Ayoosh Bansal

2024

1. Conference

   Synergistic Perception and Control Simplex for Verifiable Safe Vertical Landing

   Ayoosh Bansal, Yang Zhao, James Zhu, Sheng Cheng, Yuliang Gu, Hyung Jin Yoon, Hunmin Kim, Naira Hovakimyan, and Lui R. Sha

   2024

   Abs arXiv Bib PDF Slides

   Perception, Planning, and Control form the essential components of autonomy in advanced air mobility. This work advances the holistic integration of these components to enhance the performance and robustness of the complete cyber-physical system. We adapt Perception Simplex, a system for verifiable collision avoidance amidst obstacle detection faults, to the vertical landing maneuver for autonomous air mobility vehicles. We improve upon this system by replacing static assumptions of control capabilities with dynamic confirmation, i.e., real-time confirmation of control limitations of the system, ensuring reliable fulfillment of safety maneuvers and overrides, without dependence on overly pessimistic assumptions. Parameters defining control system capabilities and limitations, e.g., maximum deceleration, are continuously tracked within the system and used to make safety-critical decisions. We apply these techniques to propose a verifiable collision avoidance solution for autonomous aerial mobility vehicles operating in cluttered and potentially unsafe environments.

   @misc{bansal2023synergistic,
     author = {Bansal, Ayoosh and Zhao, Yang and Zhu, James and Cheng, Sheng and Gu, Yuliang and Yoon, Hyung Jin and Kim, Hunmin and Hovakimyan, Naira and Sha, Lui R.},
     title = {Synergistic Perception and Control Simplex for Verifiable Safe Vertical Landing},
     booktitle = {AIAA SCITECH 2024 Forum},
     chapter = {},
     pages = {},
     doi = {10.2514/6.2024-1167},
     url = {https://arc.aiaa.org/doi/abs/10.2514/6.2024-1167},
     year = {2024},
   }

2023

1. Journal

   System Auditing for Real-Time Systems

   Ayoosh Bansal, Anant Kandikuppa, Monowar Hasan, Chien-Ying Chen, Adam Bates, and Sibin Mohan

   ACM Trans. Priv. Secur., Sep 2023

   Abs Bib PDF Code

   System auditing is an essential tool for detecting malicious events and conducting forensic analysis. Although used extensively on general-purpose systems, auditing frameworks have not been designed with consideration for the unique constraints and properties of Real-Time Systems (RTS). System auditing could provide tremendous benefits for security-critical RTS. However, a naı̈ve deployment of auditing on RTS could violate the temporal requirements of the system while also rendering auditing incomplete and ineffectual. To ensure effective auditing that meets the computational needs of recording complete audit information while adhering to the temporal requirements of the RTS, it is essential to carefully integrate auditing into the real-time (RT) schedule. This work adapts the Linux Audit framework for use in RT Linux by leveraging the common properties of such systems, such as special purpose and predictability. Ellipsis, an efficient system for auditing RTS is devised that learns the expected benign behaviors of the system and generates succinct descriptions of the expected activity. Evaluations using varied RT applications show that Ellipsis reduces the volume of audit records generated during benign activity by up to 97.55%, while recording detailed logs for suspicious activities. Empirical analyses establish that the auditing infrastructure adheres to the properties of predictability and isolation that are important to RTS. Furthermore, the schedulability of RT task sets under audit is comprehensively analyzed to enable the safe integration of auditing in RT task schedules.

   @article{bansal2023system,
     address = {New York, NY, USA},
     author = {Bansal, Ayoosh and Kandikuppa, Anant and Hasan, Monowar and Chen, Chien-Ying and Bates, Adam and Mohan, Sibin},
     doi = {10.1145/3625229},
     issn = {2471-2566},
     journal = {ACM Trans. Priv. Secur.},
     keywords = {security auditing, model-based reduction, cyber-physical systems},
     month = sep,
     publisher = {Association for Computing Machinery},
     title = {System Auditing for Real-Time Systems},
     url = {https://doi.org/10.1145/3625229},
     year = {2023}
   }

2022

1. Conference

   Verifiable obstacle detection

   Ayoosh Bansal, Hunmin Kim, Simon Yu, Bo Li, Naira Hovakimyan, Marco Caccamo, and Lui Sha

   In 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE), Sep 2022

   arXiv Bib PDF Code Video

   @inproceedings{bansal2022verifiable,
     title = {Verifiable obstacle detection},
     author = {Bansal, Ayoosh and Kim, Hunmin and Yu, Simon and Li, Bo and Hovakimyan, Naira and Caccamo, Marco and Sha, Lui},
     booktitle = {2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)},
     pages = {61--72},
     year = {2022},
     organization = {IEEE},
     video = {https://youtu.be/lCfN86efqh8}
   }

2. Preprint

   Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles

   Ayoosh Bansal, Simon Yu, Hunmin Kim, Bo Li, Naira Hovakimyan, Marco Caccamo, and Lui Sha

   arXiv preprint arXiv:2209.01710, Sep 2022

   arXiv Bib PDF

   @article{bansal2022synergistic,
     title = {Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles},
     author = {Bansal, Ayoosh and Yu, Simon and Kim, Hunmin and Li, Bo and Hovakimyan, Naira and Caccamo, Marco and Sha, Lui},
     journal = {arXiv preprint arXiv:2209.01710},
     year = {2022},
   }

2020

1. Conference

   Reconciling predictability and coherent caching

   Ayoosh Bansal, Jayati Singh, Yifan Hao, Jen-Yang Wen, Renato Mancuso, and Marco Caccamo

   In 2020 9th Mediterranean Conference on Embedded Computing (MECO), Sep 2020

   arXiv Bib PDF Code Slides

   @inproceedings{bansal2020reconciling,
     title = {Reconciling predictability and coherent caching},
     author = {Bansal, Ayoosh and Singh, Jayati and Hao, Yifan and Wen, Jen-Yang and Mancuso, Renato and Caccamo, Marco},
     booktitle = {2020 9th Mediterranean Conference on Embedded Computing (MECO)},
     pages = {1--6},
     year = {2020},
     organization = {IEEE},
   }