SilGAN: Generating driving maneuvers for scenario-based software-in-The-loop testing
Paper in proceeding, 2021

Automotive software testing continues to rely largely upon expensive field tests to ensure quality because alternatives like simulation-based testing are relatively immature. As a step towards lowering reliance on field tests, we present SilGAN, a deep generative model that eases specification, stimulus generation, and automation of automotive software-in-The-loop testing. The model is trained using data recorded from vehicles in the field. Upon training, the model uses a concise specification for a driving scenario to generate realistic vehicle state transitions that can occur during such a scenario. Such authentic emulation of internal vehicle behavior can be used for rapid, systematic and inexpensive testing of vehicle control software. In addition, by presenting a targeted method for searching through the information learned by the model, we show how a test objective like code coverage can be automated. The data driven end-To-end testing pipeline that we present vastly expands the scope and credibility of automotive simulation-based testing. This reduces time to market while helping maintain required standards of quality.

software-in-The-loop testing

generative adversarial networks

time series generation

latent space search


Dhasarathy Parthasarathy

Volvo Group

Anton Johansson

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Proceedings - 3rd IEEE International Conference on Artificial Intelligence Testing, AITest 2021

9781665434812 (ISBN)

3rd IEEE International Conference on Artificial Intelligence Testing, AITest 2021
Virtual, Online, United Kingdom,

Subject Categories

Software Engineering

Vehicle Engineering

Computer Systems



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