Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912458112761856 |
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| author | Han, Zeyu Cai, Mengchi Chen, Chaoyi Meng, Qingwen Wang, Guangwei Liu, Ying Xu, Qing Wang, Jianqiang Li, Keqiang |
| author_facet | Han, Zeyu Cai, Mengchi Chen, Chaoyi Meng, Qingwen Wang, Guangwei Liu, Ying Xu, Qing Wang, Jianqiang Li, Keqiang |
| contents | The safe trajectory planning of intelligent and connected vehicles is a key component in autonomous driving technology. Modeling the environment risk information by field is a promising and effective approach for safe trajectory planning. However, existing risk assessment theories only analyze the risk by current information, ignoring future prediction. This paper proposes a predictive risk analysis and safe trajectory planning framework for intelligent and connected vehicles. This framework first predicts future trajectories of objects by a local risk-aware algorithm, following with a spatiotemporal-discretised predictive risk analysis using the prediction results. Then the safe trajectory is generated based on the predictive risk analysis. Finally, simulation and vehicle experiments confirm the efficacy and real-time practicability of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles Han, Zeyu Cai, Mengchi Chen, Chaoyi Meng, Qingwen Wang, Guangwei Liu, Ying Xu, Qing Wang, Jianqiang Li, Keqiang Robotics The safe trajectory planning of intelligent and connected vehicles is a key component in autonomous driving technology. Modeling the environment risk information by field is a promising and effective approach for safe trajectory planning. However, existing risk assessment theories only analyze the risk by current information, ignoring future prediction. This paper proposes a predictive risk analysis and safe trajectory planning framework for intelligent and connected vehicles. This framework first predicts future trajectories of objects by a local risk-aware algorithm, following with a spatiotemporal-discretised predictive risk analysis using the prediction results. Then the safe trajectory is generated based on the predictive risk analysis. Finally, simulation and vehicle experiments confirm the efficacy and real-time practicability of our approach. |
| title | Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.23999 |