Call for Papers: 

Special Issue on Foundation Models for Intelligent Control in Autonomous Driving Traffic Systems

Submission Deadline: 1 June 2026

Theme: "Foundation Models for Intelligent Control in Autonomous Driving Traffic Systems"

We are excited to announce a special call for papers for an upcoming issue of Communications in Transportation Research（COMMTR）that is dedicated to the emerging intersection of generative models and intelligent control in autonomous driving traffic systems. This issue aims to explore how Large Language Models (LLMs), Visual Language Models (VLMs), and other generative Artificial Intelligence (AI) paradigms are reshaping the landscape of intelligent transportation. The special issue will serve as a high-impact forum for presenting theoretical advancements, algorithmic innovations, and real-world applications that integrate AI-based decision intelligence with traffic control, vehicle coordination, and system optimization.

  1 Objective

 特刊简介

The objective of this special issue is to advance the frontier of intelligent autonomous driving and transportation systems through the integration and innovation of foundation models. By bridging control theory, machine learning, and transportation engineering, we aim to foster an inclusive and forward-looking research environment that encourages interdisciplinary breakthroughs. This includes research spanning knowledge–data-driven modeling, heterogeneous traffic coordination, digital-twin-enabled causal inference, and iterative self-learning frameworks—all contributing to the development of next-generation traffic intelligence powered by foundation models.

We envision this special issue as a cornerstone for building an international research community focused on foundation-model-driven autonomy in traffic systems. It will seek to promote sustained collaboration, open discourse, and continued innovation in this dynamically evolving field.

2 Submission Details

 投稿须知

Paper Submission Website  投稿网址 

https://mc03.manuscriptcentral.com/commtr 

 Submission Deadline  截止日期 

1 June 2026

 Expected Publication Date  出版日期

28 August 2026

 3 Scope

特刊主题

We welcome submissions that address, but are not limited to, the following topics:

Foundation model-based control and decision-making in autonomous driving

LLMs and multimodal foundation models for traffic perception, prediction, and planning

Physics-aware and safety-constrained foundation models for vehicle autonomy

Cooperative vehicle–infrastructure–cloud intelligence frameworks enabled by foundation models

Transformer, diffusion, and graph-based models for trajectory generation and policy optimization

Multi-agent foundation models for emergent traffic flow regulation and coordination

Digital twin-driven simulation, adaptation, and evaluation using foundation models

Safety, robustness, interpretability, and verification of foundation-model-based autonomous systems

Benchmarking, dataset creation, and evaluation protocols for foundation models in driving and traffic systems

 4 Submission Guidelines

投稿指南

Manuscripts should be original and not published elsewhere.

All submissions will undergo a rigorous peer-review process based on merit and quality of research.

Papers should be submitted through the journal's online submission system, clearly indicating that they are intended for the " Foundation Models for Intelligent Control in Autonomous Driving Traffic Systems" special issue. This is to ensure that your submission will be considered for this special issue instead of being handled as a regular paper.

Your paper can be submitted via https://mc03.manuscriptcentral.com/commtr.

Author Guidelines and Manuscript Template: 

https://www.sciopen.com/journal/join_journal/submission_guidelines?id=1498119308628897794&issn=2097-5023

 5 Special Submission Requirements

投稿要求

For Research Articles, Replication and data sharing are mandatory, with data serving as strong evidence and adhering to the "Data and Code Policy" of COMMTR. Please submit your replication package and explanatory file to ETS-Data (https://ets-data.sciopen.com), our designated repository indexed in the Data Citation Index.

Submissions must explicitly be relevant to the integration of generative modeling techniques with autonomous driving traffic systems.

Submission Deadline: 1 June 2026

Expected Publication Date: 28 August 2026

 6 Guest Editors

客座编辑

Prof. Zhiyong Cui，Professor 

Beihang University, China

Email:  zhiyongc@buaa.edu.cn

Dr. Qixiu Cheng，Senior Lecturer 

University of Bristol, UK

Email: qixiu.cheng@bristol.ac.uk

Dr. Zhenning Li, Assistant Professor

University of Macau, China

Email:  zhenningli@um.edu.mo

Prof. Daxin Tian, Professor

Beihang University, China

E-mail: dtian@buaa.edu.cn

Prof. Haiyang Yu, Professor

State Key Lab of ITS, Beihang University, China

E-mail: hyyu@buaa.edu.cn

Prof. Hwasoo Yeo, Professor

Korea Advanced Institute of Science and Technology (KAIST), Republic  of Korea

E-mail: hwasoo@kaist.ac.kr

Prof. Mohammed Quddus, Professor

Imperial College London, UK

E-mail: m.quddus@imperial.ac.uk

点击阅读原文 进入投稿网站

重要通知

重要通知丨COMMTR新投审稿系统全新上线：欢迎作者访问新平台投稿

COMMTR亮点

• 中国大陆第三种SCIE/SSCI双收录期刊

• 中国大陆SCIE/SSCI双收录期刊中影响因子最高的期刊

• 中国大陆SSCI收录期刊中唯一一本学科排名第一的期刊（IF12.7，位居交通学科第一）

数据库收录

• SSCI、SCIE

• Scopus

• Ei CompendeX

• DOAJ

• TRlD, the TRlS and lTRD Database

• 期刊分区表：大类 工程技术大类1区Top期刊；小类 交通运输1区、运输科技1区

• 中国科技核心期刊CSTPCD

• 中国科学引文数据库CSCD

• 全球OA期刊索引（OA Journal Index，OAJ）

• 公路运输领域高质量科技期刊—T1级

• 《科技期刊世界影响力指数（WJCI）报告》“交通运输工程综合”学科排名2/107（国产期刊第一）

重要项目

• 入选“中国科技期刊卓越行动计划二期”英文梯队期刊项目

• 入选北京市2024年度支持高水平国际科技期刊建设-强刊提升项目

• 入选2022年中国科技期刊卓越行动计划高起点新刊项目

学术影响力

• 影响因子（2026年6月公布）达12.7，位居TRANSPORTATION（交通）学科全球66种期刊榜首

• 2025年CiteScore为13.6，在Control and Systems Engineering、Decision Sciences (miscellaneous)、Transportation三个领域全部进入前8%，均稳居Q1区

• 连续两年（2024年、2025年）入选“中国最具国际影响力学术期刊”（自然科学与工程技术·英文）

国际化程度

• 国际论文占比70%，吸引了国内外几乎所有交通领域顶级团队的投稿

• 论文被72个国家/地区作者引用，引用本刊最多的为Transportation Research Part E 等本领域顶刊，自引率仅3.6% 

• 国际下载量占89%，主要集中在美国

• 编委会汇聚全球20个国家的55位专家，其中69%来自海外，包括13位各国院士

• 作者来自全球21个国家和地区的顶尖院校，包括MIT、Stanford、UC Berkerley、Duke、清华大学、浙江大学、东南大学、北航、东京大学、新国大、港大、港科大等

编辑团队荣誉

• COMMTR主编荣获 “年度主编奖”

• 编辑团队成员荣获 “年度期刊新人”

COMMTR编委团队

曲小波

欧洲科学院（Academia Europaea）院士

清华大学车辆与运载学院长聘教授

曲小波，现任清华大学车辆与运载学院长聘教授、欧洲科学院（Academia Europaea）院士。全职归国前，曾任瑞典查尔姆斯理工大学讲席教授。过去五年主持科技部重点研发、基金委重点项目、欧盟、瑞典科技部、瑞典基金委等重大项目；担任9个行业内知名期刊的主编或副主编，发表论文180余篇，受邀担任欧委会人才项目、澳洲基金委卓越科学中心、荷兰基金委重大项目等高资助强度项目的初评或终评专家。清华大学（车辆国重）—杭州临空经济示范区综合交通联合研究中心主任；兼任中国人民财产保险公司独立董事。研究领域重点涉及低空运载工具开发与运营、车城互联系统建模与优化、智能交通系统运营与控制。

李小鹏

美国威斯康星大学麦迪逊分校教授

智能公路研究中心主任

李小鹏，现任美国威斯康星大学麦迪逊分校土木与环境工程系Harvey D. Spangler教授（电气与计算机工程系兼职教授）与智能公路研究中心（Smart Highway Research Center）主任。曾任美国国家级交通研究中心（National Institute for Congestion Reduction）主任。他于2006年获得清华大学土木工程学士学位（辅修计算机工程），此后在美国伊利诺伊大学香槟分校先后获得土木工程硕士（2007年）、应用数学硕士（2010年）及土木工程博士（2011年）学位。李小鹏教授是美国国家科学基金会杰出青年奖（NSF CAREER Award）获得者，美国土木工程协会（American Society of Civil Engineering）会士（Fellow），并在及多个专业协会担任职。已发表同行评议学术论文160余篇，主持了多项由美国国家科学基金会（NSF）、美国交通部（US DOT）、美国能源部（US DOE）、国家实验室、州交通部门及工业界资助的研究项目，累计经费包括匹配总额超过7000万美元。其主要研究方向为交通及相关系统中的自动化、互联与感知技术。

COMMTR推荐阅读

1 FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation

https://www.sciopen.com/article/10.1016/j.commtr.2025.100175 

Cite this article:

Lin G, Qian S, Khattak ZH. FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation. Communications in Transportation Research, 2025, 5(2): 100175. https://doi.org/10.1016/j.commtr.2025.100175

2 HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting

https://www.sciopen.com/article/10.1016/j.commtr.2025.100218 

Cite this article:

Shao Z, Wang F, Sun T, et al. HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting. Communications in Transportation Research, 2025, 5(4): 100218. https://doi.org/10.1016/j.commtr.2025.100218

3 Machine learning-based real-time crash risk forecasting for pedestrians

https://www.sciopen.com/article/10.1016/j.commtr.2025.100224 

Cite this article:

Hussain F, Li Y, Haque SMM. Machine learning-based real-time crash risk forecasting for pedestrians. Communications in Transportation Research, 2025, 5(4): 100224. https://doi.org/10.1016/j.commtr.2025.100224

4 Physical enhanced residual learning (PERL) framework for vehicle trajectory prediction

https://www.sciopen.com/article/10.1016/j.commtr.2025.100166 

Cite this article:

Long K, Sheng Z, Shi H, et al. Physical enhanced residual learning (PERL) framework for vehicle trajectory prediction. Communications in Transportation Research, 2025, 5(2): 100166. https://doi.org/10.1016/j.commtr.2025.100166

5 Few-shot learning for novel object detection in autonomous driving

https://www.sciopen.com/article/10.1016/j.commtr.2025.100194 

Cite this article:

Zhuang Y, Liu P, Yang H, et al. Few-shot learning for novel object detection in autonomous driving. Communications in Transportation Research, 2025, 5(3): 100194. https://doi.org/10.1016/j.commtr.2025.100194 

欢迎投稿

Communications in Transportation Research（COMMTR）发表论文类型包括Research article（研究型论文）、Review（综述）、Editorial（社论）、Discussion（讨论）等。

2027 年 12 月 31 日前录用的稿件仍免收文章处理费，研究论文终版超过 10 页或综述超过 15 页的部分将按每页 150 美元收取费用，请作者投稿时留意篇幅并按要求承担相关费用，以免影响后续流程。

发表范围：

发表对新兴交通系统具有重要意义的高质量、原创性研究和综述文章等，欢迎与交通有关的跨学科研究（交通与民用、控制、人工智能、社会科学、心理科学、医疗服务等）。

发表主题包括但不限于：

• 交通系统与其他系统之间的相互作用

• 新兴技术与交通系统的整合

• 交通领域的大数据分析

• 交通领域的进步与发现

• 对未来交通系统的前瞻性判断

• 新兴技术/产品及其对交通的影响

• 新型交通模式及相关分析

• 政府发起的交通政策及影响分析

• 移动性即服务

• 新兴交通技术的现场试验

• 交通电气化、自动化和互联互通

期待各位学者的来稿！

数据代码共享

平台网址：https://ets-data.sciopen.com 

COMMTR 是首批要求代码、数据共享的交通/车辆期刊之一。为进一步保证论文的学术质量，提升研究的可复现性和研究过程的透明度，同时增进数据的重复利用，本刊要求所有录用稿件提交“Replication Package”。同时鼓励作者将数据共享至ETS-Data交通数据共享平台(https://ets-data.sciopen.com)。

ETS-Data交通数据共享平台由清华大学出版社与清华大学车辆与运载学院共同设计搭建，是全球研究者可公开访问的交通类共享数据平台，提供包括数据、代码和其他结果复制等必要文件。所有文件均可在协议框架内免费下载，且每篇数据均拥有永久的DOI及CSTR标识符，可直接下载和引用。ETS-Data交通数据共享平台已于2022年被基于WOS的数据引文索引数据库DCI收录，成为该库收录的首个中国交通领域的科学数据共享平台；2023年被Google Dataset Search索引。

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