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Automotive software testing continues to rely largely upon expensive fieldtests to ensure quality because alternatives like simulation-based testing arerelatively immature. As a step towards lowering reliance on field tests, wepresent SilGAN, a deep generative model that eases specification, stimulusgeneration, and automation of automotive software-in-the-loop testing. Themodel is trained using data recorded from vehicles in the field. Upon training,the model uses a concise specification for a driving scenario to generaterealistic vehicle state transitions that can occur during such a scenario. Suchauthentic emulation of internal vehicle behavior can be used for rapid,systematic and inexpensive testing of vehicle control software. In addition, bypresenting a targeted method for searching through the information learned bythe 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 thescope and credibility of automotive simulation-based testing. This reduces timeto market while helping maintain required standards of quality.

SilGAN: Generating driving maneuvers for scenario-based software-in-the-loop testing