Lightweight Merging Point Prediction on Highway On-Ramps Using Regression Techniques

• Item type: Symposium(1)
• 公開日: 2024-06-19
• タイトル: Lightweight Merging Point Prediction on Highway On-Ramps Using Regression Techniques
• タイトル / 言語: en
• タイトル: Lightweight Merging Point Prediction on Highway On-Ramps Using Regression Techniques
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: 大阪大学大学院情報科学研究科
• 著者所属: 大阪大学大学院情報科学研究科
• 著者所属: 大阪大学大学院情報科学研究科
• 著者所属: 大阪大学大学院情報科学研究科
• 著者所属: オートネットワーク研究所 (AutoNetworks Technologies, Ltd.)
• 著者所属: オートネットワーク研究所 (AutoNetworks Technologies, Ltd.)
• 著者所属: オートネットワーク研究所 (AutoNetworks Technologies, Ltd.)
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): AutoNetworks Technologies, Ltd
• 著者所属(英): en
• 著者所属(英): AutoNetworks Technologies, Ltd
• 著者所属(英): en
• 著者所属(英): AutoNetworks Technologies, Ltd
• 著者名: Doyoon, Lee; 廣森, 聡仁; 高井, 峰生; 山口, 弘純; 西村, 友佑; 長村, 吉富; 竹嶋, 進
• 著者名(英): Doyoon, Lee; Akihito, Hiromori; Mineo, Takai; Hirozumi, Yamaguchi; Yusuke, Nishimura; Yoshihisa, Nagamura; Susumu, Takeshima
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this paper, we introduce a lightweight machine learning framework for forecasting the merging points of vehicles on on-ramps. Our model, in contrast to prior deep learning methods, provides a practical solution that balances high precision with acceptable training and inference expenses. Our system uses the YOLO v5 object detector to identify vehicles from video footage captured by a stationary camera, tracks vehicles using our newly developed original tracker, generates vehicle merging data, and employs Random Forest Regression (RFR) and eXtreme Gradient Boosting (XGBoost) to test the merging points prediction. The models use multivariate multiple regression to forecast several decision-making points along the on-ramp lane, taking into account the positions and velocities of the merging vehicle and its four neighboring vehicles. These DPs mimic the decision-making process of human drivers. We assess our method using collected video data and compare the root mean squared error (RMSE) and inference speed with a traditional Bi-LSTM model. For unseen data, our model using the XGBoost model not only performs with a lower RMSE score but also shows a faster inference speed compared to Bi-LSTM in our dataset by 76.46% and 76.47%, respectively.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this paper, we introduce a lightweight machine learning framework for forecasting the merging points of vehicles on on-ramps. Our model, in contrast to prior deep learning methods, provides a practical solution that balances high precision with acceptable training and inference expenses. Our system uses the YOLO v5 object detector to identify vehicles from video footage captured by a stationary camera, tracks vehicles using our newly developed original tracker, generates vehicle merging data, and employs Random Forest Regression (RFR) and eXtreme Gradient Boosting (XGBoost) to test the merging points prediction. The models use multivariate multiple regression to forecast several decision-making points along the on-ramp lane, taking into account the positions and velocities of the merging vehicle and its four neighboring vehicles. These DPs mimic the decision-making process of human drivers. We assess our method using collected video data and compare the root mean squared error (RMSE) and inference speed with a traditional Bi-LSTM model. For unseen data, our model using the XGBoost model not only performs with a lower RMSE score but also shows a faster inference speed compared to Bi-LSTM in our dataset by 76.46% and 76.47%, respectively.
• 書誌情報: マルチメディア，分散，協調とモバイルシンポジウム2024論文集; 巻 2024, p. 1500-1506, 発行日 2024-06-19
• 出版者 / 言語: ja
• 出版者: 情報処理学会