Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning
With increasing public concern about autonomous vehicles, there is a growing demand for developing explainable autonomous driving planning technology. Traditional risk field methods use handcrafted potential field models to explain driving risks in a scenario. When explaining highly interactive scen...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Communications in Transportation Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772424725000010 |
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| _version_ | 1832583044324655104 |
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| author | Qiyuan Liu Jiawei Zhang Jingwei Ge Cheng Chang Zhiheng Li Shen Li Li Li |
| author_facet | Qiyuan Liu Jiawei Zhang Jingwei Ge Cheng Chang Zhiheng Li Shen Li Li Li |
| author_sort | Qiyuan Liu |
| collection | DOAJ |
| description | With increasing public concern about autonomous vehicles, there is a growing demand for developing explainable autonomous driving planning technology. Traditional risk field methods use handcrafted potential field models to explain driving risks in a scenario. When explaining highly interactive scenarios, such prior knowledge-based methods still lack flexibility, leading to insufficient interpretability. In this study, we first propose the concept of a risk map that can be seen as a discrete, ego vehicle's view form of the risk field. We then design an explainable trajectory planning framework that integrates risk maps with the candidate trajectory tree generated by trajectory prediction models. We further filter safe candidate trajectories from the tree on the basis of their cumulative risks in the risk maps and then select the optimal trajectory to execute by balancing other driving objectives. The validation results in various real-world scenarios demonstrate that our method can generate understandable risk maps and explain the risk differences between trajectories. Open-loop experiments show our model's advantages in terms of safety and efficiency for the trajectory planning task. An analysis of runtime demonstrated its potential for real-world applications. |
| format | Article |
| id | doaj-art-aee58ca21e1d469da97f641ef7e803f2 |
| institution | Kabale University |
| issn | 2772-4247 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Communications in Transportation Research |
| spelling | doaj-art-aee58ca21e1d469da97f641ef7e803f22025-01-29T05:02:44ZengElsevierCommunications in Transportation Research2772-42472025-12-015100161Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planningQiyuan Liu0Jiawei Zhang1Jingwei Ge2Cheng Chang3Zhiheng Li4Shen Li5Li Li6Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, ChinaDepartment of Automation, Tsinghua University, Beijing, 100084, ChinaDepartment of Automation, Tsinghua University, Beijing, 100084, ChinaDepartment of Automation, Tsinghua University, Beijing, 100084, ChinaTsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China; Department of Automation, Tsinghua University, Beijing, 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing, 100084, China; Corresponding author.Department of Automation, Tsinghua University, Beijing, 100084, China; Corresponding author.With increasing public concern about autonomous vehicles, there is a growing demand for developing explainable autonomous driving planning technology. Traditional risk field methods use handcrafted potential field models to explain driving risks in a scenario. When explaining highly interactive scenarios, such prior knowledge-based methods still lack flexibility, leading to insufficient interpretability. In this study, we first propose the concept of a risk map that can be seen as a discrete, ego vehicle's view form of the risk field. We then design an explainable trajectory planning framework that integrates risk maps with the candidate trajectory tree generated by trajectory prediction models. We further filter safe candidate trajectories from the tree on the basis of their cumulative risks in the risk maps and then select the optimal trajectory to execute by balancing other driving objectives. The validation results in various real-world scenarios demonstrate that our method can generate understandable risk maps and explain the risk differences between trajectories. Open-loop experiments show our model's advantages in terms of safety and efficiency for the trajectory planning task. An analysis of runtime demonstrated its potential for real-world applications.http://www.sciencedirect.com/science/article/pii/S2772424725000010Risk mapTrajectory planningExplainable autonomous driving |
| spellingShingle | Qiyuan Liu Jiawei Zhang Jingwei Ge Cheng Chang Zhiheng Li Shen Li Li Li Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning Communications in Transportation Research Risk map Trajectory planning Explainable autonomous driving |
| title | Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning |
| title_full | Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning |
| title_fullStr | Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning |
| title_full_unstemmed | Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning |
| title_short | Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning |
| title_sort | integrating spatial temporal risk maps with candidate trajectory trees for explainable autonomous driving planning |
| topic | Risk map Trajectory planning Explainable autonomous driving |
| url | http://www.sciencedirect.com/science/article/pii/S2772424725000010 |
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