Intention-Aware Supervisory Control with Driving Safety Applications

Yunus E. Sahin, Zexiang Liu, Kwesi Rutledge, Dimitra Panagou, Sze Zheng Yong, Necmiye Ozay

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

1 Scopus citations

Abstract

This paper proposes a guardian architecture, consisting of an estimation and a supervisor module providing a set of inputs that guarantees safety, in driving scenarios. The main idea is to offline compute a library of robust controlled invariant sets (RCIS), for each possible driver intention model of the other vehicles, together with an intention-agnostic albeit conservative RCIS. At runtime, when the intention estimation module determines which driver model the other vehicles are following, the appropriate RCIS is chosen to provide the safe and less conservative input set for supervision. We show that the composition of the intention estimation module with the proposed intention-aware supervisor module is safe. Moreover, we show how to compute intention-agnostic and intention-specific RCIS by growing an analytically found simple invariant safe set. The results are demonstrated on a case study on how to safely interact with a human-driven car on a highway scenario, using data collected from a driving simulator.

Original languageEnglish (US)
Title of host publicationCCTA 2019 - 3rd IEEE Conference on Control Technology and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
ISBN (Electronic)9781728127675
DOIs
StatePublished - Aug 2019
Externally publishedYes
Event3rd IEEE Conference on Control Technology and Applications, CCTA 2019 - Hong Kong, China
Duration: Aug 19 2019Aug 21 2019

Publication series

NameCCTA 2019 - 3rd IEEE Conference on Control Technology and Applications

Conference

Conference3rd IEEE Conference on Control Technology and Applications, CCTA 2019
Country/TerritoryChina
CityHong Kong
Period8/19/198/21/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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