Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections
This paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surrounding vehicles, which are collected by sensors mounte...
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doaj-5e6b7a531ae4491082dac95ee31656342021-03-29T16:59:32ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132020-01-01121410.1109/OJITS.2020.29659698957421Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn IntersectionsYonghwan Jeong0https://orcid.org/0000-0001-9193-7349Seonwook Kim1https://orcid.org/0000-0003-1922-4269Kyongsu Yi2https://orcid.org/0000-0002-0484-9752Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaDepartment of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaDepartment of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaThis paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surrounding vehicles, which are collected by sensors mounted on an autonomous vehicle. Data on 484 vehicle trajectories were collected from real traffic situations at multi-lane turn intersections. 11,662 and 4,998 samples acquired from the vehicle trajectories were used to train and evaluate the networks, respectively. A motion planner based on Model Predictive Control (MPC) is designed to determine the longitudinal acceleration command based on the predicted states of surrounding vehicles. The future states of the subject vehicle derived by MPC is used as an input feature to reflect the interaction of subject and target vehicles in LSTM-RNN based motion predictor. The proposed algorithm was evaluated in terms of its accuracy and its effects on the motion planning algorithm based on the driving data sets. The improved prediction accuracy substantially increased safety by bounding the prediction error within the safety margin. The application results of the proposed predictor demonstrate the improved recognition timing of the preceding vehicle and the similarity of longitudinal acceleration with drivers.https://ieeexplore.ieee.org/document/8957421/Autonomous vehicleintersection driving datamotion predictionmachine learningrecurrent neural networklong short-term memory |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yonghwan Jeong Seonwook Kim Kyongsu Yi |
spellingShingle |
Yonghwan Jeong Seonwook Kim Kyongsu Yi Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections IEEE Open Journal of Intelligent Transportation Systems Autonomous vehicle intersection driving data motion prediction machine learning recurrent neural network long short-term memory |
author_facet |
Yonghwan Jeong Seonwook Kim Kyongsu Yi |
author_sort |
Yonghwan Jeong |
title |
Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections |
title_short |
Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections |
title_full |
Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections |
title_fullStr |
Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections |
title_full_unstemmed |
Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections |
title_sort |
surround vehicle motion prediction using lstm-rnn for motion planning of autonomous vehicles at multi-lane turn intersections |
publisher |
IEEE |
series |
IEEE Open Journal of Intelligent Transportation Systems |
issn |
2687-7813 |
publishDate |
2020-01-01 |
description |
This paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surrounding vehicles, which are collected by sensors mounted on an autonomous vehicle. Data on 484 vehicle trajectories were collected from real traffic situations at multi-lane turn intersections. 11,662 and 4,998 samples acquired from the vehicle trajectories were used to train and evaluate the networks, respectively. A motion planner based on Model Predictive Control (MPC) is designed to determine the longitudinal acceleration command based on the predicted states of surrounding vehicles. The future states of the subject vehicle derived by MPC is used as an input feature to reflect the interaction of subject and target vehicles in LSTM-RNN based motion predictor. The proposed algorithm was evaluated in terms of its accuracy and its effects on the motion planning algorithm based on the driving data sets. The improved prediction accuracy substantially increased safety by bounding the prediction error within the safety margin. The application results of the proposed predictor demonstrate the improved recognition timing of the preceding vehicle and the similarity of longitudinal acceleration with drivers. |
topic |
Autonomous vehicle intersection driving data motion prediction machine learning recurrent neural network long short-term memory |
url |
https://ieeexplore.ieee.org/document/8957421/ |
work_keys_str_mv |
AT yonghwanjeong surroundvehiclemotionpredictionusinglstmrnnformotionplanningofautonomousvehiclesatmultilaneturnintersections AT seonwookkim surroundvehiclemotionpredictionusinglstmrnnformotionplanningofautonomousvehiclesatmultilaneturnintersections AT kyongsuyi surroundvehiclemotionpredictionusinglstmrnnformotionplanningofautonomousvehiclesatmultilaneturnintersections |
_version_ |
1724198417733779456 |