Lane-Level Map Matching Based on HMM

Lane-level map matching is essential for autonomous driving. In this paper, the authors propose a Hidden Markov Model (HMM) for matching a trajectory of noisy global positioning system (GPS) measurements to the road lanes in which the vehicle records its positions. To our knowledge, this is the first time that HMM is used for lane-level map matching. Apart from GPS values, the model is further assisted by yaw rate data (converted to a lane change indicator signal) and visual cues in the form of the left and right lane marking types (dashed, solid, etc.). Having defined expressions for the HMM emission and transition probabilities, the authors evaluate their model to demonstrate that it achieves 95.1% recall and 3.3% median path length error for motorway trajectories.

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  • English

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  • Accession Number: 01785358
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 2 2021 10:01AM