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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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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