Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 18th European Control Conference, ECC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3880-3885 |
| Number of pages | 6 |
| ISBN (Electronic) | 9783907144008 |
| DOIs | |
| State | Published - Jun 1 2019 |
| Event | 18th European Control Conference, ECC 2019 - Naples, Italy Duration: Jun 25 2019 → Jun 28 2019 |
Publication series
| Name | 2019 18th European Control Conference, ECC 2019 |
|---|
Conference
| Conference | 18th European Control Conference, ECC 2019 |
|---|---|
| Country | Italy |
| City | Naples |
| Period | 6/25/19 → 6/28/19 |
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ASJC Scopus subject areas
- Instrumentation
- Control and Optimization
Cite this
Partition-based parametric active model discrimination with applications to driver intention estimation. / Niu, Ruochen; Shen, Qiang; Yong, Sze.
2019 18th European Control Conference, ECC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3880-3885 8795825 (2019 18th European Control Conference, ECC 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Partition-based parametric active model discrimination with applications to driver intention estimation
AU - Niu, Ruochen
AU - Shen, Qiang
AU - Yong, Sze
PY - 2019/6/1
Y1 - 2019/6/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85071571971&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071571971&partnerID=8YFLogxK
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.
ER -