Context-aware decision making in autonomous vehicles: Integrating social behavior modeling with large language models

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2025-06-11 DOI:10.1016/j.array.2025.100420
Badri Raj Lamichhane, Aueaphum Aueawatthanaphisut, Gun Srijuntongsiri, Teerayut Horanont
{"title":"Context-aware decision making in autonomous vehicles: Integrating social behavior modeling with large language models","authors":"Badri Raj Lamichhane,&nbsp;Aueaphum Aueawatthanaphisut,&nbsp;Gun Srijuntongsiri,&nbsp;Teerayut Horanont","doi":"10.1016/j.array.2025.100420","DOIUrl":null,"url":null,"abstract":"<div><div>Integrating context-aware decision-making in autonomous vehicles (AVs) is critical for advancing operational efficiency, safety, and user experience. However, existing frameworks struggle to incorporate social context into real-time navigation, relying on deterministic algorithms or reinforcement learning models that overlook implicit social norms and face challenges in translating LLM-derived reasoning into safety-compliant control policies. This paper investigates the application of social behavior modeling fused with large language models (LLMs) to establish a comprehensive framework for context-aware understanding and decision-making processes by AVs. Through understanding of the scene and the deployment of LLMs, this framework enables AVs to interpret and respond to complex social interactions and contextual cues, enhancing adaptability in dynamic environments. We propose concepts and approaches to foster context-aware and socially responsible decision-making processes, including test cases for validation to some level. The results demonstrate substantial improvements in decision accuracy adopting virtual simulation, providing a foundation for addressing complex ethical dilemmas and real-time decision-making challenges that AVs encounter in diverse and dynamic settings.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100420"},"PeriodicalIF":4.5000,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Array","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590005625000475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Integrating context-aware decision-making in autonomous vehicles (AVs) is critical for advancing operational efficiency, safety, and user experience. However, existing frameworks struggle to incorporate social context into real-time navigation, relying on deterministic algorithms or reinforcement learning models that overlook implicit social norms and face challenges in translating LLM-derived reasoning into safety-compliant control policies. This paper investigates the application of social behavior modeling fused with large language models (LLMs) to establish a comprehensive framework for context-aware understanding and decision-making processes by AVs. Through understanding of the scene and the deployment of LLMs, this framework enables AVs to interpret and respond to complex social interactions and contextual cues, enhancing adaptability in dynamic environments. We propose concepts and approaches to foster context-aware and socially responsible decision-making processes, including test cases for validation to some level. The results demonstrate substantial improvements in decision accuracy adopting virtual simulation, providing a foundation for addressing complex ethical dilemmas and real-time decision-making challenges that AVs encounter in diverse and dynamic settings.
自动驾驶汽车中的情境感知决策:将社会行为建模与大型语言模型集成
在自动驾驶汽车(AVs)中集成环境感知决策对于提高运营效率、安全性和用户体验至关重要。然而,现有的框架很难将社会背景整合到实时导航中,依赖于确定性算法或强化学习模型,这些模型忽略了隐含的社会规范,并且在将llm衍生的推理转化为符合安全要求的控制策略方面面临挑战。本文研究了融合大语言模型(LLMs)的社会行为建模的应用,以建立自动驾驶汽车上下文感知理解和决策过程的综合框架。通过对场景的理解和llm的部署,该框架使自动驾驶汽车能够解释和响应复杂的社会互动和上下文线索,增强在动态环境中的适应性。我们提出了一些概念和方法来促进上下文感知和对社会负责的决策过程,包括在某种程度上进行验证的测试用例。结果表明,采用虚拟仿真可以显著提高决策精度,为解决自动驾驶汽车在不同动态环境中遇到的复杂伦理困境和实时决策挑战提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信