Title :
A hierarchical, multi-resolutional moving object prediction approach for autonomous on-road driving
Author :
Schlenoff, C. ; Madhavan, R. ; Barbera, T.
Author_Institution :
Intelligent Syst. Div., Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
fDate :
April 26-May 1, 2004
Abstract :
In this paper, we present a hierarchical multi-resolutional approach for moving object prediction via estimation-theoretic and situation-based probabilistic techniques. The results of the prediction are made available to a planner to allow it to make accurate plans in the presence of a dynamic environment. 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. Experimental results are shown in two simulation environments.
Keywords :
Kalman filters; estimation theory; mobile robots; object detection; prediction theory; probability; DARPA; Defense Advanced Research Projects Agency; autonomous on road driving; estimation based probabilistic technique; hierarchical moving object prediction; mobile autonomous robotic systems; multiresolutional moving object prediction; on road driving control hierarchy; situation based probabilistic technique; theoretic based probabilistic technique; Computer architecture; Control systems; Databases; Hidden Markov models; Intelligent systems; Mobile robots; NIST; Navigation; Predictive models; Remotely operated vehicles;
Conference_Titel :
Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on
Print_ISBN :
0-7803-8232-3
DOI :
10.1109/ROBOT.2004.1308110
