MOSA C2 for Autonomous Vehicles to Counter UAS, SWARM, and Ground-Based Robotic Threats

2025-01-0435

09/16/2025

Authors Abstract
Content
A Modular Open Systems Approach (MOSA) for command and control (C2) of autonomous vehicles equipped with sensor and defeat mechanisms enhances force protection against unmanned aerial systems (UAS), swarm, and ground-based robotic threats with current technology while providing an adaptable framework able to accommodate technological advances. This approach emphasizes modularity, which allows for independent upgrades and maintenance; interoperability, which ensures seamless integration with other systems; and scalability, which enables the system to grow and adapt to increasing threats and new technologies – all of which are essential for managing complex, dynamic, and evolving operational threats from UAS, swarm, and ground-based robots. The proposed systems approach is designed around component-based modules with standardized interfaces, ensuring ease of integration, maintenance, and upgrades. The integration of diverse sensors through plug-and-play capabilities and multi-sensor fusion enhances the detection, tracking, and identification of aerial and ground threats. Autonomous decision-making is empowered by artificial intelligence (AI) and machine learning (ML) algorithms and supported by edge and cloud computing for efficient data processing while retaining human-in-the-loop confirmation prior to threat defeat activation. Lifecycle management strategies with a continuous integration/continuous development (CI/CD) pipeline enable rapid updates and a scalable architecture to accommodate evolving threats and new technologies.
Meta TagsDetails
DOI
https://doi.org/10.4271/2025-01-0435
Pages
15
Citation
Davidson, J., Drewes, P., Graham, R., Haider, E. et al., "MOSA C2 for Autonomous Vehicles to Counter UAS, SWARM, and Ground-Based Robotic Threats," SAE Technical Paper 2025-01-0435, 2025, https://doi.org/10.4271/2025-01-0435.
Additional Details
Publisher
Published
Sep 16
Product Code
2025-01-0435
Content Type
Technical Paper
Language
English