Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems

Qi Zhu, Chao Huang, Ruochen Jiao, Shuyue Lan, Hengyi Liang, Xiangguo Liu, Yixuan Wang, Zhilu Wang, Shichao Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations


Future autonomous systems will employ sophisticated machine learning techniques for the sensing and perception of the surroundings and the making corresponding decisions for planning, control, and other actions. They often operate in highly dynamic, uncertain and challenging environment, and need to meet stringent timing, resource, and mission requirements. In particular, it is critical and yet very challenging to ensure the safety of these autonomous systems, given the uncertainties of the system inputs, the constant disturbances on the system operations, and the lack of analyzability for many machine learning methods (particularly those based on neural networks). In this paper, we will discuss some of these challenges, and present our work in developing automated, quantitative, and formalized methods and tools for ensuring the safety of autonomous systems in their design and during their runtime adaptation. We argue that it is essential to take a holistic approach in addressing system safety and other safety-related properties, vertically across the functional, software, and hardware layers, and horizontally across the autonomy pipeline of sensing, perception, planning, and control modules. This approach could be further extended from a single autonomous system to a multi-agent system where multiple autonomous agents perform tasks in a collaborative manner. We will use connected and autonomous vehicles (CAVs) as the main application domain to illustrate the importance of such holistic approach and show our initial efforts in this direction.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781450379991
StatePublished - Jan 18 2021
Event26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021 - Virtual, Online, Japan
Duration: Jan 18 2021Jan 21 2021

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC


Conference26th Asia and South Pacific Design Automation Conference, ASP-DAC 2021
CityVirtual, Online

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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