An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution
Comprehensive and accurate understanding of the interactive traffic environment facilitates reasonable motion planning for automated vehicles. This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted dr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10599622/ |
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| author | Zhiqiang Zhang Lei Zhang Mingqiang Wang Cong Wang Zhenpo Wang |
| author_facet | Zhiqiang Zhang Lei Zhang Mingqiang Wang Cong Wang Zhenpo Wang |
| author_sort | Zhiqiang Zhang |
| collection | DOAJ |
| description | Comprehensive and accurate understanding of the interactive traffic environment facilitates reasonable motion planning for automated vehicles. This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted driving behaviors of surrounding vehicles. A Social Temporal Convolutional Long Short-Term Memory network is constructed to capture the interactive characteristics among the host and surrounding vehicles and to predict the statistical distribution of the trajectory prediction uncertainty in the prediction horizon. Then a two-dimensional Gaussian distribution-based dynamic risk assessment with a soft update method is developed to spatially and temporally quantify the driving risk by constructing the occupancy map based on the multi-modal distribution of the predicted trajectories for the surrounding vehicles. The optimal motion of the host vehicle is determined by minimizing a multi-objective function of the alternative driving behaviors. The effectiveness of the proposed scheme is verified under typical driving scenarios extracted from the NGSIM dataset. The results show that the proposed method can comprehensively evaluate the potential risk and efficiently achieve motion planning while minimizing the driving risk. |
| format | Article |
| id | doaj-art-c7aab3081e1649ad928ff162cc0500c6 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-c7aab3081e1649ad928ff162cc0500c62025-01-30T00:04:15ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151386139910.1109/OJVT.2024.342864510599622An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction DistributionZhiqiang Zhang0Lei Zhang1https://orcid.org/0000-0002-1763-0397Mingqiang Wang2Cong Wang3Zhenpo Wang4https://orcid.org/0000-0002-1396-906XAdvanced Technology Research Institute, Beijing Institute of Technology, Jinan, ChinaAdvanced Technology Research Institute, Beijing Institute of Technology, Jinan, ChinaNational Engineering Research Center for Electric Vehicles, Beijing Institute of Technology, Beijing, ChinaSchool of Vehicle and Mobility, Tsinghua University, Beijing, ChinaAdvanced Technology Research Institute, Beijing Institute of Technology, Jinan, ChinaComprehensive and accurate understanding of the interactive traffic environment facilitates reasonable motion planning for automated vehicles. This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted driving behaviors of surrounding vehicles. A Social Temporal Convolutional Long Short-Term Memory network is constructed to capture the interactive characteristics among the host and surrounding vehicles and to predict the statistical distribution of the trajectory prediction uncertainty in the prediction horizon. Then a two-dimensional Gaussian distribution-based dynamic risk assessment with a soft update method is developed to spatially and temporally quantify the driving risk by constructing the occupancy map based on the multi-modal distribution of the predicted trajectories for the surrounding vehicles. The optimal motion of the host vehicle is determined by minimizing a multi-objective function of the alternative driving behaviors. The effectiveness of the proposed scheme is verified under typical driving scenarios extracted from the NGSIM dataset. The results show that the proposed method can comprehensively evaluate the potential risk and efficiently achieve motion planning while minimizing the driving risk.https://ieeexplore.ieee.org/document/10599622/Automated vehiclesrisk assessmentmotion planningpredictive occupancy map |
| spellingShingle | Zhiqiang Zhang Lei Zhang Mingqiang Wang Cong Wang Zhenpo Wang An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution IEEE Open Journal of Vehicular Technology Automated vehicles risk assessment motion planning predictive occupancy map |
| title | An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution |
| title_full | An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution |
| title_fullStr | An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution |
| title_full_unstemmed | An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution |
| title_short | An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution |
| title_sort | uncertainty aware lane change motion planning algorithm based on probabilistic trajectory prediction distribution |
| topic | Automated vehicles risk assessment motion planning predictive occupancy map |
| url | https://ieeexplore.ieee.org/document/10599622/ |
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