Learning to Simulate on Sparse Trajectory Data

Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationApplied Data Science Track - European Conference, ECML PKDD 2020, Proceedings
EditorsYuxiao Dong, Dunja Mladenic, Craig Saunders
PublisherSpringer Science and Business Media Deutschland GmbH
Pages530-545
Number of pages16
ISBN (Print)9783030676667
DOIs
StatePublished - 2021
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: Sep 14 2020Sep 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12460 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online
Period9/14/209/18/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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