Scipediacontent: Scipediacontent moved page Draft Content 751458006 to Gangopadhyay et al 2019a

2021-02-01T21:52:08Z

Scipediacontent moved page Draft Content 751458006 to Gangopadhyay et al 2019a

Scipediacontent: Created page with " == Abstract == With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomo..."

2021-02-01T21:52:04Z

Created page with " == Abstract == With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomo..."

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== Abstract ==

With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomous driving systems are highly safety critical in nature and need to be thoroughly tested for a diverse set of conditions before being commercially deployed. Due to the huge complexities involved with Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traditional software testing methods have well-known limitations. They also fail to cover the infinite number of adverse conditions that can occur due to a slight change in the interactions between the environment and the system. Hence, it is important to identify test conditions that push the vehicle under test to breach its safe boundaries. Hazard Based Testing (HBT) methods, inspired by Systems-Theoretic Process Analysis (STPA), identify such parameterized test conditions that can lead to system failure. However, these techniques fall short of discovering the exact parameter values that lead to the failure condition. The presented paper proposes a test case identification technique using Bayesian Optimization. For a given test scenario, the proposed method learns parameter values by observing the system's output. The identified values create test cases that drive the system to violate its safe boundaries. STPA inspired outputs (parameters and pass/fail criteria) are used as inputs to the Bayesian Optimization model. The proposed method was applied to an SAE Level-4 Low Speed Automated Driving (LSAD) system which was modelled in a driving simulator.

== Original document ==

The different versions of the original document can be found in:

* [http://wrap.warwick.ac.uk/139533/1/WRAP-Identification-test-cases-automated-driving-systems-Jennings-2019.pdf http://wrap.warwick.ac.uk/139533/1/WRAP-Identification-test-cases-automated-driving-systems-Jennings-2019.pdf]

* [http://xplorestaging.ieee.org/ielx7/8907344/8916833/08917103.pdf?arnumber=8917103 http://xplorestaging.ieee.org/ielx7/8907344/8916833/08917103.pdf?arnumber=8917103],
: [http://dx.doi.org/10.1109/itsc.2019.8917103 http://dx.doi.org/10.1109/itsc.2019.8917103]

* [https://dblp.uni-trier.de/db/conf/itsc/itsc2019.html#GangopadhyayKDD19 https://dblp.uni-trier.de/db/conf/itsc/itsc2019.html#GangopadhyayKDD19],
: [https://doi.org/10.1109/ITSC.2019.8917103 https://doi.org/10.1109/ITSC.2019.8917103],
: [http://doi.org/10.1109/ITSC.2019.8917103 http://doi.org/10.1109/ITSC.2019.8917103],
: [http://wrap.warwick.ac.uk/139533 http://wrap.warwick.ac.uk/139533],
: [https://academic.microsoft.com/#/detail/2989745381 https://academic.microsoft.com/#/detail/2989745381]

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Gangopadhyay et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 751458006 to Gangopadhyay et al 2019a

Scipediacontent: Created page with " == Abstract == With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomo..."