TY - GEN
T1 - Partition-based parametric active model discrimination with applications to driver intention estimation
AU - Niu, Ruochen
AU - Shen, Qiang
AU - Yong, Sze Zheng
N1 - Funding Information:
This work was supported in part by DARPA grant D18AP00073. Toyota Research Institute (“TRI”) also provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity.
Publisher Copyright:
© 2019 EUCA.
PY - 2019/6
Y1 - 2019/6
N2 - In this paper, we propose a partition-based parametric active model discrimination approach that distinguishes among a set of discrete-time affine time-invariant models with uncontrolled inputs, model-independent parameters that are revealed in real-time and noise. By partitioning the operating region of the parameters, the problem turns into a sequence of offline optimization problems. Thus, at each time instant, we only need to determine which subregion in the resulting partition tree the revealed parameters lie in and select the corresponding pre-computed inputs. The offline optimal input design problem is formulated as a bilevel problem and further cast as a mixed-integer linear program (MILP). Finally, we demonstrate the effectiveness of the proposed approach for estimating driver intention in a lane-changing scenario.
AB - In this paper, we propose a partition-based parametric active model discrimination approach that distinguishes among a set of discrete-time affine time-invariant models with uncontrolled inputs, model-independent parameters that are revealed in real-time and noise. By partitioning the operating region of the parameters, the problem turns into a sequence of offline optimization problems. Thus, at each time instant, we only need to determine which subregion in the resulting partition tree the revealed parameters lie in and select the corresponding pre-computed inputs. The offline optimal input design problem is formulated as a bilevel problem and further cast as a mixed-integer linear program (MILP). Finally, we demonstrate the effectiveness of the proposed approach for estimating driver intention in a lane-changing scenario.
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U2 - 10.23919/ECC.2019.8795825
DO - 10.23919/ECC.2019.8795825
M3 - Conference contribution
AN - SCOPUS:85071571971
T3 - 2019 18th European Control Conference, ECC 2019
SP - 3880
EP - 3885
BT - 2019 18th European Control Conference, ECC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th European Control Conference, ECC 2019
Y2 - 25 June 2019 through 28 June 2019
ER -