SafeDrive: Knowledge- and data-driven risk-sensitive decision-making for autonomous vehicles with Large Language Models Article Swipe
Zhiyuan Zhou
,
Heye Huang
,
Boqi Li
,
Shiyue Zhao
,
Yao Mu
,
Jianqiang Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.aap.2025.108299
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.aap.2025.108299
Recent advancements in autonomous vehicles (AVs) leverage Large Language Models (LLMs) to perform well in normal driving scenarios. However, ensuring safety in dynamic, high-risk environments and managing safety-critical long-tail events remains a significant challenge. To address these issues, we propose SafeDrive, a knowledge- and data-driven risk-sensitive decision-making framework, to enhance AV safety and adaptability. The proposed framework introduces a modular system comprising: (1) a Risk Module for comprehensive quantification of multi-factor coupled risks involving driver, vehicle, and road interactions; (2) a Memory Module for storing and retrieving typical scenarios to improve adaptability; (3) a LLM-powered Reasoning Module for context-aware safety decision-making; and (4) a Reflection Module for refining decisions through iterative learning. By integrating knowledge-driven insights with adaptive learning mechanisms, the framework ensures robust decision-making under uncertain conditions. Extensive evaluations on real-world traffic datasets characterized by dynamic and high-risk scenarios, including highways (HighD), intersections (InD), and roundabouts (RounD), validate the framework's ability to enhance decision-making safety (achieving a 100% safety rate), replicate human-like driving behaviors (with decision alignment exceeding 85%), and adapt effectively to unpredictable scenarios. The proposed framework of SafeDrive establishes a novel paradigm for integrating knowledge- and data-driven methods, highlighting significant potential to improve the safety and adaptability of autonomous driving in long-tail or high-risk traffic scenarios. Project page: https://mezzi33.github.io/SafeDrive/.
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- 18
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https://openalex.org/W4415664683Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.aap.2025.108299Digital Object Identifier
- Title
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SafeDrive: Knowledge- and data-driven risk-sensitive decision-making for autonomous vehicles with Large Language ModelsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-29Full publication date if available
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Zhiyuan Zhou, Heye Huang, Boqi Li, Shiyue Zhao, Yao Mu, Jianqiang WangList of authors in order
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https://doi.org/10.1016/j.aap.2025.108299Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.aap.2025.108299Direct OA link when available
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18Number of works referenced by this work
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| abstract_inverted_index.potential | 193 |
| abstract_inverted_index.replicate | 161 |
| abstract_inverted_index.scenarios | 88 |
| abstract_inverted_index.uncertain | 126 |
| abstract_inverted_index.(achieving | 156 |
| abstract_inverted_index.Reflection | 104 |
| abstract_inverted_index.SafeDrive, | 40 |
| abstract_inverted_index.autonomous | 3, 201 |
| abstract_inverted_index.challenge. | 33 |
| abstract_inverted_index.framework, | 47 |
| abstract_inverted_index.human-like | 162 |
| abstract_inverted_index.introduces | 57 |
| abstract_inverted_index.knowledge- | 42, 187 |
| abstract_inverted_index.real-world | 131 |
| abstract_inverted_index.retrieving | 86 |
| abstract_inverted_index.scenarios, | 139 |
| abstract_inverted_index.scenarios. | 17, 175, 208 |
| abstract_inverted_index.LLM-powered | 94 |
| abstract_inverted_index.comprising: | 61 |
| abstract_inverted_index.conditions. | 127 |
| abstract_inverted_index.data-driven | 44, 189 |
| abstract_inverted_index.effectively | 172 |
| abstract_inverted_index.establishes | 181 |
| abstract_inverted_index.evaluations | 129 |
| abstract_inverted_index.framework's | 150 |
| abstract_inverted_index.integrating | 113, 186 |
| abstract_inverted_index.mechanisms, | 119 |
| abstract_inverted_index.roundabouts | 146 |
| abstract_inverted_index.significant | 32, 192 |
| abstract_inverted_index.adaptability | 199 |
| abstract_inverted_index.advancements | 1 |
| abstract_inverted_index.environments | 24 |
| abstract_inverted_index.highlighting | 191 |
| abstract_inverted_index.multi-factor | 70 |
| abstract_inverted_index.adaptability. | 53 |
| abstract_inverted_index.adaptability; | 91 |
| abstract_inverted_index.characterized | 134 |
| abstract_inverted_index.comprehensive | 67 |
| abstract_inverted_index.context-aware | 98 |
| abstract_inverted_index.interactions; | 78 |
| abstract_inverted_index.intersections | 143 |
| abstract_inverted_index.unpredictable | 174 |
| abstract_inverted_index.quantification | 68 |
| abstract_inverted_index.risk-sensitive | 45 |
| abstract_inverted_index.decision-making | 46, 124, 154 |
| abstract_inverted_index.safety-critical | 27 |
| abstract_inverted_index.decision-making; | 100 |
| abstract_inverted_index.knowledge-driven | 114 |
| abstract_inverted_index.https://mezzi33.github.io/SafeDrive/. | 211 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |