A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving
In mixed and dynamic traffic environments, accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety. In this paper, we propose an integrated probabili...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809922001412 |
| _version_ | 1811157900009144320 |
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| author | Jinxin Liu Yugong Luo Zhihua Zhong Keqiang Li Heye Huang Hui Xiong |
| author_facet | Jinxin Liu Yugong Luo Zhihua Zhong Keqiang Li Heye Huang Hui Xiong |
| author_sort | Jinxin Liu |
| collection | DOAJ |
| description | In mixed and dynamic traffic environments, accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety. In this paper, we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction, which consists of a driving inference model (DIM) and a trajectory prediction model (TPM). The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network. The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information. To further improve the prediction accuracy and realize uncertainty estimation, we develop a Gaussian process-based TPM, considering both the short-term prediction results of the vehicle model and the driving motion characteristics. Afterward, the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios. The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods. |
| first_indexed | 2024-04-10T05:15:19Z |
| format | Article |
| id | doaj.art-d051dc48552e423caf7b91de801d9061 |
| institution | Directory Open Access Journal |
| issn | 2095-8099 |
| language | English |
| last_indexed | 2024-04-10T05:15:19Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering |
| spelling | doaj.art-d051dc48552e423caf7b91de801d90612023-03-09T04:13:17ZengElsevierEngineering2095-80992022-12-0119228239A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous DrivingJinxin Liu0Yugong Luo1Zhihua Zhong2Keqiang Li3Heye Huang4Hui Xiong5State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaCorresponding author.; State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaIn mixed and dynamic traffic environments, accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety. In this paper, we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction, which consists of a driving inference model (DIM) and a trajectory prediction model (TPM). The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network. The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information. To further improve the prediction accuracy and realize uncertainty estimation, we develop a Gaussian process-based TPM, considering both the short-term prediction results of the vehicle model and the driving motion characteristics. Afterward, the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios. The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.http://www.sciencedirect.com/science/article/pii/S2095809922001412Autonomous drivingDynamic Bayesian networkDriving intention recognitionGaussian processVehicle trajectory prediction |
| spellingShingle | Jinxin Liu Yugong Luo Zhihua Zhong Keqiang Li Heye Huang Hui Xiong A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving Engineering Autonomous driving Dynamic Bayesian network Driving intention recognition Gaussian process Vehicle trajectory prediction |
| title | A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving |
| title_full | A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving |
| title_fullStr | A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving |
| title_full_unstemmed | A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving |
| title_short | A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving |
| title_sort | probabilistic architecture of long term vehicle trajectory prediction for autonomous driving |
| topic | Autonomous driving Dynamic Bayesian network Driving intention recognition Gaussian process Vehicle trajectory prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2095809922001412 |
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