Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction
Innovative technologies and naturalistic driving data sources provide a great potential to develop reliable autonomous driving systems. Understanding the behaviors of surrounding vehicles is essential for improving safety and mobility of autonomous vehicles. Onboard sensors like Radar, Lidar and Cam...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9043491/ |
| _version_ | 1818379495324778496 |
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| author | Abdelmoudjib Benterki Moussa Boukhnifer Vincent Judalet Choubeila Maaoui |
| author_facet | Abdelmoudjib Benterki Moussa Boukhnifer Vincent Judalet Choubeila Maaoui |
| author_sort | Abdelmoudjib Benterki |
| collection | DOAJ |
| description | Innovative technologies and naturalistic driving data sources provide a great potential to develop reliable autonomous driving systems. Understanding the behaviors of surrounding vehicles is essential for improving safety and mobility of autonomous vehicles. Onboard sensors like Radar, Lidar and Camera are able to track surrounding vehicles motion and to get different features like position, velocity and yaw. This paper proposes a hybrid approach to integrate maneuver classification using neural networks and trajectory prediction using Long Short-term Memory (LSTM) networks to get the future positions of adjacent vehicles. In this study we use the Next Generation Simulation (NGSIM) public dataset that provides a real driving data. The proposed approach is validated experimentally using VEDECOM demonstrator data. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 2.2 seconds in advance. The Root Mean Square (RMS) errors of lateral and longitudinal positions are 0.30 m and 3.1 m respectively. The results demonstrate a high performance compared to various existing methods. |
| first_indexed | 2024-12-14T02:03:42Z |
| format | Article |
| id | doaj.art-34fe2daec43a45dabbd4306eeb9d5d77 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:03:42Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-34fe2daec43a45dabbd4306eeb9d5d772022-12-21T23:20:56ZengIEEEIEEE Access2169-35362020-01-018569925700210.1109/ACCESS.2020.29821709043491Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory PredictionAbdelmoudjib Benterki0https://orcid.org/0000-0002-8182-8592Moussa Boukhnifer1https://orcid.org/0000-0002-1729-5453Vincent Judalet2Choubeila Maaoui3Department of Autonomous and Connected Vehicles, Institut VEDECOM, Versailles, FranceUniversité de Lorraine, LCOMS, Metz, FranceDepartment of Autonomous and Connected Vehicles, Institut VEDECOM, Versailles, FranceUniversité de Lorraine, LCOMS, Metz, FranceInnovative technologies and naturalistic driving data sources provide a great potential to develop reliable autonomous driving systems. Understanding the behaviors of surrounding vehicles is essential for improving safety and mobility of autonomous vehicles. Onboard sensors like Radar, Lidar and Camera are able to track surrounding vehicles motion and to get different features like position, velocity and yaw. This paper proposes a hybrid approach to integrate maneuver classification using neural networks and trajectory prediction using Long Short-term Memory (LSTM) networks to get the future positions of adjacent vehicles. In this study we use the Next Generation Simulation (NGSIM) public dataset that provides a real driving data. The proposed approach is validated experimentally using VEDECOM demonstrator data. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 2.2 seconds in advance. The Root Mean Square (RMS) errors of lateral and longitudinal positions are 0.30 m and 3.1 m respectively. The results demonstrate a high performance compared to various existing methods.https://ieeexplore.ieee.org/document/9043491/Artificial intelligenceautonomous vehicleintention predictionLSTMmaneuver classificationneural networks |
| spellingShingle | Abdelmoudjib Benterki Moussa Boukhnifer Vincent Judalet Choubeila Maaoui Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction IEEE Access Artificial intelligence autonomous vehicle intention prediction LSTM maneuver classification neural networks |
| title | Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction |
| title_full | Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction |
| title_fullStr | Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction |
| title_full_unstemmed | Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction |
| title_short | Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction |
| title_sort | artificial intelligence for vehicle behavior anticipation hybrid approach based on maneuver classification and trajectory prediction |
| topic | Artificial intelligence autonomous vehicle intention prediction LSTM maneuver classification neural networks |
| url | https://ieeexplore.ieee.org/document/9043491/ |
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