Journal
2021 26TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC)
Volume -, Issue -, Pages 753-760Publisher
IEEE
DOI: 10.1145/3394885.3431623
Keywords
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Funding
- National Science Foundation (NSF) [1834701, 1834324, 1839511, 1724341]
- Office of Naval Research (ONR) [N00014-19-1-2496]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1834324] Funding Source: National Science Foundation
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Future autonomous systems will utilize machine learning for decision making, but ensuring system safety is challenging. This paper discusses the challenges and presents methods for ensuring safety in design and operation.
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.
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