The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform

Yaser P Fallah - One of the best experts on this subject based on the ideXlab platform.

  • a driver behavior modeling structure based on non parametric bayesian Stochastic Hybrid architecture
    Vehicular Technology Conference, 2018
    Co-Authors: Hossein Nourkhiz Mahjoub, Behrad Toghi, Yaser P Fallah
    Abstract:

    Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation Systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical Stochastic Hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a Stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different Stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from the NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

  • a driver behavior modeling structure based on non parametric bayesian Stochastic Hybrid architecture
    arXiv: Signal Processing, 2018
    Co-Authors: Hossein Nourkhiz Mahjoub, Behrad Toghi, Yaser P Fallah
    Abstract:

    Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation Systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical Stochastic Hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a Stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different Stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

Hossein Nourkhiz Mahjoub - One of the best experts on this subject based on the ideXlab platform.

  • a driver behavior modeling structure based on non parametric bayesian Stochastic Hybrid architecture
    Vehicular Technology Conference, 2018
    Co-Authors: Hossein Nourkhiz Mahjoub, Behrad Toghi, Yaser P Fallah
    Abstract:

    Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation Systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical Stochastic Hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a Stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different Stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from the NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

  • a driver behavior modeling structure based on non parametric bayesian Stochastic Hybrid architecture
    arXiv: Signal Processing, 2018
    Co-Authors: Hossein Nourkhiz Mahjoub, Behrad Toghi, Yaser P Fallah
    Abstract:

    Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation Systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical Stochastic Hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a Stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different Stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

Behrad Toghi - One of the best experts on this subject based on the ideXlab platform.

  • a driver behavior modeling structure based on non parametric bayesian Stochastic Hybrid architecture
    Vehicular Technology Conference, 2018
    Co-Authors: Hossein Nourkhiz Mahjoub, Behrad Toghi, Yaser P Fallah
    Abstract:

    Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation Systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical Stochastic Hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a Stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different Stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from the NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

  • a driver behavior modeling structure based on non parametric bayesian Stochastic Hybrid architecture
    arXiv: Signal Processing, 2018
    Co-Authors: Hossein Nourkhiz Mahjoub, Behrad Toghi, Yaser P Fallah
    Abstract:

    Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation Systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical Stochastic Hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a Stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different Stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

Marco Prandini - One of the best experts on this subject based on the ideXlab platform.

  • Energy Management of a Building Cooling System With Thermal Storage: An Approximate Dynamic Programming Solution
    IEEE Transactions on Automation Science and Engineering, 2017
    Co-Authors: Robert Vignali, Luigi Piroddi, Mihajlo Strelec, Franco Borghesan, Marco Prandini
    Abstract:

    This paper concerns the design of an energy management System for a building cooling System that includes a chiller plant (with two or more chiller units), a thermal storage unit, and a cooling load. The latter is modeled in a probabilistic framework to account for the uncertainty in the building occupancy. The energy management task essentially consists in the minimization of the energy consumption of the cooling System, while preserving comfort in the building. This is achieved by a twofold strategy. The cooling power request is optimally distributed among the chillers and the thermal storage unit. At the same time, a slight modulation of the temperature set-point of the zone is allowed, trading energy saving for comfort. The problem can be decoupled into a static optimization problem (mainly addressing the chiller plant optimization) and a dynamic programming (DP) problem for a discrete time Stochastic Hybrid System (SHS) that takes care of the overall energy minimization. The DP problem is solved by abstracting the SHS to a (finite) controlled Markov chain, where costs associated with state transitions are computed by simulating the original model and determining the corresponding energy consumption. A numerical example shows the efficacy of the approach.

  • a self recovery approach to the probabilistic invariance problem for Stochastic Hybrid Systems
    Conference on Decision and Control, 2012
    Co-Authors: Marco Prandini, Luigi Piroddi
    Abstract:

    In this paper, we consider the problem of designing a feedback policy for a discrete time Stochastic Hybrid System that should be kept operating within some compact set A. To this purpose, we introduce an infinite-horizon discounted average reward function, where a negative reward is associated to the transitions driving the System outside A and a positive reward to those leading it back to A. The idea is that the stationary policy maximizing this reward function will keep the System within A as long as possible, and, if the System happens to exit A, it will bring it back to A as soon as possible, compatibly with the System dynamics. This self-recovery approach is particularly useful in those cases where it is not possible to maintain the System within A indefinitely. The performance of the resulting strategy is assessed on a benchmark example.

  • approximate model checking of Stochastic Hybrid Systems
    European Journal of Control, 2010
    Co-Authors: Alessandro Abate, Joostpieter Katoen, John Lygeros, Marco Prandini
    Abstract:

    A method for approximate model checking of Stochastic Hybrid Systems with provable approximation guarantees is proposed. We focus on the probabilistic invariance problem for discrete time Stochastic Hybrid Systems and propose a two-step scheme. The Stochastic Hybrid System is first approximated by a finite state Markov chain. The approximating chain is then model checked for probabilistic invariance. Under certain regularity conditions on the transition and reset kernels governing the dynamics of the Stochastic Hybrid System, the invariance probability computed using the approximating Markov chain is shown to converge to the invariance probability of the original Stochastic Hybrid System, as the grid used in the approximation gets finer. A bound on the convergence rate is also provided. The performance of the two-step approximate model checking procedure is assessed on a case study of a multi-room heating System.

Christos G Cassandras - One of the best experts on this subject based on the ideXlab platform.

  • infinitesimal perturbation analysis for quasi dynamic traffic light controllers
    arXiv: Optimization and Control, 2014
    Co-Authors: Julia L Fleck, Christos G Cassandras
    Abstract:

    We consider the traffic light control problem for a single intersection modeled as a Stochastic Hybrid System. We study a quasi-dynamic policy based on partial state information defined by detecting whether vehicle backlogs are above or below certain controllable thresholds. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimators of a cost metric with respect to these threshold parameters and use these estimators to iteratively adjust the threshold values through a standard gradient-based algorithm so as to improve overall System performance under various traffic conditions. Results obtained by applying this methodology to a simulated urban setting are also included.

  • multi intersection two way traffic light control with blocking using infinitesimal perturbation analysis
    International Conference on Control Applications, 2013
    Co-Authors: Yanfeng Geng, Christos G Cassandras
    Abstract:

    We address the traffic light control problem for multiple intersections in tandem by viewing it as a Stochastic Hybrid System and developing a Stochastic Flow Model (SFM) for it. Our model includes roads with two-way traffic and with finite vehicle capacity in-between intersections, which may lead to additional delays due to traffic blocking. Using Infinitesimal Perturbation Analysis (IPA), we derive on-line gradient estimates of an average traffic congestion metric with respect to the controllable green and red cycle lengths. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach.

  • traffic light control using infinitesimal perturbation analysis
    Conference on Decision and Control, 2012
    Co-Authors: Yanfeng Geng, Christos G Cassandras
    Abstract:

    We address the traffic light control problem for a single intersection by viewing it as a Stochastic Hybrid System and developing a Stochastic Flow Model (SFM) for it. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimates of a cost metric with respect to the controllable green and red cycle lengths. The IPA estimators obtained require counting traffic light switchings and estimating car flow rates only when specific events occur. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach.

  • perturbation analysis and resource contention games in multiclass Stochastic flow models
    IFAC Proceedings Volumes, 2009
    Co-Authors: Chen Yao, Christos G Cassandras
    Abstract:

    Abstract We present a Stochastic Hybrid System framework for Stochastic Flow Models (SFMs) with multiple classes and class-dependent performance objectives. Using Infinitesimal Perturbation Analysis (IPA) estimators for the derivatives of such objectives, we formulate and solve resource contention games that contrast System-centric and user-centric optimization in such SFMs.