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Map matching when the map is wrong: Efficient on/off road vehicle tracking and map learning

Published: 05 November 2019 Publication History

Abstract

Given a sequence of possibly sparse and noisy GPS traces and a map of the road network, map matching algorithms can infer the most accurate trajectory on the road network. However, if the road network is wrong (for example due to missing or incorrectly mapped roads, missing parking lots, misdirected turn restrictions or misdirected one-way streets) standard map matching algorithms fail to reconstruct the correct trajectory.
In this paper, an algorithm to tracking vehicles able to move both on and off the known road network is formulated. It efficiently unifies existing hidden Markov model (HMM) approaches for map matching and standard free-space tracking methods (e.g. Kalman smoothing) in a principled way. The algorithm is a form of interacting multiple model (IMM) filter subject to an additional assumption on the type of model interaction permitted, termed here as semi-interacting multiple model (sIMM) filter. A forward filter (suitable for real-time tracking) and backward MAP sampling step (suitable for MAP trajectory inference and map matching) are described. The framework set out here is agnostic to the specific tracking models used, and makes clear how to replace these components with others of a similar type. In addition to avoiding generating misleading map matching trajectories, this algorithm can be applied to learn map features by detecting unmapped or incorrectly mapped roads and parking lots, incorrectly mapped turn restrictions and road directions.

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Cited By

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  • (2024)Detecting road network errors from trajectory data with partial map matching and bidirectional recurrent neural network modelInternational Journal of Geographical Information Science10.1080/13658816.2024.230615838:3(478-502)Online publication date: 24-Jan-2024
  • (2023)A Practical HMM-Based Map-Matching Method for Pedestrian Navigation2023 International Conference on Information Networking (ICOIN)10.1109/ICOIN56518.2023.10049007(806-811)Online publication date: 11-Jan-2023
  • (2022)From driving trajectories to driving paths: a survey on map-matching AlgorithmsCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00101-w4:3(252-267)Online publication date: 23-May-2022
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cover image ACM Conferences
IWCTS'19: Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science
November 2019
89 pages
ISBN:9781450369671
DOI:10.1145/3357000
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 05 November 2019

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Author Tags

  1. Bayesian filtering
  2. Map matching
  3. map learning
  4. object tracking
  5. road networks

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View all
  • (2024)Detecting road network errors from trajectory data with partial map matching and bidirectional recurrent neural network modelInternational Journal of Geographical Information Science10.1080/13658816.2024.230615838:3(478-502)Online publication date: 24-Jan-2024
  • (2023)A Practical HMM-Based Map-Matching Method for Pedestrian Navigation2023 International Conference on Information Networking (ICOIN)10.1109/ICOIN56518.2023.10049007(806-811)Online publication date: 11-Jan-2023
  • (2022)From driving trajectories to driving paths: a survey on map-matching AlgorithmsCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00101-w4:3(252-267)Online publication date: 23-May-2022
  • (2021)Error Decomposition for Hybrid Localization Systems2021 IEEE International Intelligent Transportation Systems Conference (ITSC)10.1109/ITSC48978.2021.9564415(149-156)Online publication date: 19-Sep-2021
  • (2019)Introducing CrowdMapping: A Novel System for Generating Autonomous Driving Aiding Traffic Network Databases2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)10.1109/ICCAIRO47923.2019.00010(7-12)Online publication date: May-2019

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