Abstract
This paper proposes an interpretable machine learning structure for the task of lane change intention prediction, based on multivariate time series data. A Mixture-of-Experts architecture is adapted to simultaneously predict lane change directions and the time-to-lane-change. To facilitate reproducibility, the approach is demonstrated on a publicly available dataset of German highway scenarios. Recurrent networks for time series classification using Gated Recurrent Units and Long-Short-Term Memory cells, as well as convolution neural networks serve as comparison references. The interpretability of the results is shown by extracting the rule sets of the underlying classification and regression trees, which are grown using data-adaptive interpretable features. The proposed method outperforms the reference methods in false alarm behavior while displaying a state-of-the-art early detection performance.
Funded by Audi AG.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7) (2015). https://doi.org/10.1371/journal.pone.0130140
Baldacchino, T., Cross, E.J., Worden, K., Rowson, J.: Variational Bayesian mixture of experts models and sensitivity analysis for nonlinear dynamical systems. Mech. Syst. Signal Process. 66-67, 178–200 (2016). https://doi.org/10.1016/j.ymssp.2015.05.009. http://www.sciencedirect.com/science/article/pii/S0888327015002307
Tang, B., Heywood, M.I., Shepherd, M.: Input partitioning to mixture of experts. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002 (Cat. No. 02CH37290), vol. 1, pp. 227–232 (2002). https://doi.org/10.1109/IJCNN.2002.1005474
Breiman, L.: Classification and regression trees. The Wadsworth statistics/probability series, Wadsworth International Group and Wadsworth & Brooks/Cole and Wadsworth & Brooks/Cole Advanced Books & Software, Belmont, Calif. and Pacific Grove, Calif. and Pacific Grove, Calif. and Monterey, Calif. (1984)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734. Association for Computational Linguistics, Stroudsburg (2014). https://doi.org/10.3115/v1/D14-1179
Dang, H.Q., Furnkranz, J., Biedermann, A., Hoepfl, M.: Time-to-lane-change prediction with deep learning. In: IEEE ITSC 2017, pp. 1–7. IEEE, Piscataway (2017). https://doi.org/10.1109/ITSC.2017.8317674
Ebrahimpour, R., Kabir, E., Esteky, H., Yousefi, M.R.: A mixture of multilayer perceptron experts network for modeling face/nonface recognition in cortical face processing regions. Intell. Autom. Soft Comput. 14(2), 151–162 (2008). https://doi.org/10.1080/10798587.2008.10642988
Gallitz, O., de Candido, O., Botsch, M., Utschick, W.: Interpretable feature generation using deep neural networks and its application to lane change detection. In: The 2019 IEEE Intelligent Transportation Systems Conference - ITSC, pp. 3405–3411. IEEE, Piscataway (2019). https://doi.org/10.1109/ITSC.2019.8917524
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 1–42 (2019). https://doi.org/10.1145/3236009
Gunning, D.: Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA), nd Web (2017)
He, G., Duan, Y., Peng, R., Jing, X., Qian, T., Wang, L.: Early classification multivariate time series. Neurocomputing 149, 777–787 (2015). https://doi.org/10.1016/j.neucom.2014.07.056. http://www.sciencedirect.com/science/article/pii/S092523121401008X
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79–87 (1991). https://doi.org/10.1162/neco.1991.3.1.79
Krajewski, R., Bock, J., Kloeker, L., Eckstein, L.: The highd dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: 2018 IEEE Intelligent Transportation Systems Conference, pp. 2118–2125. IEEE, Piscataway (2018). https://doi.org/10.1109/ITSC.2018.8569552
Lin, Y.-F., Chen, H.-H., Tseng, V.S., Pei, J.: Reliable early classification on multivariate time series with numerical and categorical attributes. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9077, pp. 199–211. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18038-0_16
Ghalwash, M.F., Obradovic, Z.: Early classification of multivariate temporal observations by extraction of interpretable shapelets. BMC Bioinform. 13(1), 1–12 (2012). https://doi.org/10.1186/1471-2105-13-195
Sadouk, L.: CNN approaches for time series classification. In: Ngan, C.K. (ed.) Time Series Analysis - Data, Methods, and Applications. IntechOpen (2019). https://doi.org/10.5772/intechopen.81170
Samek, W., Wiegand, T., Müller, K.R.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models (2017). http://arxiv.org/pdf/1708.08296v1
Schlechtriemen, J., Wirthmueller, F., Wedel, A., Breuel, G., Kuhnert, K.D.: When will it change the lane? A probabilistic regression approach for rarely occurring events. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 1373–1379 (2015)
van Lent, F.: An explainable artificial intelligence system for small-unit tactical behavior. In: Proceedings of the 16th Conference on Innovative Applications of Artificial Intelligence, pp. 900–907 (2004)
Wissing, C., Nattermann, T., Glander, K.H., Hass, C., Bertram, T.: Lane change prediction by combining movement and situation based probabilities. IFAC-PapersOnLine 50(1), 3554–3559 (2017). https://doi.org/10.1016/j.ifacol.2017.08.960
Xing, Z., Pei, J., Yu, P.S., Wang, K.: Extracting interpretable features for early classification on time series. In: Liu, B., Liu, H., Clifton, C.W., Washio, T., Kamath, C. (eds.) Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 247–258. Society for Industrial and Applied Mathematics, Philadelphia (2011). https://doi.org/10.1137/1.9781611972818.22
Yan, Z., Yang, K., Wang, Z., Yang, B., Kaizuka, T., Nakano, K.: Time to lane change and completion prediction based on gated recurrent unit network. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 102–107. IEEE, Piscataway (2019). https://doi.org/10.1109/IVS.2019.8813838
Ye, L., Keogh, E.: Time series shapelets. In: Elder, J., Fogelman, F.S., Flach, P., Zaki, M. (eds.) KDD 2009, p. 947. ACM, New York (2009). https://doi.org/10.1145/1557019.1557122
Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front. Comput. Sci. 10(1), 96–112 (2016). https://doi.org/10.1007/s11704-015-4478-2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gallitz, O., De Candido, O., Botsch, M., Melz, R., Utschick, W. (2020). Interpretable Machine Learning Structure for an Early Prediction of Lane Changes. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-61609-0_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61608-3
Online ISBN: 978-3-030-61609-0
eBook Packages: Computer ScienceComputer Science (R0)