury, T., Campbell, A.: The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp. 71–84. ACM (2010)
Dai, J., Teng, J., Bai, X., Shen, Z., Xuan, D.: Mobile phone based drunk driving detection. In: 2010 4th International Conference on-NO PERMISSIONS Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 1–8. IEEE (2010)
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd. (2008)
Wagner, N., Michalewicz, Z.: An analysis of adaptive windowing for time series forecasting in dynamic environments: further tests of the dyfor gp model. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1657–1664. ACM, New York (2008)
Kaboudan, M.: Spatiotemporal forecasting of housing prices by use of genetic programming. In: The 16th Annual Meeting of the Association of Global Business (2004)
Hetland, M.L., Sætrom, P.: Temporal rule discovery using genetic programming and specialized hardware. Applications and Science in Soft Computing 24, 87 (2004)
Song, A., Pinto, B.: Study of gp representations for motion detection with unstable background. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)
Xie, F., Song, A., Ciesielski, V.: Event detection in time series by genetic programming. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (June 2012)
Ling, C., Huang, J., Zhang, H.: Auc: a better measure than accuracy in comparing learning algorithms. In: Advances in Artificial Intelligence, pp. 991–991 (2003)
Liu, C., Berry, P.M., Dawson, T.P., Pearson, R.G.: Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28(3), 385–393 (2005)
Quinlan, J.R.: C4. 5: programs for machine learning, vol. 1. Morgan Kaufmann (1993)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publishers Inc. (1995)
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems 10, NIPS 1997, pp. 507–513. MIT Press, Cambridge (1998)
Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148–156 (1996)
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Xie, F., Song, A., Salim, F., Bouguettaya, A., Sellis, T., Bradbrook, D. (2013). Learning Risky Driver Behaviours from Multi-Channel Data Streams Using Genetic Programming. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_22
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DOI: https://doi.org/10.1007/978-3-319-03680-9_22
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