Context fusion for driveability analysis

Abstract

Driveability analysis is a quite complex problem that for its solution depends on several factors. One of these factors concerns the type of vehicle for which a drive-way should be determined. Besides this, the terrain structure, the type of vegetation but also the ground type and its conditions play important roles. Driveability analysis will consequently include analysis of primarily geographical information and the outcome of this analysis can be used to support decision making in command and control systems. However, quite often the required geographical information is represented in a resolution that is either too low and/or is represented with a high degree of uncertainty that cannot be neglected. In this work, an approach to driveability analysis is presented in which geographical information is regarded as context information that eventually is fused to generate paths, that may be drivable for certain types of vehicles. This information is fused by means of a knowledge-based technique that determines the driveability from a set of qualitative driveability impact factors.

Particle Swarm Optimization and Dempster Shafer Approach to Achieve Internet of Things Context Fusion Using Quality of Context

“…The authors of [12] used the terms driveability or trafficability to come up with a method to analyze how an object could assess its environment. The proposed method analyzed geographical information as context for decision making in the command and control systems.…”