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Adaptive physics-informed trajectory reconstruction exploiting driver behavior and car dynamics.

Michail A Makridis1, Anastasios Kouvelas2

  • 1Department of Civil, Environmental and Geomatic Engineering, ETH, 8093, Zurich, Switzerland. michail.makridis@ivt.baug.ethz.ch.

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Summary
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This study introduces an adaptive framework to improve vehicle trajectory reconstruction from noisy sensor data. It minimizes errors by optimizing filtering, ensuring more accurate traffic flow analysis.

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Area of Science:

  • Traffic flow dynamics
  • Data analysis and signal processing

Background:

  • Trajectory data analysis offers traffic insights but suffers from measurement noise and device heterogeneity.
  • Existing methods for trajectory reconstruction often rely on heuristic filtering, limiting accuracy without ground truth.

Purpose of the Study:

  • To develop an adaptive, physics-informed framework for trajectory reconstruction.
  • To minimize speed error and ensure compatibility with realistic vehicle dynamics and driver behavior.

Main Methods:

  • An iterative approach to detect optimal filtering magnitude.
  • Minimizing local acceleration variance under stable conditions.
  • Ensuring compatibility with feasible vehicle acceleration dynamics and common driver behavior characteristics.

Main Results:

  • Significant reduction in speed error observed in assessments.
  • Framework demonstrates invariability across different data acquisition devices.
  • Enables objective comparison of drivers irrespective of sensing equipment.

Conclusions:

  • The proposed framework enhances the accuracy and reliability of trajectory reconstruction.
  • It provides a robust method for analyzing traffic flow data from diverse sources.
  • Facilitates standardized driver behavior assessment in intelligent transportation systems.