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A Formal and Quantifiable Log Analysis Framework for Test Driving of Autonomous Vehicles.

Kyungbok Sung1, Kyoung-Wook Min1, Jeongdan Choi1

  • 1Autonomous Driving Intelligence Research Section, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea.

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|March 4, 2020
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Summary

This study introduces a new log analysis framework for autonomous vehicle test driving. It uses formal specifications and metamorphic testing to detect complex errors in vehicle logs.

Keywords:
autonomous vehicle testingfailure detectionformal methodslog analysis frameworkmetamorphic testing

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Area of Science:

  • Autonomous Systems Engineering
  • Software Engineering
  • Formal Methods

Background:

  • Vehicle logs are crucial for analyzing autonomous vehicle performance and detecting errors.
  • Concurrent log generation from multiple modules (sensors, actuators, programs) results in fragmented and mixed data, hindering error analysis.
  • Traditional oracle testing may not reveal latent errors caused by complex module interactions.

Purpose of the Study:

  • To propose a novel log analysis framework for autonomous vehicle test driving.
  • To address the challenges of fragmented and mixed log data for effective error detection.
  • To enhance the verification of formal specifications using metamorphic testing.

Main Methods:

  • Developed a logging architecture based on formal specifications for hierarchical organization of vehicle logs.
  • Implemented a method to analyze a priori and a posteriori relationships within the organized logs.
  • Integrated metamorphic testing to quantitatively verify formal specifications, overcoming limitations of oracle testing.

Main Results:

  • The framework successfully organizes fragmented vehicle logs using formal specifications.
  • Algorithmic and implementation errors are detectable through the analysis of log relationships.
  • Metamorphic testing quantitatively verified three critical metamorphic relations for autonomous vehicle testing.

Conclusions:

  • The proposed framework offers a robust solution for analyzing complex autonomous vehicle logs.
  • Formal specifications and metamorphic testing are effective in uncovering latent errors.
  • This approach enhances the reliability and safety of autonomous vehicle development and testing.