SilGAN: Generating driving maneuvers for scenario-based software-in-The-loop testing
Paper i 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, Matematiska vetenskaper, Tillämpad matematik och statistik

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


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







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