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Map matching and lane detection based on Markovian behavior, GIS, and IMU data

Jens Trogh, Dick Botteldooren (UGent) , Bert De Coensel (UGent) , Luc Martens (UGent) , Wout Joseph (UGent) and David Plets (UGent)
(2022) IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. 23(3). p.2056-2070
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Abstract
This paper presents a fast, memory-efficient, and worldwide map matching algorithm based on raw geographic coordinates and enriched open map data with support for trajectories on foot, by bike, and motorized vehicles. The proposed algorithm combines the Markovian behavior and the shortest path aspect while taking into account the type and direction of all road segments, information about one-way traffic, maximum allowed speed per road segment, and driving behavior. Furthermore, a self-adapting lane detection algorithm based solely on accelerometer readings is added on top of the map matching algorithm. An experimental validation consisting of 30 trajectories on foot, by bike, and by car, showed the efficiency and accuracy of the proposed algorithms, with an average F1-score and median error of 99.5% and 1.89 m for the map matching algorithm and an average F1-score of 86.7% for the lane detection algorithm, which resulted in the correctly estimated lane 93.0% of the time. Moreover, the proposed technique outperforms existing state of the art techniques with accuracy improvements up to 45.2%.
Keywords
FUSION, SYSTEM, NAVIGATION, VEHICLES, TRACKING, Global Positioning System, Hidden Markov models, Trajectory, Automobiles, Noise measurement, Map matching, lane detection, GPS, accelerometer, sensor, data fusion

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Citation

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MLA
Trogh, Jens, et al. “Map Matching and Lane Detection Based on Markovian Behavior, GIS, and IMU Data.” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 23, no. 3, 2022, pp. 2056–70, doi:10.1109/TITS.2020.3031080.
APA
Trogh, J., Botteldooren, D., De Coensel, B., Martens, L., Joseph, W., & Plets, D. (2022). Map matching and lane detection based on Markovian behavior, GIS, and IMU data. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 23(3), 2056–2070. https://doi.org/10.1109/TITS.2020.3031080
Chicago author-date
Trogh, Jens, Dick Botteldooren, Bert De Coensel, Luc Martens, Wout Joseph, and David Plets. 2022. “Map Matching and Lane Detection Based on Markovian Behavior, GIS, and IMU Data.” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 23 (3): 2056–70. https://doi.org/10.1109/TITS.2020.3031080.
Chicago author-date (all authors)
Trogh, Jens, Dick Botteldooren, Bert De Coensel, Luc Martens, Wout Joseph, and David Plets. 2022. “Map Matching and Lane Detection Based on Markovian Behavior, GIS, and IMU Data.” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 23 (3): 2056–2070. doi:10.1109/TITS.2020.3031080.
Vancouver
1.
Trogh J, Botteldooren D, De Coensel B, Martens L, Joseph W, Plets D. Map matching and lane detection based on Markovian behavior, GIS, and IMU data. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. 2022;23(3):2056–70.
IEEE
[1]
J. Trogh, D. Botteldooren, B. De Coensel, L. Martens, W. Joseph, and D. Plets, “Map matching and lane detection based on Markovian behavior, GIS, and IMU data,” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 23, no. 3, pp. 2056–2070, 2022.
@article{01GKKA5NJDNA1KP5P2JWE21ED5,
  abstract     = {{This paper presents a fast, memory-efficient, and worldwide map matching algorithm based on raw geographic coordinates and enriched open map data with support for trajectories on foot, by bike, and motorized vehicles. The proposed algorithm combines the Markovian behavior and the shortest path aspect while taking into account the type and direction of all road segments, information about one-way traffic, maximum allowed speed per road segment, and driving behavior. Furthermore, a self-adapting lane detection algorithm based solely on accelerometer readings is added on top of the map matching algorithm. An experimental validation consisting of 30 trajectories on foot, by bike, and by car, showed the efficiency and accuracy of the proposed algorithms, with an average F1-score and median error of 99.5% and 1.89 m for the map matching algorithm and an average F1-score of 86.7% for the lane detection algorithm, which resulted in the correctly estimated lane 93.0% of the time. Moreover, the proposed technique outperforms existing state of the art techniques with accuracy improvements up to 45.2%.}},
  author       = {{Trogh, Jens and Botteldooren, Dick and De Coensel, Bert and Martens, Luc and Joseph, Wout and Plets, David}},
  issn         = {{1524-9050}},
  journal      = {{IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}},
  keywords     = {{FUSION,SYSTEM,NAVIGATION,VEHICLES,TRACKING,Global Positioning System,Hidden Markov models,Trajectory,Automobiles,Noise measurement,Map matching,lane detection,GPS,accelerometer,sensor,data fusion}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{2056--2070}},
  title        = {{Map matching and lane detection based on Markovian behavior, GIS, and IMU data}},
  url          = {{http://dx.doi.org/10.1109/TITS.2020.3031080}},
  volume       = {{23}},
  year         = {{2022}},
}
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