Advanced Multimodal Perception and Sensor Fusion for Smart Transportation Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 48
Editors
Interests: computer vision; machine learning; automated driving; AI-based system; environment perception; cooperative systems
Special Issues, Collections and Topics in MDPI journals
2. PICS-L Lab, COSYS Department, Université Gustave Eiffel, 25 Allée des Marronnier, 78000 Versailles, France
3. The International Associated Lab ICCAM (France-Australia), Université Gustave Eiffel, 25 Allée des Marronnier, 78000 Versailles, France
Interests: automated driving; multi-sensor data fusion; cooperative systems; environment perception; extended perception; sensors simulation for ADAS prototyping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to provide a comprehensive overview of recent advances in multimodal sensing, perception, and sensor fusion technologies for smart transportation systems. As intelligent and autonomous mobility solutions continue to rapidly evolve, multi-sensor systems have emerged as a critical foundation for accurate environmental perception, localization, mapping, and decision-making in complex and dynamic traffic scenarios. The growing deployment of connected, automated, and cooperative mobility systems further highlights the vital role of integrated sensing modalities in enhancing vehicular safety, reliability, and operational efficiency.
While individual sensing technologies—such as LiDAR, RADAR, and vision-based cameras—offer significant advantages, they each face distinct challenges when operating in isolation. These include sensitivity to adverse weather conditions, sensor interference, occlusions, limited range, and the escalating complexity of large-scale data processing. Addressing these limitations requires innovative approaches that combine advanced hardware technologies with complementary sensing modalities, intelligent data processing techniques, and collaborative perception frameworks.
This Special Issue welcomes original research contributions addressing mainstream and emerging sensor technologies, novel sensing architectures, multimodal data processing methods, 3D/4D object detection and tracking, simultaneous localization and mapping (SLAM), sensor fusion algorithms, and real-time perception systems. Particular emphasis is placed on AI-driven and deep learning-based approaches that enhance the robustness, accuracy, and efficiency of perception systems across diverse transportation applications.
Contributions exploring the integration of vehicle-based sensing with physical and digital transportation infrastructure (PDI) are also highly encouraged. Topics of interest include infrastructure-assisted perception, vehicle-to-everything (V2X) communications, cooperative and distributed sensing, digital twins, intelligent roadside units, and collaborative perception among connected vehicles. Furthermore, the utilization of high-definition (HD) maps, semantic mapping, and dynamic environment modeling is considered essential for improving localization accuracy, situational awareness, and predictive decision-making capabilities.
Dr. Sio Song Ieng
Dr. Dominique Gruyer
Guest Editors
Manuscript Submission Information
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Keywords
- multimodal perception
- 3D perception
- point cloud processing
- sensor fusion
- autonomous driving
- cooperative perception
- intelligent transportation systems (ITSs)
- deep learning
- HD mapping and localization
- connected and automated vehicles (CAVs)
- V2X communications
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