AI/ML-Enabled Voice-Commanded Autonomous Maneuver for Ground Combat Vehicles
Operating, maneuvering, engaging, defending, and commanding the Combat Vehicles on the modern battlefield requires a significant level of crew communication, systems management, and situational awareness. As a result, it imperative to reduce the cognitive burden on vehicle crews and enable rapid decision-making, especially during combat. To support this, the US Army is seeking innovative solutions that will enable autonomous maneuvers in response to crew commands issued with natural language.
Automated Course of Action Generation
Several approaches to CoA generation for military applications currently exist. These methods leverage large language models (LLM) applied to Doctrine or deep learning/hierarchical modeling applied to wargaming. Each existing LLM approach is inadequate for CoA generation at BN due to lack of proper consideration of Tactics, Techniques and Procedures (TTPs) and Standard Operating Procedures (SOPs) for both friendly and enemy forces. The existing learned/hierarchical approaches that depend on wargaming are also inadequate due to a lack of fully automated wargaming capability at the BN echelon and/or due to CoA outcomes that are not aligned with best practices (TTPs and SOPs). Proposals submitted under this topic will leverage state-of-the-art Artificial Intelligence (AI) approaches to create a CoA recommendation module narrowly tailored to the BN echelon. This capability will expand to encompass higher echelons in later phases.
Beacon for Assured Landing Location
Develop, demonstrate, and deliver solutions that increase the precision of automated drone landings. The system should be capable of landing a drone onto either moving or non-moving unmanned platforms, or the ground and should be able to function in highly congested or adverse environments (such as dense foliage, close buildings, or multiple aerial operations) with little or no input from a human operator.
Cold Regions Research and Engineering Laboratory CSO
The U.S. Army Engineer Research and Development Center's Cold Regions Research and Engineering Laboratory is looking to obtain innovative solutions or potential new capabilities in the following categories: Building Cold Region Domain Awareness, Enhancing Mobility and Maneuver in Cold Region Environments, Integrated Ice Operations, Advanced Materials Development and Applications in Extreme Cold Environments, and Resilient Cold Region Energy Systems.
Large Language Models for System Security Engineering Analysis
The primary problem this topic addresses is the growing complexity of traditional and AI/ML-based cybersecurity threats to DoD weapon systems, increasingly connected and automated weapon systems architectures, and the need for advanced tools to enhance development-phase cyber resiliency efforts and exploit modeling. This topic is essential because it will improve US DoD aviation and missile platforms’ resilience, survivability, and lethality through the enhanced identification and exploitation modeling of system vulnerabilities during system development. This will achieve significant impact in the most cost-effective phase of the system lifecycle. The proposed approach leverages state-of-the-art LLMs to develop a specialized tool for system security engineering risk reduction efforts (blue team and AAMCATs) and provides critical exploitation inputs to DoD-approved red team operations. This topic innovates on existing technology by fine-tuning a foundational LLM built to analyze complex weapon system codebases, identify vulnerabilities, and generate proof-of-concept exploits. This approach combines natural language processing and code analysis capabilities, creating a powerful tool that surpasses current manual and automated analysis methods. The proposed LLM will be trained on the large amounts of relevant data available to DEVCOM AvMC including code, network traffic, static and dynamic analysis results, external software code bases and malicious software examples, and threat intelligence from partner US Government organizations. The LLM will be integrated with AvMC software and hardware integration labs to provide further tuning and address the issue of run-time analysis when software code is unavailable to the cyber resiliency team.
Secure Wireless Data Transfer Solutions for Predictive Logistics
A technology solution is needed for wireless data transmission from Digital Source Collectors (DSCs) on Tactical Wheeled Vehicles back to a centralized collection point in the motorpool, specifically the Army Vantage dashboard. Transmission of Predictive Logistics data wirelessly during field operations with acceptable speed and network availability will significantly enhance the efficiency and effectiveness of vehicle operations. This data transport capability will enable Unit Commanders and Maintainers to monitor the health status, fuel and ammunition of their platforms in near real-time, facilitating tactical adjustments and prompt responses to critical maintenance issues. Solutions should focus on movement of High Priority critical fault and fuel/ammo status notifications.
