Advance Technologies of Navigation for Intelligent Vehicles

Dear Colleagues, 

Intelligent vehicles have become an area of intense research within the robotics and control community, wherein unmanned ground/aerial/underwater vehicles have covered a wide range of applications from the civil domain to the military domain, such as logistics, mine detection, building and environment monitoring, intruder detection and attacking, etc. In recent years, swarms or networks of such autonomous robots are emerging as a disruptive technology to enable highly reconfigurable, on-demand, distributed intelligent vehicles with a high impact in many areas of science, technology, and society. In any application, networked and cooperative vehicles are expected to be more capable than a single large vehicle, offering significantly enhanced flexibility (adaptability, scalability, and maintainability) and robustness (reliability, survivability, and fault tolerance).

Autonomous navigation is one of the supporting pillars for realizing the abovementioned intelligent applications of intelligent vehicles. To date, efforts in this study have been continuously increasing, but many problems remain to be explored, discovered, and solved. The primary purpose of this Special Issue is to explore and display the latest achievements of theory and practice related to the advance technologies of navigation for intelligent vehicles. The areas of interest include but are not limited to:

• Overview of intelligent vehicles;
• Signal processing methods and sensor modules for intelligent vehicles;
• Autonomous navigation in GPS-denied environments;
• Multi-sensor target localization and tracking; 
• Autonomous decision making for game and cooperation;
• Cooperative path planning and re-planning for homogeneous/nonhomogeneous vehicles;
• Synchronization for large-scale networks of intelligent vehicles;
• Learning-based and bio-inspired control for complex tasks;
• Distributed optimization and parallel decision making;
• Fault tolerance and robustness in disturbed and uncertain environments;
• Artificial intelligence in swarm cooperative control;
• Deep learning for resource-constrained embedded vision sensor applications;
• Event-driven control strategies for silent and camouflaged intelligent vehicles.

Dr. Jinwen Hu
Prof. Wei Meng
Dr. Shenghai Yuan
Guest Editors

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