Intention-Aware Supervisory Control with Driving Safety Applications

Yunus Emre Sahin1, Zexiang Liu1, Kwesi Rutledge2, Dimitra Panagou3, Sze Zheng Yong4, Necmiye Ozay5

  • 1University of Michigan
  • 2University of Michigan - Ann Arbor
  • 3University of Michigan, Ann Arbor
  • 4Arizona State University
  • 5Univ. of Michigan



Regular Session


10:30 - 12:30 | Mon 19 Aug | Lau, 5-203 | MoA1

Control for Connected and Automated Vehicles

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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 run-time, 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.

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