SilGAN: Generating driving maneuvers for scenario-based software-in-the-loop testing
This work addresses the need for more efficient and credible simulation-based testing in the automotive industry, reducing reliance on costly field tests.
The authors tackled the problem of expensive field tests in automotive software testing by introducing SilGAN, a deep generative model that generates realistic vehicle state transitions from concise driving scenario specifications, enabling rapid and inexpensive software-in-the-loop testing.
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.