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Performance Analysis of Camera-based Object Detection for Automated Vehicles.

Thomas Ponn1, Thomas Kröger1, Frank Diermeyer1

  • 1Institute of Automotive Technology, Technical University of Munich, 85748 Garching, Germany.

Sensors (Basel, Switzerland)
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

This study investigates factors affecting AI-powered camera object detection for automated vehicles. It introduces a new SHapley Additive exPlanations (SHAP) method to explain detection performance and identify critical testing scenarios.

Keywords:
artificial intelligenceautomated vehiclescamera sensorcomputer visioncritical scenariosexplainable artificial intelligence (AI)object detectionsafety

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Automated Driving Systems

Background:

  • Ensuring the safety of automated vehicles requires robust risk assessment of all system components.
  • Camera-based object detection, crucial for automated vehicles, is susceptible to environmental conditions and algorithmic limitations.

Purpose of the Study:

  • To comprehensively investigate factors influencing camera-based object detection performance beyond environmental conditions.
  • To develop a precise modeling approach for detection performance and explain AI-driven detection results.
  • To utilize findings for creating critical scenarios for testing and type approval of automated vehicles.

Main Methods:

  • Investigated various factors impacting camera-based object detection performance.
  • Developed a modeling approach based on identified influence factors.
  • Applied the SHapley Additive exPlanations (SHAP) method to analyze and explain object detection algorithm performance.

Main Results:

  • Identified multiple factors, such as object rotation and image position, that consistently affect detection performance across different algorithms.
  • Demonstrated that SHAP analysis can effectively explain the detection performance of various object detection algorithms.
  • Revealed specific weaknesses in tested object detectors.

Conclusions:

  • The study provides a novel method for understanding and explaining AI-based object detection in automated vehicles.
  • Findings enable the derivation of challenging test scenarios crucial for the validation and type approval of automated driving systems.
  • The research contributes to enhancing the safety and reliability of automated vehicles through improved testing methodologies.