Paper Info
Title
Towards autonomous on-road driving via multiresolutional and hierarchical moving-object prediction
Abstract
In this paper, we present the PRIDE framework (Prediction In Dynamic Environments), which is a hierarchical multi-resolutional approach for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (Real-time Control System) and provides information to planners at the level of granularity that is appropriate for their planning horizon. The lower levels of the framework utilize estimation theoretic short-term predictions based upon an extended Kalman filter that provide predictions and associated uncertainty measures. The upper levels utilize a probabilistic prediction approach based upon situation recognition with an underlying cost model that provide predictions that incorporate environmental information and constraints. These predictions are made at lower-frequencies and at a level of resolution more in line with the needs of higher-level planners. PRIDE is run in the systems' world model independently of the planner and the control system. The results of the prediction are made available to a planner to allow it to make accurate plans in dynamic environments. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort.
Year
DOI
Venue
2002
10.1117/12.580172
PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE)
Keywords
Field
DocType
autonomous vehicle,on-road driving,PRIDE,moving object prediction,hierarchical
Extended Kalman filter,Time horizon,Knowledge engineering,Artificial intelligence,Autonomous system (mathematics),Probabilistic logic,Control system,Engineering,Hierarchy,Machine learning,Robotics
Conference
Volume
ISSN
Citations 
5609
0277-786X
2
PageRank 
References 
Authors
0.71
0
3
Name
Order
Citations
PageRank
Jerome Ajot1516.75
Craig Schlenoff221934.06
Raj Madhavan323727.91