SBIR/STTR Research Award
Monte Carlo Sampling Based Collision Detection Algorithm Development And False Positive And False Negative Rate Analysis: A Bayesian Approach
Award amount
$98,630Program and phase
STTR · Phase IAward year
2008Award details
- Awarding agency
- Department of Defense
- Branch
- Defense Advanced Research Projects Agency
- Recipient company
- Princeton Vision LLC
- Proposal award date
- 2008-08-22
- Contract end date
- 2009-07-31
- UEI
- Not reported
- Solicitation number
- 2008.A
- Topic code
- ST081-005
Research abstract
The ability to detect and avoid collision is vital for intelligent vehicles navigating in dynamic environments. There is an acute need of high quality collision detection systems to improve the performance of the US military UGV/UAV, and to develop active safety devices for the conventional automotive market. In the proposed Phase I work, a Monte Carlo sampling based collision detection algorithm will be developed and evaluated rigorously. A sampling based framework will also be built to facilitate the analysis of FAR/FNR with respect to the realistic noise distributions.
Technology keywords
Not reported
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- Vision Based Sense and Avoid Solution for small UAS
- Passive Collision Detection for UAV Sense and Avoid Systems
- Monte Carlo Sampling Based Collision Detection Algorithm Development And False Positive And False Negative Rate Analysis: A Bayesian Approach
- Passive Collision Detection for UAV Sense and Avoid Systems