Data-driven reachability analysis for human-in-the-loop systems

Vijay Govindarajan, Katherine Driggs-Campbell, Ruzena Bajcsy

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

Abstract

In order to design safe and effective human-in-the-loop systems, developing robust and useful models of human behavior is absolutely vital. However, this problem is highly difficult to address, given that humans often act unpredictably. We investigate the problem of determining prediction sets for human-driven vehicles using Hamilton-Jacobi reachability analysis and empirical observations from driving datasets. Given evaluation metrics of accuracy, precision, and risk, we optimize disturbance bounds to construct forward reachable sets with high precision that satisfy accuracy and risk constraints. To demonstrate the approach, we apply our framework to a lane changing scenario to provide set predictions that provide safety guarantees without being over-conservative. We show an example of this method that allows us to construct a reachable set with over 85% accuracy and under 25% risk.

Original languageEnglish (US)
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2617-2622
Number of pages6
ISBN (Electronic)9781509028733
DOIs
StatePublished - Jan 18 2018
Event56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia
Duration: Dec 12 2017Dec 15 2017

Publication series

Name2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume2018-January

Other

Other56th IEEE Annual Conference on Decision and Control, CDC 2017
CountryAustralia
CityMelbourne
Period12/12/1712/15/17

ASJC Scopus subject areas

  • Decision Sciences (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Control and Optimization

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