TY - GEN
T1 - Intention-Aware Supervisory Control with Driving Safety Applications
AU - Sahin, Yunus E.
AU - Liu, Zexiang
AU - Rutledge, Kwesi
AU - Panagou, Dimitra
AU - Yong, Sze Zheng
AU - Ozay, Necmiye
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85077802745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077802745&partnerID=8YFLogxK
U2 - 10.1109/CCTA.2019.8920426
DO - 10.1109/CCTA.2019.8920426
M3 - Conference contribution
T3 - CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications
SP - 1
EP - 8
BT - CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Conference on Control Technology and Applications, CCTA 2019
Y2 - 19 August 2019 through 21 August 2019
ER -