Numerous studies have been conducted to predict lane-change trajectories. The significant differences between cut-ins and other lane changes suggest the necessity of building specialized algorithms tailored to learning vehicle cut-ins. In this paper, we explore predicting the trajectory and velocity of the cut-in vehicles with a deep learning method. Particularly, we propose a prediction algorithm by combining a Transformer-based encoder and an LSTM-based decoder. The Transformer-based encoder is applied to capture features related to the driving context of the cut-in vehicle. The LSTM decoder is employed to predict the trajectory and velocity of the cut-in vehicles by considering their temporal and social relationships. We extracted the cut-in events from NGSIM dataset for algorithm evaluation. We compared the performance of the proposed algorithm and three other deep learning algorithms based on the extracted cut-in events. The results suggest that the proposed algorithm outperforms other algorithms in trajectory and velocity predictions of the cut-in vehicles. Moreover, we analyze the effect of the historical data window size on the prediction performance of the proposed algorithm.


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    Title :

    Trajectory and Velocity Prediction of Cut-In Vehicles With Deep Learning Method


    Contributors:
    Wang, Hanfeng (author) / Lu, Yun (author) / Su, Rong (author) / Luo, Ruikang (author) / Zhao, Nanbin (author) / De Boer, Niels (author) / Guan, Yong Liang (author)


    Publication date :

    2024-09-24


    Size :

    405533 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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