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DNN-Based Estimation for Misalignment State of Automotive Radar Sensor.

Junho Kim1, Taewon Jeong2, Seongwook Lee1

  • 1School of Electrical and Electronics Engineering, College of ICT Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a deep neural network (DNN) method to estimate radar sensor tilt angles without bumper removal. This ensures reliable advanced driver assistance systems (ADAS) performance and vehicle safety.

Keywords:
automotive radardeep neural networkfrequency-modulated continuous wave radarmisalignmenttilt angle

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Area of Science:

  • Automotive Engineering
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Advanced driver assistance systems (ADAS) and autonomous vehicles rely on accurate automotive sensors (radar, lidar, camera).
  • External shocks can cause sensor misalignment, degrading performance and compromising vehicle safety.
  • Radar sensor tilt, especially towards the ground or sky, significantly impairs sensing capabilities.

Purpose of the Study:

  • To develop a non-invasive method for estimating radar sensor vertical tilt angles.
  • To ensure stable sensor detection performance and enhance driver safety.
  • To facilitate easier maintenance of vehicle radar sensors.

Main Methods:

  • Acquired radar data at various tilt angles and distances to characterize signal behavior.
  • Extracted range profiles from received radar signals.
  • Designed and implemented a deep neural network (DNN) classifier using range profiles as input for angle estimation.

Main Results:

  • The proposed DNN-based classifier accurately estimates radar sensor tilt angles.
  • The method achieves an average estimation accuracy exceeding 99.08%.
  • The angle estimation is independent of the measured distance.

Conclusions:

  • The developed DNN method provides an effective and accurate way to estimate radar sensor misalignment.
  • This non-invasive technique simplifies radar sensor maintenance without bumper removal.
  • The findings contribute to ensuring the reliability and safety of ADAS and autonomous driving systems.