Integrating trajectory prediction to the decision-making and planning modules of modular autonomous driving systems is expected to improve the safety and efficiency of self-driving vehicles. However, a vehicle's future trajectory prediction is a challenging task since it is affected by the social interactive behaviors of neighboring vehicles, and the number of neighboring vehicles can vary in different situations. This work proposes a GNN-RNN based Encoder-Decoder network for interaction-aware trajectory prediction, where vehicles' dynamics features are extracted from their historical tracks using RNN, and the inter-vehicular interaction is represented by a directed graph and encoded using a GNN. The parallelism of GNN implies the proposed method's potential to predict multi-vehicular trajectories simultaneously. Evaluation on the dataset extracted from the NGSIM US-101 dataset shows that the proposed model is able to predict a target vehicle's trajectory in situations with a variable number of surrounding vehicles.


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    Title :

    Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving


    Contributors:
    Mo, Xiaoyu (author) / Xing, Yang (author) / Lv, Chen (author)


    Publication date :

    2021-09-19


    Size :

    1687178 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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