Title :
Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements
Author :
Weidl, Galia ; Madsen, Anders L. ; Kasper, Dietmar ; Breuel, Gabi
Author_Institution :
R&D, Dept. of Driving Autom., Daimler AG, Böblingen, Germany
Abstract :
An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended.
Keywords :
Linux; automotive engineering; belief networks; driver information systems; object-oriented programming; Bayesian networks; ECU-processor; Linux PC; OOBN; alternative modelling techniques; automotive application; automotive requirements; driving maneuver recognition; highway traffic; i7 processor; object oriented Bayesian network; prediction horizon; prototype car; recognition performance; Computational modeling; Hidden Markov models; Memory management; Object oriented modeling; Random access memory; Uncertainty; Vehicles;
Conference_Titel :
Intelligent Control (ISIC), 2014 IEEE International Symposium on
Conference_Location :
Juan Les Pins
DOI :
10.1109/ISIC.2014.6967630
