Advanced Sensing for Connected and Automated Mobilities
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 15 October 2024 | Viewed by 121
Special Issue Editors
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; multisensor data fusion; cooperative systems; environment perception; extended perception; sensors simulation for ADAS prototyping
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; AI based system; machine learning; automated driving; environment perception
Interests: artificial intelligence; sensor fusion; machine learning; computer vision with applications in unmanned vehicles, robotics, and industrial automation
Special Issues, Collections and Topics in MDPI journals
Interests: automated driving; cooperative systems; path planning; control theory; risk assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to comprehensively explore the recent advancements in Advanced Sensing Technologies crucial for the progression of autonomous driving, encompassing topics such as Physical and Digital Infrastructure, AI, Deep Learning, Cooperative Fusion, and HD Maps. As autonomous vehicle technologies evolve, the pivotal role of advanced sensing becomes increasingly apparent, particularly in enhancing safety, perception, and overall system reliability.
Addressing challenges posed by factors such as interferences, weather conditions, and the limited field of view in existing sensor technologies, the Special Issue seeks innovative solutions by integrating advanced sensors. Deploying sensors across different wave bands and with diverse characteristics is considered imperative for effectively tackling these challenges.
Research contributions are invited to delve into topics such as novel sensor technologies, advances in sensor and data processing, and embedded/distributed/collaborative architectures. Emphasis is placed not only on ensuring the accuracy and reliability of sensor data but also on fostering seamless cooperation between sensors for a comprehensive perception of the vehicle's surroundings.
Additionally, the Special Issue welcomes contributions exploring the intersection of Advanced Sensing Technologies with Physical and Digital Infrastructure, leveraging AI and Deep Learning for enhanced sensor capabilities. Cooperative Fusion strategies, promoting collaboration among sensors and vehicles, are explored to facilitate a more holistic understanding of the dynamic environment. Furthermore, the integration of HD Maps in autonomous driving systems is emphasized to provide an additional layer of precision for navigation and real-time decision-making.
Keywords encompass sensor fusion, sensor processing, multi-sensor integration, environmental perception, autonomous driving, Advanced Sensor Technologies, Physical Infrastructure, Digital Infrastructure, AI, Deep Learning, Cooperative Fusion, and HD Maps.
In summary, this Special Issue endeavours to present a state-of-the-art exploration of Advanced Sensing Technologies, integrating discussions on Physical and Digital Infrastructure, AI, Deep Learning, Cooperative Fusion, and HD Maps. Contributions in these areas are encouraged to propel the field towards a future of intelligent, reliable, and context-aware autonomous driving.
Dr. Dominique Gruyer
Dr. Sio Song Ieng
Prof. Dr. Homayoun Najjaran
Prof. Dr. Sebastien Glaser
Guest Editors
Manuscript Submission Information
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Keywords
- advanced sensing technologies
- AI and deep learning
- AI-based sensor fusion
- cooperative and multiple sensor fusion
- new sensor technologies
- sensor and data processing
- embedded/distributed/collaborative architectures
- information processing
- cooperative perception
- automated driving
