Modeling drivers' behavior is believed to be essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. These models are handcrafted in a top-down software engineering process. Here we propose a machine-learning alternative by estimating stochastic driver models from behavior traces. They are more robust than their non-stochastic predecessors. In this paper we present a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) model for the longitudinal control of human drivers in an inner-city traffic scenario. It is learnt on the basis of multivariate time-series obtained in simulator studies. Percepts relevant for longitudinal control were included in the model by a structure-learning method using Bayesian information criteria. Besides mimicking human driver behavior we suggest using the model for prototyping intelligent assistance systems with human-like behavior.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Learning the human longitudinal control behavior with a modular hierarchical Bayesian Mixture-of-Behaviors model


    Contributors:
    Eilers, M. (author) / Mobus, C. (author)


    Publication date :

    2011-06-01


    Size :

    1132930 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Learning the Human Longitudinal Control Behavior with a Modular Hierarchical Bayesian Mixture-Of-Behaviors Model

    Eilers, M. / Mobus, C. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2011


    Mixture of behaviors in a Bayesian autonomous driver model

    Möbus, Claus / Eilers, Mark / Zilinski, Malte et al. | Tema Archive | 2009


    Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression

    Zhang, Chengyuan / Chen, Kehua / Zhu, Meixin et al. | IEEE | 2024


    A Bayesian Gaussian Mixture Model for Probabilistic Modeling of Car-Following Behaviors

    Chen, Xiaoxu / Zhang, Chengyuan / Cheng, Zhanhong et al. | IEEE | 2024


    Modular predictions for complex human behaviors

    NOY DOMINIC / ANGUS MATTHEW CAMERON / EVERARD JAMES OVER et al. | European Patent Office | 2023

    Free access