Deep Learning Sensor Fusion for Human–Machine Interaction in Intelligent Transportation Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 15 May 2026 | Viewed by 685
Special Issue Editors
Interests: driver assistance systems; human system interaction; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; human–machine interfaces; automotive control systems
Special Issue Information
Dear Colleagues,
The application of deep learning-driven human–machine interaction (HMI) in intelligent transportation systems (ITSs) facilitates smarter and safer transportation. Deep learning models can analyze multimodal data from human, environment, and vehicle systems, providing accurate recognition of human behavior, driver intention, and environmental factors and enabling the identification of the human–machine interaction relationship in an ITS. This will grant the creators of autonomous vehicles, traffic control systems, and personal devices an improved understanding of human behavior, improving the relevant decision-making processes and optimizing traffic flow. Furthermore, deep learning-driven HMI can improve safety by predicting accidents or near misses and providing timely interventions through automated alerts or corrective actions, enhancing users’ experience, acceptance, and trust.
This Special Issue invites researchers, academicians, and industry practitioners to contribute to the discourse on “Deep Learning Sensor Fusion for Human–Machine Interaction in Intelligent Transportation Systems”. Our aim is to compile state-of-the-art research contributing to advancements in deep learning approaches for HMI in ITSs.
Dr. Zheng Wang
Dr. Edric John Cruz Nacpil
Dr. Fei-Xiang Xu
Guest Editors
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Keywords
- deep learning-driven human–machine interaction
- advanced control algorithms for ITSs
- human–machine shared control
- multimodal driver state perception and intention cognition
- explainable and trustworthy deep learning models
- sensor fusion and signal processing in ITSs
- sensing for human behavior recognition in ITSs
- advanced analytics and predictive modeling for ITSs
- deep learning-driven interaction among vehicles in ITSs
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