An Innovative Interaction Approach in IMM Filtering for Vehicle Motion Models with Unequal States Dimension

Jasmina Zubača*, Michael Stolz, Richard Seeber, Markus Schratter, Daniel Watzenig

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Robust and adaptive vehicle state estimation and tracking algorithms have become a very important part within the autonomous driving field. The family of interacting multiple model (IMM) filters has shown to provide very effective and accurate state estimation in systems whose behavior patterns change significantly over time. The main reason for the improved performance of IMM filters compared to single model approaches is the mode mixing, which constantly aligns the different models. This paper proposes an innovative way for the mode mixing, when the state-vectors of the models are of different size. The proposed mixing approach consists of two parts: firstly mixing the common states and secondly weighting between original and mixed states based on the model probabilities. Results from artificial simulations and real world measurement setups are shown to demonstrate the validity of the approach. Compared to previously suggested solutions, the proposed approach is more general and the overall complexity of the mode mixing step is reduced, which is the main contribution of the presented paper.
Original languageEnglish
Pages (from-to)3579-3594
Number of pages16
JournalIEEE Transactions on Vehicular Technology
Issue number4
Early online date27 Jan 2022
Publication statusPublished - 1 Apr 2022


  • Interacting Multiple Model Filter
  • Mode Mixing
  • Vehicle Motion Models
  • State Estimation
  • Vehicle Tracking
  • Autonomous Driving
  • Tracking
  • Motion estimation
  • Noise measurement
  • Filtering algorithms
  • Data models
  • State estimation
  • Autonomous vehicles
  • interacting multiple model filter
  • Autonomous driving
  • mode mixing
  • state estimation
  • vehicle motion models
  • vehicle tracking

ASJC Scopus subject areas

  • Aerospace Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Automotive Engineering


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