To improve both efficiency and safety of automatic driving in complex traffic scenarios, autonomous vehicles need to have the ability to predict the future trajectories of vehicles. The interaction among vehicles in real traffic scenarios makes the trajectory prediction of vehicles challenging. By utilizing the historical trajectory of the targeted vehicle and the surrounding environment information, this paper proposes a coupling LSTM model in order to effectively predict the future trajectories of vehicles. The proposed model predicts the observable motion intentions of vehicles, and builds grids for the targeted vehicles to extract the implied space and intention information of the neighboring vehicles. Experiment results verify the proposed model in contrast to other basic models in real traffic scenarios.


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    Title :

    Intelligent Prediction of Mobile Vehicle Trajectory Based on Space-Time Information


    Contributors:
    Guan, Dong (author) / Zhao, Hui (author) / Zhao, Long (author) / Zheng, Kan (author)


    Publication date :

    2019-04-01


    Size :

    1003285 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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