Today the automotive industry faces a robust trend toward assisted and automated driving. The technology to accomplish this ambition has evolved rapidly over the last few years, and yet there are still a lot of algorithmical challenges left to make an automation of the driving task a safe and comfortable experience. One of the main remaining challenges is the comprehension of the current traffic situation and the anticipation of all traffic participants’ future driving behavior, which is needed for the technical system to obtain situation awareness: an indispensable foundation for successful decision-making. In this paper, a prediction framework is presented that is able to infer a driver's maneuver intention. This is achieved via a hybrid Bayesian network whose hidden layers represent a driver's lane contentedness. A pre-training of the network's parameters with simulated data provides for human interpretable parameters even after running the expectation maximization algorithm based on data gathered on German highways. Moreover, the future driving path of any traffic participant is predicted by solving an optimal control problem, whereby the parameters of the optimal control formulation are found via inverse reinforcement learning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Lane Change Intention Awareness for Assisted and Automated Driving on Highways


    Contributors:

    Published in:

    Publication date :

    2019-06-01


    Size :

    1557068 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    LANE CHANGE PREDICTION ON HIGHWAYS

    MEHTA ASHISH BIREN / SURANA MONU / JOSHI AVDHUT et al. | European Patent Office | 2025

    Free access

    Lane Change Model for Automated Vehicles on Multi-Lane Highways in Mixed Traffic

    Hofinger, Felix / Mischinger-Rodziewicz, Marlies / Haberl, Michael et al. | IEEE | 2023


    Lane Change Intention Recognition Based on Natural Driving

    Hao, Yanjun / Dong, Hongxia / Zhao, Xiaojin et al. | ASCE | 2024