NSF Org:	CMMI Division of Civil, Mechanical, and Manufacturing Innovation
Recipient:	REGENTS OF THE UNIVERSITY OF MINNESOTA
Initial Amendment Date:	September 20, 2001
Latest Amendment Date:	May 28, 2003
Award Number:	0127893
Award Instrument:	Standard Grant
Program Manager:	Dennis Wenger CMMI  Division of Civil, Mechanical, and Manufacturing Innovation ENG  Directorate for Engineering
Start Date:	October 1, 2001
End Date:	September 30, 2004 (Estimated)
Total Intended Award Amount:	$99,789.00
Total Awarded Amount to Date:	$131,789.00
Funds Obligated to Date:	FY 2001 = $99,789.00 FY 2002 = $32,000.00
History of Investigator:	Nikolaos Papanikolopoulos (Principal Investigator) npapas@cs.umn.edu Ravi Janardan (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Minnesota-Twin Cities 2221 UNIVERSITY AVE SE STE 100 MINNEAPOLIS MN  US  55414-3074 (612)624-5599
Sponsor Congressional District:	05
Primary Place of Performance:	University of Minnesota-Twin Cities 2221 UNIVERSITY AVE SE STE 100 MINNEAPOLIS MN  US  55414-3074
Primary Place of Performance Congressional District:	05
Unique Entity Identifier (UEI):	KABJZBBJ4B54
Parent UEI:
NSF Program(s):	HDBE-Humans, Disasters, and th
Primary Program Source:	01000102DB NSF RESEARCH & RELATED ACTIVIT app-0101  app-0102
Program Reference Code(s):	1038, 1039, 1057, 9231, 9251, CVIS
Program Element Code(s):	163800
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.041

ABSTRACT

Papanikolopoulos
Institution: University of Minnesota - Twin Cities.
NSF/USDOT: Real-time collision Warning at Traffic Intersections

Collisions between vehicles at urban and rural intersections account for nearly a third of all reported crashes in the United States. This research entails developing some of the components of a real-time system which uses cameras to continuously gather traffic data at intersections (e.g., vehicle speeds, positions, trajectories, accelerations/decelerations, vehicle sizes, signal status, etc.), applies efficient algorithmic techniques to detect potential collisions and near-misses, and then issues suitable countermeasures. At this stage, the goal is to establish the feasibility of this approach using both computer simulations and field tests at actual intersections (urban intersections in the Twin Cities---Minneapolis and St. Paul, MN). The proposed work has as main emphasis the design of efficient and robust computer vision and collision-detection algorithms that can address the intersection collision warning problem.

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