In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approaches aimed to predict multiple vehicles, they either predicted each vehicle independently with no considerations on possible interaction with surrounding entities or generated discretized joint motions which cannot be directly used in decision making and motion planning for autonomous vehicle. In this paper, we present a probabilistic framework that is able to jointly predict continuous motions for multiple interacting road participants under any driving scenarios and is capable of forecasting the duration of each interaction, which can enhance the prediction performance and efficiency. The proposed traffic scene prediction framework contains two hierarchical modules: the upper module and the lower module. The upper module forecasts the intention of the predicted vehicle, while the lower module predicts motions for interacting scene entities. An exemplar real-world scenario is used to implement and examine the proposed framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Framework for Probabilistic Generic Traffic Scene Prediction


    Contributors:


    Publication date :

    2018-11-01


    Size :

    1040565 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    PROBABILISTIC SITUATION ASSESSMENT FRAMEWORK FOR MULTIPLE, INTERACTING TRAFFIC PARTICIPANTS IN GENERIC TRAFFIC SCENES

    Klingelschmitt, Stefan / Damerow, Florian / Willert, Volker et al. | British Library Conference Proceedings | 2016



    Probabilistic ship behavior prediction using generic models

    Thind, Navreet S. / Soffker, Dirk | IEEE | 2022


    A Probabilistic Framework for Trajectory Prediction in Traffic Utilizing Driver Characterization

    Gill, Jasprit Singh / Pisu, Pierluigi / Schmid, Matthias J. | IEEE | 2019


    A Probabilistic Framework for Microscopic Traffic Propagation

    Wheeler, Tim A. / Robbel, Philipp / Kochenderfer, Mykel J. | IEEE | 2015