Driving intention recognition and trajectory prediction of moving vehicles are two important requirements of future advanced driver assistance systems (ADAS) for urban intersections. In this paper, we present a consistent framework for solving these two problems. The key idea is to model the spatio-temporal dependencies of traffic situations with a two-dimensional Gaussian process regression. With this representation the driving intention can be recognized by evaluating the data likelihood for each individual regression model. For the trajectory prediction purpose, we transform these regression models into the corresponding dynamical models and combine them with Unscented Kalman Filters (UKF) to overcome the non-linear issue. We evaluate our framework with data collected from real traffic scenarios and show that our approach can be used for recognition of different driving intentions and for long-term trajectory prediction of traffic situations occurring at urban intersections.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modelling of traffic situations at urban intersections with probabilistic non-parametric regression


    Contributors:
    Tran, Quan (author) / Firl, Jonas (author)


    Publication date :

    2013-06-01


    Size :

    1307256 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    MODELLING OF TRAFFIC SITUATIONS AT URBAN INTERSECTIONS WITH PROBABILISTIC NON-PARAMETRIC REGRESSION

    Tran, Q. / Firl, J. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2013


    Traffic performance at urban street intersections

    Greenshields, B.D. / Schapiro, D. / Ericksen, E.L. | Engineering Index Backfile | 1947


    Modelling of Traffic Hazards at Urban Intersections Using Potential Collision Areas

    Szczuraszek, Tomasz / Klusek, Radosław | Springer Verlag | 2017


    Modelling of traffic noise in the vicinity of urban road intersections

    Yadav, Adarsh / Mandhani, Jyoti / Parida, Manoranjan et al. | Elsevier | 2022


    Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

    Ahmad H. Alomari / Bara’ W. AL-Mistarehi / Al-Jammal A. Al-Jammal et al. | DOAJ | 2023

    Free access