Know the Unknowns: Addressing Disturbances and Uncertainties in Autonomous Systems : Invited Paper

Qi Zhu, Wenchao Li, Hyoseung Kim, Yecheng Xiang, Kacper Wardega, Zhilu Wang, Yixuan Wang, Hengyi Liang, Chao Huang, Jiameng Fan, Hyunjong Choi

Research output: Contribution to journalConference articlepeer-review

Abstract

Future autonomous systems will employ complex sensing, computation, and communication components for their perception, planning, control, and coordination, and could operate in highly dynamic and uncertain environment with safety and security assurance. To realize this vision, we have to better understand and address the challenges from the 'unknowns' - the unexpected disturbances from component faults, environmental interference, and malicious attacks, as well as the inherent uncertainties in system inputs, model inaccuracies, and machine learning techniques (particularly those based on neural networks). In this work, we will discuss these challenges, propose our approaches in addressing them, and present some of the initial results. In particular, we will introduce a cross-layer framework for modeling and mitigating execution uncertainties (e.g., timing violations, soft errors) with weakly-hard paradigm, quantitative and formal methods for ensuring safe and time-predictable application of neural networks in both perception and decision making, and safety-assured adaptation strategies in dynamic environment.

Original languageEnglish (US)
Article number9256731
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2020-November
DOIs
StatePublished - Nov 2 2020
Event39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States
Duration: Nov 2 2020Nov 5 2020

Keywords

  • adaptation
  • Autonomous systems
  • disturbance
  • neural networks
  • safety verification
  • uncertainty
  • weakly-hard

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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