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A Formal and Quantifiable Log Analysis Framework for Test Driving of Autonomous Vehicles by Sung, Kyungbok; Min, Kyoung-Wook; Choi, Jeongdan; Kim, Byung-Cheol is a Engineering article available to read on EtoBox.

What is A Formal and Quantifiable Log Analysis Framework for Test Driving of Autonomous Vehicles about?

We propose a log analysis framework for test driving of autonomous vehicles. The log of a vehicle is a fundamental source to detect and analyze events during driving. A set of dumped logs are, however, usually mixed and fragmented since they are generated concurrently by a number of modules such as sensors, actuators and programs. This makes it hard to analyze them to discover latent errors that could occur due to complex chain reactions among those modules. Our framework provides a logging architecture based on formal specifications, which hierarchically organizes them to find out a priori relationships between them. Then, algorithmic or implementation errors can be detected by examining a posteriori relationships. However, a test in a situation of certain parameters, so called an oracle test, does not necessarily trigger latent violations of the relationships. In our framework, this is remedied by adopting metamorphic testing to quantitatively verify the formal specification. As a working proof, we define three metamorphic relations critical for testing autonomous vehicles and verify them in a quantitative manner based on our logging system.

Who reads A Formal and Quantifiable Log Analysis Framework for Test Driving of Autonomous Vehicles?

Author

Sung, Kyungbok; Min, Kyoung-Wook; Choi, Jeongdan; Kim, Byung-Cheol

Publisher

Molecular Diversity Preservation International; MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: Molecular Diversity Preservation International (MDPI), 2001-; Publons (ISSN 1424-8220)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)