NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
|
Initial Amendment Date: | August 15, 2009 |
Latest Amendment Date: | August 10, 2011 |
Award Number: | 0931686 |
Award Instrument: | Standard Grant |
Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
Start Date: | September 1, 2009 |
End Date: | August 31, 2013 (Estimated) |
Total Intended Award Amount: | $1,473,121.00 |
Total Awarded Amount to Date: | $1,473,121.00 |
Funds Obligated to Date: |
|
ARRA Amount: | $1,473,121.00 |
History of Investigator: |
|
Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
Sponsor Congressional District: |
|
Primary Place of Performance: |
341 PINE TREE RD ITHACA NY US 14850-2820 |
Primary Place of Performance Congressional District: |
|
Unique Entity Identifier (UEI): |
|
Parent UEI: |
|
NSF Program(s): | CPS-Cyber-Physical Systems |
Primary Program Source: |
|
Program Reference Code(s): |
|
Program Element Code(s): |
|
Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.082 |
ABSTRACT
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The objective of this research is to develop truly intelligent, automated driving through a new paradigm that tightly integrates probabilistic perception and deterministic planning in a formal, verifiable framework. The interdisciplinary approach utilizes three interlinked tasks. Representations develops new techniques for constructing and maintaining representations of a dynamic environment to facilitate higher-level planning. Anticipation and Motion Planning develops methods to anticipate changes in the environment and use them as part of the planning process. Verifiable Task Planning develops theory and techniques for providing probabilistic guarantees for high-level behaviors. Ingrained in the approach is the synergy between theory and experiment using an in house, fully equipped vehicle.
The recent Urban Challenge showed the current brittleness of autonomous driving, where small perception mistakes would propagate into planners, causing near misses and small accidents; Fundamentally, there is a mismatch between probabilistic perception and deterministic planning, leading to "reactive" rather than "intelligent" behaviors. The proposed research directly addresses this by developing a single, unified theory of perception and planning for intelligent cyber-physical systems.
Near term, this research could be used to develop advanced safety systems in cars. The elderly and physically impaired would benefit from inexpensive, advanced automation in cars. Far term, the advanced intelligence could lead to automated vehicles for applications such as cooperative search and rescue. The research program will educate students through interdisciplinary courses in computer science and mechanical engineering, and experiential learning projects. Results will be disseminated to the community including under-represented colleges and universities.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
Please report errors in award information by writing to: awardsearch@nsf.gov.