Learning-Empowered Autonomous Driving and Intelligent Transportation Systems

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.

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