We propose a novel method called Short-/Long-term Hybrid Prediction (SLHP) that predicts short-term and long-term trajectories of surrounding objects while estimating their future influences on an autonomous ego-vehicle for both types of trajectories. Recently, long-term prediction methods based on trajectory sample generation and verification with road environments and/or interactions have been proposed; however, they entail high computational costs because they need to generate and verify multiple trajectory samples for multiple objects. Therefore, they are not appropriate in scenes where short-term prediction is required such as for sudden motions by surrounding objects. In contrast, our SLHP consists of a hybrid prediction based on short-term and long-term trajectory predictors. SLHP provides flexible predictions that are appropriate to scenes caused by objects' motions, road environments, interactions, and so on. In this paper, we apply and evaluate our method to cut-in prediction as a typical prediction task by using the public road dataset that includes various cut-in events. Experimental results show that SLHP achieves a correctness rate of F-measure = 0.86 for cut-in prediction. Additionally, we confirmed the effectiveness of our hybrid prediction method that provides prediction as early as 3.57 s and 4.82 s before the cut-in event for short-term and long-term trajectory predictions, respectively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    SLHP: Short-/Long-term Hybrid Prediction for Road Users


    Contributors:


    Publication date :

    2020-10-19


    Size :

    858426 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SLHP: SHORT-/LONG-TERM HYBRID PREDICTION FOR ROAD USERS

    Takei, Shoichi / Tanaka, Shinya / Yamaguchi, Shotaro et al. | British Library Conference Proceedings | 2020


    Ontology-based Reasoning Approach for Long-term Behavior Prediction of Road Users

    Fang, Fang / Yamaguchi, Shotaro / Khiat, Abdelaziz | IEEE | 2019


    Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users

    Saleh, Khaled / Hossny, Mohammed / Nahavandi, Saeid | IEEE | 2020


    Road section traffic speed short-term prediction method in view of long-term equilibrium relationship

    XIA JINGXIN / NIE QINGHUI / QIAN ZHENDONG et al. | European Patent Office | 2015

    Free access

    Movement Prediction for Road Users

    JANJOS FARIS / DOLGOV MAXIM | European Patent Office | 2024

    Free access