The effect of process models on short-term prediction of moving objects for unmanned ground vehicles

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn , Issue , 2004, Pages 471-476

  Madhavan, R ^a   Schienoff, C ^a  

^a NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

Author keywords

Estimation theory; Moving object prediction; Unmanned ground vehicles

Indexed keywords

ALGORITHMS; COMPUTER ARCHITECTURE; COMPUTER SIMULATION; HIERARCHICAL SYSTEMS; KALMAN FILTERING; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILISTIC LOGICS; REAL TIME SYSTEMS; UNMANNED VEHICLES;

ESTIMATION THEORY; EXTENDED KALMAN FILTERS (EKF); MOVING OBJECT PREDICTION; UNMANNED GROUND VEHICLES;

GROUND VEHICLES;

EID: 13344285991     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. J. Albus et al. 4D/RCS Version 2.0: A Reference Model Architecture for Unmanned Vehicle Systems. Technical Report NISTIR 6910, National Institute of Standards and Technology, Gaitherburg, MD 20899, U.S.A., 2002.
2. R. Madhavan and H. Durrant-Whyte. Terrain Aided Localization of Autonomous Ground Vehicles. Journal of Automation in Construction (Invited), 13(1):69-86, January 2004.
3. P. Maybeck. Stochastic Models, Estimation, and Control Vol. 1. Academic Press, New York, June 1979.
4. C. Schlenoff, R. Madhavan, and T. Barbera. A Hierarchical, Multi-Resolutional Moving Object Prediction Approach for Autonomous On-Road Driving. In Proceedings of the IEEE Intl. Conf. on Robotics and Automation, pages 1956-1961, April 2004.