Scenic
Scenic

Scenic is a domain-specific probabilistic programming language for modeling the environments of cyber-physical systems like robots and autonomous cars. A Scenic program defines a distribution over scenes, configurations of physical objects and agents; sampling from this distribution yields concrete scenes which can be simulated to produce training or testing data. Scenic can also define (probabilistic) policies for dynamic agents, allowing modeling scenarios where agents take actions over time in response to the state of the world.

 

Example Scenario Generated by Scenic
                                                Example Scenario Generated by Scenic

 

Acknowledgements

This work is supported in part by the  DARPA Assured Autonomy  program.

Contacts

Daniel J. Fremont

ORGANIZATION

University of California, Berkeley, California, USA

Contributors

Daniel J. Fremont

Edward Kim

Tommaso Dreossi

Shromona Ghosh

Xiangyu Yue

Alberto L. Sangiovanni-Vincentelli

Sanjit A. Seshia

Links
Scenic Documentation
References
Fremont, D. J., Dreossi, T., Sangiovanni-Vincentelli, A. L., & Seshia, S. A. (2019). Scenic: a language for scenario specification and scene generation. In 40th ACM SIGPLAN Conference on Programming Language Design and Implementation. Phoenix, Arizona, USA: Association for Computing Machinery. http://doi.org/10.1145/3314221.3314633
Dreossi, T., Fremont, D. J., Ghosh, S., Kim, E., Ravanbakhsh, H., Vazquez-Chanlatte, M., & Seshia, S. A. (2019). Verifai: A toolkit for the formal design and analysis of artificial intelligence-based systems. In International Conference on Computer Aided Verification . New York City, New York, USA: Springer, Cham. http://doi.org/10.1007/978-3-030-25540-4_25
Fremont, D. J., Chiu, J., Margineantu, D. D., Osipychev, D., & Seshia, S. A. (2020). Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI. Arxiv. Retrieved from https://arxiv.org/abs/2005.07173
Fremont, D. J., Kim, E., Pant, Y. V., Seshia, S. A., Acharya, A., Bruso, X., et al. (2020). Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World. Arxiv. Retrieved from https://arxiv.org/abs/2003.07739
Kim, E., Gopinath, D., Pasareanu, C., & Seshia, S. A. (2020). A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors. In Conference on Computer Vision and Pattern Recognition. Seattle, Washington, USA: IEEE. http://doi.org/10.1109/CVPR42600.2020.01114