Synthetic vision systems - from sandbox to reality

Abstract

Synthetic vision (SV) systems are the subject of intensive ongoing research and development. Their promise lies in their ability to fuse three dimensional data into intuitive displays that can provide life-saving awareness to flight crews. There exists a large gap between the desktop prototyping environment and the operational embedded systems environment that has proven daunting to bridge. This gap is in three primary areas: hardware, data, and software. Advanced embedded graphics hardware subsystems are maturing, based on industry standards like OpenGL and desktop graphics capabilities from companies like NVidia and ATI migrating to embedded environments. These systems bring with them the necessary throughput and performance to close the hardware performance gap. The data gap is closing in many ways, with commercial data providers such as Honeywell and Jeppesen offering certified data sets. But the software gap is most challenging, for it presents not just an engineering problem but a process, tools, and certification challenge as well. Simply put, most SV research efforts focus on the capability and the symbology, and pay too little attention to the end goal of operational embedded and DO178B certifiable software. Desktop software, even attractive SV display software, is easy to write, but embedded certified software is not. Just pointing to a prototype as a 'requirement' and expecting the embedded guys to 'go do it' will not make it happen. Simply focusing on certifying graphics drivers is not enough. A culture change in how the research/prototyping industry does business is required. This paper challenges the SV R&D community to pay attention to how SV software is developed and to do business in a way that will lead to their innovative concepts becoming available as certifiable embedded systems.