Abstract
Simulation of the real-world traffic can be used to help validate the transportation policies. A good simulator means the simulated traffic is similar to real-world traffic, which often requires dense traffic trajectories (i.e., with high sampling rate) to cover dynamic situations in the real world. However, in most cases, the real-world trajectories are sparse, which makes simulation challenging. In this paper, we present a novel framework ImIn-GAIL to address the problem of learning to simulate the driving behavior from sparse real-world data. The proposed architecture incorporates data interpolation with the behavior learning process of imitation learning. To the best of our knowledge, we are the first to tackle the data sparsity issue for behavior learning problems. We investigate our framework on both synthetic and real-world trajectory datasets of driving vehicles, showing that our method outperforms various baselines and state-of-the-art methods.
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Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: ICML (2004)
Asamer, J., van Zuylen, H.J., Heilmann, B.: Calibrating car-following parameters for snowy road conditions in the microscopic traffic simulator VISSIM. IET Intell. Transp. Syst. 7(1), 114–121 (2013)
Bhattacharyya, R.P., Phillips, D.J., Wulfe, B., Morton, J., Kuefler, A., Kochenderfer, M.J.: Multi-agent imitation learning for driving simulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2018)
Ho, J., Ermon, S.: Generative adversarial imitation learning. In: NeurIPS (2016)
Kesting, A., Treiber, M.: Calibrating car-following models by using trajectory data: methodological study. Transp. Res. Rec. 2088(1), 148–156 (2008)
Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5(3&4), 128–138 (2012)
Krauss, S.: Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics. Ph.D. thesis (1998)
Kuefler, A., Morton, J., Wheeler, T., Kochenderfer, M.: Imitating driver behavior with generative adversarial networks. In: IEEE Intelligent Vehicles Symposium (IV). IEEE (2017)
Leutzbach, W., Wiedemann, R.: Development and applications of traffic simulation models at the Karlsruhe Institut fur Verkehrwesen. Traffic Eng. Control 27(5), 270–278 (1986)
Li, S.C.X., Marlin, B.M.: A scalable end-to-end gaussian process adapter for irregularly sampled time series classification. In: NeurIPS (2016)
Liu, Y., Zhao, K., Cong, G., Bao, Z.: Online anomalous trajectory detection with deep generative sequence modeling. In: ICDE (2020)
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: SIGSPATIAL. ACM (2009)
Michie, D., Bain, M., Hayes-Miches, J.: Cognitive models from subcognitive skills. IEEE Control Eng. Ser. 44 (1990)
Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. de Physique I 2(12), 2221–2229 (1992)
Ng, A.Y., Russell, S.J., et al.: Algorithms for inverse reinforcement learning. In: ICML (2000)
Osorio, C., Punzo, V.: Efficient calibration of microscopic car-following models for large-scale stochastic network simulators. Transp. Res. Part B: Methodol. 119, 156–173 (2019)
Schulman, J., Levine, S., Moritz, P., Jordan, M.I., Abbeel, P.: Trust region policy optimization. In: ICML (2015)
Song, J., Ren, H., Sadigh, D., Ermon, S.: Multi-agent generative adversarial imitation learning. In: NeurIPS (2018)
Sugiyama, Y., et al.: Traffic jams without bottlenecks-experimental evidence for the physical mechanism of the formation of a jam. New J. Phys. 10(3), 1–8 (2008)
Tang, X., et al.: Joint modeling of dense and incomplete trajectories for citywide traffic volume inference. In: The World Wide Web Conference. ACM (2019)
Torabi, F., Warnell, G., Stone, P.: Behavioral cloning from observation. In: IJCAI (2018)
Wei, H., et al.: PressLight: learning max pressure control to coordinate traffic signals in arterial network. In: KDD (2019)
Wei, H., et al.: CoLight: learning network-level cooperation for traffic signal control. In: CIKM (2019)
Wei, H., Zheng, G., Gayah, V., Li, Z.: A survey on traffic signal control methods. arXiv preprint arXiv:1904.08117 (2019)
Wei, H., Zheng, G., Yao, H., Li, Z.: IntelliLight: a reinforcement learning approach for intelligent traffic light control. In: KDD (2018)
Wu, C., Kreidieh, A., Vinitsky, E., Bayen, A.M.: Emergent behaviors in mixed-autonomy traffic. In: Conference on Robot Learning (2017)
Wu, Y., Tan, H., Ran, B.: Differential variable speed limits control for freeway recurrent bottlenecks via deep reinforcement learning. arXiv preprint arXiv:1810.10952 (2018)
Yi, X., Zheng, Y., Zhang, J., Li, T.: ST-MVL: filling missing values in geo-sensory time series data. In: IJCAI. AAAI Press (2016)
Zhang, H., et al.: CityFlow: a multi-agent reinforcement learning environment for large scale city traffic scenario. In: International World Wide Web Conference (2019)
Zheng, G., Liu, H., Xu, K., Li, Z.: Learning to simulate vehicle trajectories from demonstrations. In: ICDE (2020)
Zheng, K., Zheng, Y., Xie, X., Zhou, X.: Reducing uncertainty of low-sampling-rate trajectories. In: ICDE (2012)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 1–41 (2015)
Ziebart, B.D., Maas, A.L., Bagnell, J.A., Dey, A.K.: Maximum entropy inverse reinforcement learning. In: AAAI, vol. 8 (2008)
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The work was supported in part by NSF awards #1652525 and #1618448. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.
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Wei, H., Chen, C., Liu, C., Zheng, G., Li, Z. (2021). Learning to Simulate on Sparse Trajectory Data. In: Dong, Y., Mladenić, D., Saunders, C. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12460. Springer, Cham. https://doi.org/10.1007/978-3-030-67667-4_32
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