Machine Learning and Remote Sensing for Improved Autonomous Driving
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".
Deadline for manuscript submissions: 28 September 2025 | Viewed by 113
Special Issue Editors
Interests: automotive radar; statistical signal processing; data fusion; integrated circuit design; wireless communication
Special Issues, Collections and Topics in MDPI journals
Interests: radar signal processing; radar system design; array signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; computational imaging; image processing and computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: MIMO radar; MIMO communication
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Autonomous driving stands at the forefront of the transformative technologies poised to revolutionize transportation safety, efficiency, and sustainability. However, despite significant advancements, critical challenges persist in perception, decision-making, and real-time adaptability across diverse and dynamic environments. Remote sensing technologies—such as automotive radar, LiDAR, hyperspectral imaging, and high-resolution satellite/airborne systems—have emerged as pivotal tools, offering complementary spatial, temporal, and spectral data to augment traditional onboard sensors (e.g., cameras). When integrated with machine learning (ML), these technologies enable robust solutions for object detection, scene understanding, and predictive analytics under complex scenarios, including adverse weather, urban clutter, and long-range perception.
The synergy of ML and remote sensing addresses critical gaps in autonomous systems, enhancing sensor fusion for redundancy, enabling large-scale environmental modeling, and improving algorithmic generalizability across geographic and operational domains. This intersection is vital for developing next-generation autonomous vehicles (AVs) capable of safe, scalable, and socially compliant navigation.
This Special Issue aligns with Remote Sensing' s focus on innovative methodologies for sensing observation and data analytics. It seeks to bridge ML advancements and remote sensing applications to advance AVs' perception, localization, and decision-making frameworks. Submissions should emphasize cutting-edge algorithms, novel sensor integration, and real-world validation, ensuring relevance in urban, rural, and mixed environments.
Contributions are invited on the following topics, including, but not limited to:
- Deep learning for multi-modal sensor fusion (radar, LiDAR, satellite imagery);
- Real-time object detection, tracking, and semantic segmentation using remote sensing data;
- ML-driven 3D environmental reconstruction and dynamic map generation;
- Transfer learning for cross-domain/sensor adaptability in AV systems;
- Uncertainty quantification and explainability in autonomous perception;
- Edge computing and lightweight ML architectures for onboard processing;
- Ethical AI and policy implications for ML-enabled autonomous navigation.
Article types: original research, and datasets with benchmarks.
Dr. Le Zheng
Dr. Peng Chen
Dr. Bihan Wen
Dr. Junhui Qian
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
- machine learning for autonomous driving
- multi-sensor fusion
- mmWave radar
- LiDAR and radar perception
- deep learning in remote sensing
- environmental modeling
- real-time object detection
- edge computing for AVs
- urban remote sensing
- autonomous vehicle navigation
- explainable AI in transportation
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