Partition-Based Parametric Active Model Discrimination with Applications to Driver Intention Estimation

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.

[1]  Ian R. Manchester,et al.  Input design for discrimination between classes of LTI models , 2015, Autom..

[2]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[3]  Hongwei Lou,et al.  The distinguishability of linear control systems , 2009 .

[4]  Richard D. Braatz,et al.  Closed-loop input design for guaranteed fault diagnosis using set-valued observers , 2016, Autom..

[5]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[6]  Kenneth H. Rosen.  Discrete mathematics and its applications , 1984 .

[7]  Constantine Caramanis,et al.  Theory and Applications of Robust Optimization , 2010, SIAM Rev..

[8]  Stephen L. Campbell,et al.  Auxiliary signal design for active failure detection in uncertain linear systems with a priori information , 2006, Autom..

[9]  Emilio Frazzoli,et al.  Generalized innovation and inference algorithms for hidden mode switched linear stochastic systems with unknown inputs , 2014, 53rd IEEE Conference on Decision and Control.

[10]  Constantine Caramanis,et al.  Finite Adaptability in Multistage Linear Optimization , 2010, IEEE Transactions on Automatic Control.

[11]  Davide Martino Raimondo,et al.  Active fault diagnosis: A multi-parametric approach , 2017, Autom..

[12]  Sze Zheng Yong,et al.  Optimal Input Design for Affine Model Discrimination with Applications in Intention-Aware Vehicles , 2017, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).

[13]  Sze Zheng Yong,et al.  Active Model Discrimination with Applications to Fraud Detection in Smart Buildings , 2017 .

[14]  Richard D. Braatz,et al.  Input design for guaranteed fault diagnosis using zonotopes , 2014, Autom..

[15]  Anca D. Dragan,et al.  Information gathering actions over human internal state , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).