Autonomous Navigation Systems for Robust Driving Under Adverse Conditions
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 133
Special Issue Editors
Interests: artificial intelligence; autonomous vehicle; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: 6G mobile communication network; non-terrestrial communication networks; wireless communication system; aerial communication network; disaster-resilient communication network; radio resource management; LEO multibeam satellite communication networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Autonomous navigation systems are rapidly advancing, yet their robustness under adverse conditions remains a critical challenge. This Special Issue aims to explore cutting-edge methodologies, algorithms, and technologies that enhance the resilience and adaptability of autonomous driving in challenging environments. We invite researchers and practitioners to contribute original research and review articles addressing solutions to improve autonomous navigation under adverse weather conditions (rain, snow, fog, and extreme temperatures), low-visibility scenarios, dynamic and unstructured environments, and unexpected road hazards.
This Special Issue seeks to advance the field by addressing key challenges such as sensor fusion, real-time perception under adverse conditions, uncertainty-aware decision-making, and reinforcement learning-based navigation strategies. The goal is to foster innovation that ensures safety, reliability, and efficiency in autonomous driving under real-world challenges.
Dr. Chao-Yang Lee
Dr. Ang-Hsun Tsai
Guest Editors
Manuscript Submission Information
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Keywords
- robust perception systems
- adversarial navigation strategies
- sensor- and data-driven approaches
- uncertainty-aware control and planning
- scene understanding for decision making
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