This study presents a cost-effective machine learning-based framework for predicting vehicle merging points on-ramps. Unlike previous deep learning-based methods, our model offers a practical solution that combines high accuracy with reasonable training and inference costs. Our framework detects vehicles from videos by a fixed camera using the YOLO v5 object detector, tracks vehicles by our newly designed original tracker, generates vehicle merging data, and predicts merging points using the Random Forest Regression (RFR) model. The model employs multivariate multiple regression to predict multiple decision-making points (DPs) along the on-ramp lane by considering the positions and velocities of the merging vehicle and its four neighboring vehicles. These DPs replicate the decision-making process of human drivers. We evaluate our approach on collected video data and compare the performance in terms of prediction accuracy and inference speed with a (shallow) bi-directional long short-term memory (Bi-LSTM) model. The results show that our model's RMSE from the human drivers' data is about 74% smaller than that of Bi-LSTM in our data setting, despite only a one-millisecond difference in inference time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Efficient On-Ramp Merging Point Prediction Using Machine Learning


    Contributors:


    Publication date :

    2024-09-24


    Size :

    7783238 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RAMP MERGING ASSISTANCE

    WANG ZIRAN / HAN KYUNGTAE / TIWARI PRASHANT | European Patent Office | 2021

    Free access

    Ramp merging assistance

    WANG ZIRAN / HAN KYUNGTAE / TIWARI PRASHANT | European Patent Office | 2022

    Free access

    Secure Ramp Merging using Blockchain

    Abdo, Ahmed / Wu, Guoyuan / Abu-Ghazaleh, Nael | IEEE | 2021


    Interactive on-ramp merging decision-making with motion prediction considering merging motivation

    Liu, Jinqiang / Zhao, Wanzhong / Wang, Chunyan et al. | SAGE Publications | 2024


    ANTI-JERK ON-RAMP MERGING USING DEEP REINFORCEMENT LEARNING

    Lin, Yuan / McPhee, John / Azad, Nasser L. | British Library Conference Proceedings | 2020