Artificial Intelligence/Machine Learning, ASA(ALT), Direct to Phase II
Artificial Intelligence for Aided Driving of Ground Combat Vehicles
We are no longer accepting white papers. SBIR submissions are now limited to those eligible through the xTech competition.
IMPORTANT: xTechIgnite will be used to identify small business concerns that meet the criteria for award. Winners selected from the xTechIgnite prize competition will be the only firms eligible to submit an SBIR proposal under any of the topics listed above. Proposals submitted to the topics listed above by nonwinners of the xTechIgnite competition will not be evaluated. See the full xTechIgnite competition RFI here: https://www.xtech.army.mil/competition/xtechignite/.
The white paper submission deadline for xTechIgnite is March 12, 2025. White papers must be submitted by following instructions provided at the xTechIgnite link above. NOTE: white papers are NOT submitted to DSIP. Small business concerns that do not submit a concept white paper to the xTechIgnite competition before the March 12, 2025 deadline will be ineligible to compete or submit a full SBIR proposal to DSIP.
Objective
Develop and integrate Artificial Intelligence (AI) technology that can augment current and future sensor hardware enhancing the ability of the driver to maneuver and operate large, heavy, ground combat vehicles. These vehicles typically weigh 40 to 80 tons and have limited to zero direct line of sight making it difficult for the driver to safely maneuver the vehicle.
Description
Develop and integrate Artificial Intelligence and Machine Learning technology that can augment current and future sensor hardware enhancing the ability of the driver to maneuver and operate large, heavy, ground combat vehicles.
This can be done by improving driver vision and situation awareness with AI/ML based crew aids such as using AI for computer vision and object detection and/or using a large language model AI. This capability will support an AI Driver Assist System similar to Apple’s Siri or Amazon’s Alexa that is tailored toward driver combat scenarios. This capability will provide the foundation towards partial autonomy and could also lead to a fully autonomous system in the future.
IMPORTANT: A prize competition, xTechIgnite, will be used to identify small business concerns that meet the criteria for award for this topic. Winners selected from the xTechIgnite prize competition will be the only firms eligible to submit a SBIR proposal under this topic. All other proposals will not be evaluated. See the full xTechIgnite competition details here: https://www.xtech.army.mil/competition/xtechignite/.
Phase I
This topic is accepting Direct to Phase II proposals for a cost up to $2,000,000 for a 24-month period of performance. Proposers interested in submitting a DP2 proposal must provide documentation to substantiate that the scientific and technical merit and feasibility equivalent to a Phase I project has been met. Documentation can include data, reports, specific measurements, success criteria of a prototype, etc.
Phase II
To develop, integrate, and demonstrate AI/ML technologies to enhance the driver’s ability to maneuver and operate the vehicle.
Phase III
Analysis shows commercial potential across multiple fields including the automotive industry, security and law enforcement, and logistics and fleet management. Specific examples of potential dual-use could include: Advanced Driver Assistance systems that employ AI/ML and sensor fusion technologies for obstacle detection, lane keeping, and semi-autonomous driving features, AI-enabled route optimization, surveillance of vehicle surroundings, and real-time threat assessment.
Submission Information
For more information, and to submit your full proposal package, visit the DSIP Portal.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil
References:
- https://aimagazine.com/articles/the-power-of-advanced-ai-assisted-driving-systems
- https://www.contents.com/magazine/artificial-intelligence/revolutionising-the-road-how-ai-is-transforming-driving-experiences/
- KEYWORDS: Driver Assist System; Partial Autonomy; Detection Algorithms; Hazard Detection
We are no longer accepting white papers. SBIR submissions are now limited to those eligible through the xTech competition.
IMPORTANT: xTechIgnite will be used to identify small business concerns that meet the criteria for award. Winners selected from the xTechIgnite prize competition will be the only firms eligible to submit an SBIR proposal under any of the topics listed above. Proposals submitted to the topics listed above by nonwinners of the xTechIgnite competition will not be evaluated. See the full xTechIgnite competition RFI here: https://www.xtech.army.mil/competition/xtechignite/.
The white paper submission deadline for xTechIgnite is March 12, 2025. White papers must be submitted by following instructions provided at the xTechIgnite link above. NOTE: white papers are NOT submitted to DSIP. Small business concerns that do not submit a concept white paper to the xTechIgnite competition before the March 12, 2025 deadline will be ineligible to compete or submit a full SBIR proposal to DSIP.
Objective
Develop and integrate Artificial Intelligence (AI) technology that can augment current and future sensor hardware enhancing the ability of the driver to maneuver and operate large, heavy, ground combat vehicles. These vehicles typically weigh 40 to 80 tons and have limited to zero direct line of sight making it difficult for the driver to safely maneuver the vehicle.
Description
Develop and integrate Artificial Intelligence and Machine Learning technology that can augment current and future sensor hardware enhancing the ability of the driver to maneuver and operate large, heavy, ground combat vehicles.
This can be done by improving driver vision and situation awareness with AI/ML based crew aids such as using AI for computer vision and object detection and/or using a large language model AI. This capability will support an AI Driver Assist System similar to Apple’s Siri or Amazon’s Alexa that is tailored toward driver combat scenarios. This capability will provide the foundation towards partial autonomy and could also lead to a fully autonomous system in the future.
IMPORTANT: A prize competition, xTechIgnite, will be used to identify small business concerns that meet the criteria for award for this topic. Winners selected from the xTechIgnite prize competition will be the only firms eligible to submit a SBIR proposal under this topic. All other proposals will not be evaluated. See the full xTechIgnite competition details here: https://www.xtech.army.mil/competition/xtechignite/.
Phase I
This topic is accepting Direct to Phase II proposals for a cost up to $2,000,000 for a 24-month period of performance. Proposers interested in submitting a DP2 proposal must provide documentation to substantiate that the scientific and technical merit and feasibility equivalent to a Phase I project has been met. Documentation can include data, reports, specific measurements, success criteria of a prototype, etc.
Phase II
To develop, integrate, and demonstrate AI/ML technologies to enhance the driver’s ability to maneuver and operate the vehicle.
Phase III
Analysis shows commercial potential across multiple fields including the automotive industry, security and law enforcement, and logistics and fleet management. Specific examples of potential dual-use could include: Advanced Driver Assistance systems that employ AI/ML and sensor fusion technologies for obstacle detection, lane keeping, and semi-autonomous driving features, AI-enabled route optimization, surveillance of vehicle surroundings, and real-time threat assessment.
Submission Information
For more information, and to submit your full proposal package, visit the DSIP Portal.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil
References:
- https://aimagazine.com/articles/the-power-of-advanced-ai-assisted-driving-systems
- https://www.contents.com/magazine/artificial-intelligence/revolutionising-the-road-how-ai-is-transforming-driving-experiences/
- KEYWORDS: Driver Assist System; Partial Autonomy; Detection Algorithms; Hazard Detection