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Systems
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Learning-Empowered Autonomous Driving and Intelligent Transportation Systems
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Learning-Empowered Autonomous Driving and Intelligent Transportation Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 838

Special Issue Editors

Dr. Haoran Wang

E-Mail Website
Guest Editor
College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: end-to-end autonomous driving
Special Issues, Collections and Topics in MDPI journals
Dr. Jintao Lai

E-Mail Website
Guest Editor
College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: AV testing and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Heye Huang

E-Mail Website
Guest Editor
SMART Centre (Singapore), Massachusetts Institute of Technology, Singapore 138602, Singapore
Interests: generative models and human-centered AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jia Hu

E-Mail Website
Guest Editor
College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: vehicle–infrastructure cooperative decision-making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid integration of artificial intelligence (AI) into transportation systems is transforming the foundation of autonomous driving and mobility management. To foster interdisciplinary research at the intersection of learning, system intelligence, and transportation, we can announce a Special Issue on “Learning-Empowered Autonomous Driving and Intelligent Transportation Systems”.

Modern autonomous driving and transportation systems are evolving from rule-based architectures toward learning-empowered system intelligence—systems capable of perception, reasoning, coordination, and adaptation in complex, uncertain, and dynamic environments. These intelligent systems leverage advances in machine learning, reinforcement learning, world modeling, and large-scale simulation to enhance safety, efficiency, and decision robustness at the vehicle, infrastructure, and traffic levels.

This Special Issue will bring together researchers and practitioners from the domains of artificial intelligence, control, transportation, and systems engineering to explore the next generation of intelligent transportation systems empowered by learning and adaptation. We welcome contributions that advance theoretical foundations, algorithmic innovations, and system implementations for learning-driven autonomy in mobility.

Topics of interest include, but are not limited to, the following:

  • Learning-enabled system architectures for autonomous driving;
  • Multi-agent learning and coordination in transportation systems;
  • World models and simulation-driven policy learning;
  • Data-centric intelligence for perception, planning, and control;
  • Human–AI collaboration and co-driving intelligence;
  • Edge-cloud collaboration and large-scale system optimization;
  • Digital twins and system-level reinforcement learning for transportation;
  • Robustness, safety, and interpretability in learning-based driving systems;
  • AI for sustainable, resilient, and adaptive transportation networks;
  • Benchmarking, datasets, and evaluation methodologies for intelligent transportation.

We invite authors to submit high-quality research papers and reviews that contribute to the understanding and advancement of learning-empowered system intelligence in autonomous driving and transportation. All submissions will undergo a rigorous peer-review process in accordance with the journal’s standards.

Join us in shaping the future of intelligent transportation through learning-driven system design and autonomy.

Dr. Haoran Wang
Dr. Jintao Lai
Dr. Heye Huang
Prof. Dr. Jia Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous driving
  • intelligent transportation system
  • artificial intelligence
  • imitation learning
  • reinforcement learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

23 pages, 1273 KB  
Article
A Physics-Informed Neural Network for Vehicle Trajectory Reconstruction in Cut-In Scenarios with Sparse and Noisy Observations
by Chenyi Xie, Yuan Zheng, Qingchao Liu, Jian Wang, Wenping Duan, Yu Tang and Bin Ran
Systems 2026, 14(5), 535; https://doi.org/10.3390/systems14050535 - 8 May 2026
Viewed by 449
Abstract
Accurate trajectory data are fundamental to traffic modeling and autonomous vehicle development. However, reconstructing trajectories in cut-in scenarios is challenging due to complex multi-vehicle interactions and frequently sparse, noisy observations. Existing model-based methods require extensive parameter tuning, while purely data-driven methods depend on [...] Read more.
Accurate trajectory data are fundamental to traffic modeling and autonomous vehicle development. However, reconstructing trajectories in cut-in scenarios is challenging due to complex multi-vehicle interactions and frequently sparse, noisy observations. Existing model-based methods require extensive parameter tuning, while purely data-driven methods depend on densely labeled trajectory datasets and may violate physical consistency. To address these limitations, this paper proposes CI-PINN (cut-in physics-informed neural network), a self-supervised framework for trajectory reconstruction under severe data degradation. By integrating a longitudinal interaction model that captures anticipation and relaxation behaviors, CI-PINN ensures kinematic plausibility by jointly minimizing data-fitting and physics residual losses. Experiments on the NGSIM dataset demonstrate robust performance across missing rates of 80–90%, achieving a mean absolute error of 0.91 m and a mean squared error of 2.17 m2, which are 63.2% and 78.1% lower than the best baseline method, respectively. These results demonstrate a label-efficient and physically consistent framework for trajectory reconstruction in cut-in scenarios. Beyond improving microscopic trajectory fidelity, the proposed method preserves system-level traffic metrics more reliably, facilitating more accurate safety assessments and intelligent transportation applications. Full article
(This article belongs to the Special Issue Learning-Empowered Autonomous Driving and Intelligent Transportation Systems)
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