Predicting a vehicle or pedestrian´s next move with neural networks

Author

Tascillo, Anya L. ; DiMeo, David M. ; Macneille, Perry R. ; Miller, Ronald H.

Author_Institution

Distributed Intelligence Lab., Ford Motor Co., Dearborn, MI, USA

Volume

3

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2310

Lastpage

2314

Abstract

For a given digitized view of a driving scenario, motion clusters are formed, indicating possible moving object threats to the driver. Recurrent neural networks minimize hopping between clusters and predict a cluster´s next location. Frequency analysis then categorizes as significant motion, and then as either head-on/away or transverse motion

Keywords

driver information systems; forecasting theory; image classification; minimisation; motion estimation; pattern clustering; recurrent neural nets; cluster hopping minimization; cluster location prediction; driving; frequency analysis; head-away motion; head-on motion; motion categorization; motion classification; motion clusters; moving object threats; pedestrian move prediction; recurrent neural networks; transverse motion; vehicle move prediction; Detection algorithms; Frequency; Laboratories; Motion analysis; Neural networks; Recurrent neural networks; Road accidents; Telecommunication traffic; Tracking; Vehicle driving;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007502

Filename

1007502