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

arXiv: https://arxiv.org/abs/2107.07364 · HTML: https://mlsanity.com/paper/2107.07364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 15 of 15)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Quant GANs: Deep Generation of Financial Time Series](https://mlsanity.com/paper/1907.06673/) (2019)
- [Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World](https://mlsanity.com/paper/2003.07739/) (2020)
- [Painting Outside the Box: Image Outpainting with GANs](https://mlsanity.com/paper/1808.08483/) (2018)
- [Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks](https://mlsanity.com/paper/1904.00655/) (2019)
- [Controlled time series generation for automotive software-in-the-loop testing using GANs](https://mlsanity.com/paper/2002.06611/) (2020)
- [MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry](https://mlsanity.com/paper/2007.07518/) (2020)
- [REAS: Combining Numerical Optimization with SAT Solving](https://mlsanity.com/paper/1802.04408/) (2018)
- [Enhancing Temporal Logic Falsification with Specification Transformation and Valued Booleans](https://mlsanity.com/paper/1910.08306/) (2019)

## Built on by (most cited first, 1 of 1)

- [Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2112.11255/) (2021)

## Abstract (arXiv)

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.