Name
Title | ||
|---|---|---|
Learning the relevant percepts of modular hierarchical Bayesian driver models using a Bayesian information criterion |
Abstract | ||
|---|---|---|
Modeling drivers' behavior is essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. Based on psychological studies various perceptual measures (angles, distances, time-to-x-measures) have been proposed for such models. These proposals are partly contradictory and depend on special experimental settings. A general computational vision theory of driving behavior is still pending. We propose the selection of drivers' percepts according to their statistical relevance. In this paper we present a new machine-learning method based on a variant of the Bayesian Information Criterion (BIC) using a parent-child-monitor to obtain minimal sets of percepts which are relevant for drivers' actions in arbitrary scenarios or maneuvers. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21799-9_52 | HCI (17) |
Keywords | Field | DocType |
psychological study,new machine-learning method,minimal set,bayesian information criterion,arbitrary scenario,general computational vision theory,various perceptual measure,relevant percept,modular hierarchical bayesian driver,error-compensating assistance system,various author,production-system model,machine learning | Rapid prototyping,Variable-order Bayesian network,Bayesian information criterion,Computational vision,Psychology,Bayesian programming,Artificial intelligence,Modular design,Perception,Machine learning,Bayesian probability | Conference |
Volume | ISSN | Citations |
6777 | 0302-9743 | 3 |
PageRank | References | Authors |
0.56 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Eilers | 1 | 20 | 3.70 |
| Claus Möbus | 2 | 58 | 15.18 |